Prediction and Analysis of Process Control using Artificial Neural Network

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Prediction and Analysis of Process Control using Artificial Neural Network

K.V. L. N. Murthy1

Part-time Ph.D. Scholar in Department of Mechanical Engineering,

College of Engineering(A), Andhra University, Visakhapatnam-3.

VVS Kesava Rao2

Part-time Ph.D. Scholar in Department of Mechanical Engineering,

College of Engineering(A), Andhra University, Visakhapatnam-3.

Abstract:- Control charts that are used for monitoring the process and detecting the out-of-control signals are important tools for statistical process control. It is simple to estimate source(s) for out-of-control signals in the univariate process, whereas it is difficult to identify the source(s) in the multivariate processes. The reason is that these kinds of processes require monitoring and controlling of more than one quality characteristics simultaneously. Control charts are constructed using T2 control chart and out of control signals are diagnosed through principal component analysis. In this section, artificial neural network model is proposed for prediction of source(s) for out-of-control signals. This model was implemented in an integrated steel plant for analysis of out of control signals, in production of hot metal process in the blast furnace.

Key words: T2 control charts, univariate process

1.0 INTRODUCTION

Quality is one of the most important components for success in production. Some techniques have been developed for providing the desired quality. One of the most important techniques is quality control charts. These charts provide monitoring process and detect the source(s) of out-of-control signals. In multivariate processes, multivariate control charts are used for determining the out-of-control signals. The sources of out-of-control signals may also depend on a variable(s) and/or the relationship between variables. Control charts that detect the out-of- control signals are generally created with T2 statistics. However, with this chart, it is accepted that there is interaction between variables. In this study, a multilayer neural network model has been established for eliminating disadvantages caused by separate evaluation of the variables. The proposed model is based on T2 control charts, because there was relationship between variables.

If multiple variables, affecting the process, are monitored simultaneously, then multivariate quality control diagrams are used. Hotelling T2 control diagram, developed by Hotelling based on arithmetic average. Although it is possible to evaluate the variables together and detect out- of-control signals in the process, it is not possible to determine the source(s) for these signals using this chart. Hence in previous chapter, monitoring of multiple

variables is made through T2 a control chart and detection out-of-control signal in the process is done through principal component analysis with a case study of smelting process in the blast furnace of an integrated steel plant.

In this study, Multi Layer Perceptron (MLP) of Artificial Neural Network model has been proposed for prediction and analysis of out of control Signals caused by multiple variables. Six quality variables have been considered. Initially, data on the six variables is collected during one month period from blast furnace process randomly. These six variables are considered as inputs to the neural network. T2 values of each observation and class of in control/out of control is considered as outputs to the neural network.

2.0 LITERATURE SURVEY

Gerardo Avendano and Gustavo Andrés Campos- Avendano (2017) [1] described the implementation of the control chart Hotellings T2 using real data obtained from the industry. It is concluded that the network has the ability to find out which variables have changed in the process when the Hotelling T2 chart indicates that a change has occurred.

Igor Greovnik et al. (2012) [2] developed and applied the framework based on artificial neural networks and an integrated optimization module to adjustment of process parameters in steel production. In the study, results of the model have been examined by experts from steel manufacturing industry, who confirmed that the trends exhibited in various parametric studies are consistent with expectations.

Edgar A. Ruelas-Santoyo et al. (2018) [3] described the application of wear pattern recognition system in carbon steel by employing multilayer perceptron in conjunction with digital image processing. In the study, the proposed system is compared with the human expert and an accuracy of 96.83% is obtained with less time and inspection cost

Owunna. I and A. E.Ikpe (2019) [4] adopted Artificial Neural Network modelling methodology for the TIG welding allowed extensive analysis of each input variables for predicting the best possible sets of output parameters, as well as optimizing the output data to obtain optimum values

Abhulimen and Achebo (2014) [5] investigated optimum properties in Tungsten inert gas weld of mild steel pipes using Artificial neural network prediction and optimization. The study revealed that ANN is successful to in predicting tensile and yield strength of TIG welded mild steel pipe joints .The results reported are in good agreement with other researchers

Tadeusz Wieczorek and Mirosaw Kordos (2010)

[6] presented neural network methodology for improving the efficiency of the steelmaking process by the measured data of the ladle heating furnace process (chemical compositions, temperature, etc.) to predict how much of particular additions should be added to the process. The authors felt that the results can probably be further improved if the selection of the activation functions in the hidden layer is performed during the training.

Boran and D.D. Diren (2017) [7] proposed model is to detect the source(s) for out-of-control signals without help of anexpert in the process, by using a multilayer neural network. This model was implemented with a case study, in furniture fasteners manufacturing. The authors concluded that with the proposed model, a large number of variables, affecting real processes can be analyzed together is possible.

Francisco Aparisi, José Sanz (2010) [8] designed neural networks to interpret the out-of-control signal of the MEWMA chart, and the percentage of correct classifications is studied for different cases. In this paper the percentage of correct classifications has been studied, obtaining similar results to the use of neural network for the Hotellings T2 control chart. In the study the authors developed software for Windows of very easy use, with the objective that the final user in industry can apply this method directly.

Shihua Luo Tianxin Chen and Ling Jian (2018)

  1. developed composite model combining Principal Component Analysis (PCA) and Least Squares Support Vector Machine (LSSVM) is established to predict the furnace temperature. Also, in this paper, a new algorithm formed up by combining PCA and LSSVM is used to predict [Si] in Blast Furnace System.

    Ishita Ghosh and Nilratan Chakraborty (2018)

  2. predicted solidification defects during the continuous casting of steel alloy by employing a multilayer perceptron (MLP) based neural network model. The inputs to this neural model are the various important processing parameters such as Aluminum percent, carbon drop percent in steel production, iron oxide percent in the sand mold, carbon percent, sulphur percent, fraction solid percent and critical temperature. In the study, it has been observed that carbon drop percent during steel production and aluminum percent in the steel alloy have significant contrbution in the formation of the shrinkage defect in steel alloy castings

    Wei Li et al. (2016) [11], estimated the endpoint of the basic oxygen furnace (BOF) steel making process the endpoint carbon content and the endpoint temperature of BOF. The authors proposed integrated back propagation (BP) neural network and an improved particle swarm optimization (PSO) algorithm to optimize the prediction model

    Seyed Taghi Akhavan Niaki and Babak Abbasi (2005) [12], proposed an artificial neural network based model to diagnose faults in out-of-control conditions and to help identify aberrant variables when Shewhart type multivariate control charts based on Hotellings T2 is used. The model was implemented with two numerical examples and one case study.

    Dipak Laha et al. (2015) [13] made a study on Monitoring and control of the output yield of steel in a steel making. In the study, the authors considered data mining tools such as random forests (RF), ANN, dynamic evolving neuro-fuzzy inference system (DENFIS) and support vector regression (SVR) as competitive learning tools to verify the suitability of applications of these approaches.

    McCulloch and Pitts, (1943) [14] introduced ANNs model. ANNs have received a great deal of attention as the theoretical foundations of building learning systems in the late 1950s and early 1960s.

    Medhat H.A. Awadalla and M. Abdellatif Sadek (2012) [15] developed a spiking neural network architecture and used for control charts pattern recognition (CCPR). It has a good capability in data smoothing and generalization. The overall mean percentages of correct recognition of SNN-based recognizers were 98.61%.

    Barghash, M.A. & Santarisi, N.S. (2004) [16] utilized artificial neural networks (ANN) for pattern recognition of the most common patterns which occur in quality control charts. The results of the study showed that the parameters such as minimum shift, shift range, population size and shift percentage, have significant effect on the performance of the ANN

    Nimbale.S.M. and V. B. Ghute (2016) [17] developed neural network scheme for monitoring process mean. The performance of X chart, Tukeys chart and ANN model is evaluated by Average Run Length (ARL) using the simulation under normal and non-normal distributions. From the study, the authors concluded that ANN is effective when compared traditional X chart, in respect of ARL.

    Stelios Psarakis (2011) discussed neural networks (NNs) [18] for the detection and determination of mean and/or variance shifts as well as in pattern recognition in the SPC charts. Furthermore the use of NNs in multivariate control charts is also addressed

    Mohammad Reza Maleki and Amirhossein Amiri (2015) [19], proposed neural network-based methodology to detects separate mean, variance shifts and simultaneous changes in mean vector and covariance matrix of multivariate-attribute processes. The results of comparison showed that the proposed neural network-based method outperforms the T2 control chart in most of the simultaneous shifts.

    3.0 METHODOLOGY OF THE PROPOSED MODEL

    The steps of proposed model are presented below:

    Step 1: Collect the data on quality variables

    A set of data containing 100 observations is collected for one month randomly. In each observation data on six quality characteristics (Hot metal Yield (Y), %S,

    %P, %Mn, %CO2 and PM) of hot metal is collected for multivariate process control and diagnosis of critical characteristics for monitoring of smelting process.

    Step 2: Develop the multivariate control chart

    T2 control chart is developed using Minitab 18 by considering the six quality characteristics of hot metal. The T2 value of each observation is also calculated.

    Step 3: Determine out-of-control signal for each observation

    Out of control signals of each observation is identified from T2 chart using upper control limit. T2 value of each observation beyond upper control limits is considered as out- of control. For each observation in- control/out-of-control class is designated.

    For example, (1,1,1,1,1,1) means; Yield: in-control, %S-in- control, %P in control, %Mn in-control, CO2 in control and PM-in-control. It is designated as one class. (0,0,1,1,1,1) means; Yield: out-of-control, %S out-of- control, %P in control, %Mn in-control, CO2 in control and PM-in-control. It is designated as another class.

    Similarly various classes are assigned for each observation based on the out-of control of set of variables. Step 4: Design multi layer perceptron neural network model

    Multi layer preceptor neural network model id developed by considering six quality variables as inputs to

    the input layer. Two output variables namely: T2 value and class of in- control/out-of-control are considered as outputs of the output layer.

    Step 5: Run the neural network model

    MLP of Neural networks is implemented to the case study using SPSS 18 by considering independent variable (Inputs), dependent variables (Outputs), neural network architecture, and training parameters.

    Step 6: Analyze the Results

    The output of multilayer perceptron neural network model from SPSS 18 is analyzed for model summary, classification results, predicted values etc.

      1. RESULTS AND DISCUSSION

        Initially, the proposed model was implemented to hot metal production process, in an integrated steel plant with six quality variables for multivariate quality control through T2 control chart using Minitab 18. Then, multilayer perceptron of neural network model is implemented using the results (T2 values and class of in-control/out-of-control) obtained through multivariate quality control with SPSS18. Results of multivariate quality control through T2 control chart and multilayer perceptron of neural network ore presented and discussed below.

      2. Multivariate quality control through T2 control chart

        Data on Quality Variables

        A set of data on Hot metal Yield (Y), %S, %P,

        %Mn, %CO2 and PM containing 100 observations is presented below.

        Table-1: Data on quality variables

        0.0877

        S.No

        Y

        S

        P

        Mn

        CO2

        PM

        S.No

        Y

        S

        P

        Mn

        CO2

        1

        1.4915

        0.0549

        0.0922

        0.0861

        26.7662

        17.6633

        41

        1.9994

        0.0458

        0.0849

        0.0808

        24.1524

        2

        1.8056

        0.0427

        0.0914

        0.0835

        24.2398

        26.9744

        42

        1.7981

        0.0523

        0.0863

        0.0842

        24.9965

        3

        1.9274

        0.0446

        0.091

        0.0858

        25.3491

        20.2104

        43

        1.5994

        0.0523

        0.0906

        0.0845

        26.0009

        4

        1.7945

        0.049

        0.0878

        0.0823

        24.9161

        25.836

        44

        2.0372

        0.0489

        0.0844

        0.0806

        26.6394

        5

        1.5561

        0.0527

        0.0913

        0.0853

        24.6253

        27.7104

        45

        1.7232

        0.0501

        0.0891

        0.0831

        25.2886

        1.7356

        0.0499

        0.0888

        0.083

        25.227

        20.904

        46

        1.3846

        0.0575

        0.0933

        27.2759

        7

        1.9134

        0.0471

        0.0858

        0.0813

        24.4236

        25.1896

        47

        1.8082

        0.0488

        0.0874

        0.0821

        24.776

        8

        1.5641

        0.0532

        0.0912

        0.085

        26.2277

        17.2387

        48

        1.9305

        0.0459

        0.0857

        0.0812

        25.403

        9

        1.6417

        0.0515

        0.0901

        0.084

        25.7379

        18.8549

        49

        1.9196

        0.0471

        0.0858

        0.0813

        24.4222

        10

        1.5807

        0.0502

        0.091

        0.0848

        23.6426

        16.252

        50

        1.8344

        0.0484

        0.0871

        0.082

        24.7095

        11

        2.1393

        0.0513

        0.0937

        0.0837

        25.5117

        27.3977

        51

        1.6538

        0.0513

        0.0898

        0.0838

        25.65

        12

        1.7857

        0.0492

        0.0879

        0.0824

        24.9719

        23.7013

        52

        1.8766

        0.0476

        0.0865

        0.0816

        24.5531

        13

        1.7932

        0.0491

        0.0878

        0.0823

        24.9337

        24.7902

        53

        1.6926

        0.0504

        0.0893

        0.0834

        25.4447

        14

        1.5561

        0.0534

        0.0913

        0.0853

        26.3251

        27.7104

        54

        1.8666

        0.048

        0.0867

        0.0817

        24.6036

        15

        1.7313

        0.05

        0.0889

        0.083

        25.2614

        23.2693

        55

        1.3688

        0.0484

        0.0864

        0.0834

        24.0373

        16

        1.8474

        0.0483

        0.0871

        0.0819

        24.6842

        15.7458

        56

        1.6506

        0.0513

        0.0899

        0.0839

        25.6952

        17

        1.4778

        0.0552

        0.0922

        0.0863

        26.8351

        11.4382

        57

        1.2698

        0.0591

        0.0947

        0.09

        18

        1.4047

        0.0564

        0.093

        0.0871

        27.2079

        15.3513

        58

        1.5715

        0.0473

        0.0911

        0.0849

        .&

        19

        2.0074

        0.0458

        0.0849

        0.0808

        24.1382

        24.794

        59

        1.7336

        0.0499

        0.0889

        0.083

        25.2506

        20

        1.5971

        0.0416

        0.0907

        0.0845

        25.3344

        21.6647

        60

        1.9673

        0.0463

        0.0853

        0.0809

        24.2398

        21

        1.6842

        0.0507

        0.0894

        0.0835

        25.4848

        25.2096

        61

        1.8094

        0.0487

        0.0873

        0.0821

        24.7718

        22

        1.8761

        0.0476

        0.0865

        0.0816

        24.5604

        24.2119

        62

        1.7526

        0.0491

        0.0865

        0.0815

        26.0009

        23

        1.674

        0.0509

        0.0896

        0.0836

        25.5245

        22.8109

        63

        1.469

        0.0555

        0.0924

        0.0864

        26.929

        24

        2.0813

        0.0493

        0.0835

        0.0804

        25.6507

        18.7359

        64

        1.6518

        0.0503

        0.0899

        0.0764

        27.1744

        25

        1.5199

        0.0545

        0.0918

        0.0858

        26.6228

        19.3311

        65

        1.7075

        0.0481

        0.0893

        0.0833

        23.9499

        26

        1.901

        0.0472

        0.086

        0.0814

        24.4664

        23.1196

        66

        1.6275

        0.0533

        0.0919

        0.0826

        24.5074

        27

        1.4618

        0.042

        0.0925

        0.0865

        26.8953

        22.0238

        67

        1.5919

        0.0492

        0.0866

        0.0861

        25.4447

        28

        1.5641

        0.0492

        0.0912

        0.085

        26.0749

        17.2387

        68

        1.731

        0.0452

        0.0863

        0.0808

        25.4612

        29

        1.5345

        0.054

        0.0916

        0.0855

        26.48

        22.945

        69

        1.5596

        0.0533

        0.0912

        0.0851

        26.2665

        30

        1.8422

        0.0483

        0.0871

        0.0819

        24.6982

        16.0969

        70

        1.5994

        0.0407

        0.0906

        0.0845

        25.7672

        31

        1.394

        0.0488

        0.0906

        0.085

        26.3249

        22.0178

        71

        1.6565

        0.0513

        0.0898

        0.0838

        25.6454

        32

        1.793

        0.0497

        0.0926

        0.0839

        24.6355

        15.1697

        72

        1.5006

        0.0548

        0.0922

        0.086

        26.7342

        33

        1.7539

        0.0497

        0.0885

        0.0829

        25.1422

        26.6364

        73

        1.8196

        0.0486

        0.0872

        0.082

        24.74

        34

        2.1428

        0.0427

        0.0827

        0.0802

        23.5048

        23.0655

        74

        1.6981

        0.0487

        0.097

        0.0848

        24.6982

        35

        1.3974

        0.0567

        0.0931

        0.0873

        27.2309

        17.1195

        75

        2.0716

        0.0516

        0.0838

        0.0805

        26.3935

        36

        1.4633

        0.046

        0.0844

        0.0832

        24.7152

        20.6069

        76

        1.6374

        0.0463

        0.0878

        0.0852

        23.8232

        37

        2.1892

        0.0418

        0.0818

        0.0801

        23.3724

        20.7293

        77

        1.792

        0.0491

        0.0878

        0.0823

        24.9407

        38

        1.5966

        0.0438

        0.0907

        0.0845

        25.1704

        15.8454

        78

        1.7105

        0.0501

        0.0892

        0.0832

        25.3491

        39

        1.5309

        0.0488

        0.0917

        0.0855

        25.178

        22.8396

        79

        1.8254

        0.0485

        0.0872

        0.082

        24.7333

        40

        1.686

        0.0495

        0.0853

        0.0819

        26.48

        18.5611

        80

        1.5194

        0.0545

        0.0919

        0.0858

        26.6287

        Table-1: Data on quality variables (Contd..)

        S.No

        Y

        S

        P

        Mn

        CO 2

        PM

        S.No

        Y

        S

        P

        Mn

        CO 2

        PM

        81

        1.745

        0.0499

        0.0888

        0.0829

        25.2014

        22.1462

        135

        1.7424

        0.0507

        0.0844

        0.0805

        23.4971

        24.4837

        82

        2.0191

        0.0489

        0.0847

        0.0807

        23.8246

        20.7982

        136

        2.1562

        0.0426

        0.0827

        0.0801

        23.4971

        26.0082

        83

        1.5928

        0.0466

        0.0908

        0.0845

        24.5739

        22.535

        137

        1.5678

        0.0521

        0.0911

        0.0849

        24.3921

        22.7366

        84

        1.5715

        0.053

        0.0911

        0.0849

        26.1779

        26.5707

        138

        1.7857

        0.0535

        0.0879

        0.0824

        25.3255

        23.7013

        85

        2.0187

        0.0457

        0.0847

        0.0807

        24.0872

        25.1585

        139

        1.7573

        0.0427

        0.0838

        0.0823

        25.2513

        18.8454

        86

        1.7399

        0.0499

        0.0888

        0.0829

        25.2175

        19.1465

        140

        1.7593

        0.0495

        0.0883

        0.0827

        25.0935

        25.0756

        87

        1.995

        0.0471

        0.0849

        0.0808

        24.5232

        26.2143

        141

        1.928

        0.0489

        0.0858

        0.0812

        25.1674

        20.5101

        88

        1.5206

        0.0469

        0.0897

        0.0873

        24.9337

        21.0024

        142

        1.7968

        0.049

        0.0878

        0.0823

        24.8841

        23.6743

        89

        1.7774

        0.0492

        0.0881

        0.0824

        24.9965

        23.4183

        143

        1.8666

        0.0523

        0.0867

        0.0817

        25.2744

        17.9818

        90

        1.901

        0.0518

        0.086

        0.0814

        26.0316

        23.1196

        144

        1.9305

        0.0469

        0.0857

        0.0812

        24.3775

        25.4362

        91

        2.1332

        0.0427

        0.0828

        0.0802

        23.5285

        20.9731

        145

        1.8844

        0.0473

        0.0862

        0.0814

        24.5074

        25.6039

        92

        1.6739

        0.0509

        0.0896

        0.0836

        25.5443

        26.1117

        146

        2.1306

        0.0428

        0.0829

        0.0802

        23.586

        21.9935

        93

        2.0748

        0.0445

        0.0838

        0.0805

        23.8232

        27.1298

        147

        2.0358

        0.0453

        0.0844

        0.0806

        24.0489

        23.8234

        94

        1.7031

        0.0503

        0.0893

        0.0833

        25.4034

        25.9908

        148

        2.0191

        0.0457

        0.0847

        0.0807

        24.0716

        20.7982

        95

        1.5128

        0.0499

        0.087

        0.0822

        24.3491

        24.4442

        149

        1.7634

        0.0495

        0.0883

        0.0827

        25.0762

        22.3246

        96

        2.1149

        0.0473

        0.0835

        0.0827

        24.8706

        17.908

        150

        1.9379

        0.0468

        0.0857

        0.0812

        24.3469

        14.7679

        97

        2.0889

        0.044

        0.0833

        0.0804

        23.7181

        19.358

        151

        1.5971

        0.0524

        0.0907

        0.0845

        26.0092

        21.6647

        98

        1.6385

        0.0515

        0.0901

        0.084

        25.7646

        24.4176

        152

        1.7539

        0.0497

        0.0886

        0.0829

        25.1438

        22.8425

        99

        2.0798

        0.0443

        0.0837

        0.0804

        23.7846

        22.9199

        153

        1.5717

        0.0514

        0.0869

        0.0821

        25.5443

        19.7778

        100

        2.0599

        0.0448

        0.0839

        0.0805

        23.8993

        27.2155

        154

        2.1254

        0.0431

        0.0829

        0.0802

        23.6

        23.509

        101

        1.9409

        0.0467

        0.0857

        0.0812

        24.3394

        26.1548

        155

        1.6895

        0.0505

        0.0894

        0.0834

        25.4612

        22.9209

        102

        1.5309

        0.054

        0.0917

        0.0855

        26.5002

        22.8396

        156

        1.313

        0.0582

        0.0942

        0.0887

        27.6172

        22.1153

        103

        2.0599

        0.0483

        0.0839

        0.0805

        25.4312

        27.2155

        157

        1.9928

        0.0507

        0.085

        0.0808

        27.5172

        21.6071

        104

        2.0716

        0.0446

        0.0838

        0.0805

        23.8855

        21.0998

        158

        1.5815

        0.0529

        0.091

        0.0848

        26.1635

        27.1675

        105

        1.5191

        0.0545

        0.0919

        0.0859

        26.6345

        26.2097

        159

        1.7977

        0.0489

        0.0878

        0.0822

        24.8706

        18.2192

        106

        2.0403

        0.0452

        0.0843

        0.0806

        24.0219

        26.4651

        160

        1.614

        0.0522

        0.0905

        0.0843

        25.9109

        23.3491

        107

        1.5928

        0.0526

        0.0908

        0.0845

        26.0898

        22.535

        161

        1.3634

        0.0515

        0.086

        0.0814

        24.0219

        11.5413

        108

        1.6271

        0.0519

        0.0903

        0.0842

        25.8622

        25.1169

        162

        1.4138

        0.0564

        0.093

        0.0871

        27.1744

        14.9281

        109

        1.4915

        0.0528

        0.0922

        0.0861

        22.9634

        17.6633

        163

        1.8583

        0.0481

        0.0868

        0.0817

        24.6355

        23.7408

        110

        1.884

        0.0493

        0.0863

        0.0815

        26.0514

        23.17

        164

        1.995

        0.0458

        0.0849

        0.0808

        24.1642

        26.2143

        111

        1.7135

        0.0483

        0.0967

        0.0821

        24.2223

        17.4587

        165

        2.0448

        0.0451

        0.084

        0.0806

        23.9494

        18.9181

        112

        1.7584

        0.0495

        0.0884

        0.0827

        25.1024

        19.3841

        166

        2.0207

        0.0456

        0.0846

        0.0807

        24.0701

        26.5271

        113

        1.3219

        0.058

        0.0942

        0.0883

        27.5408

        18.8045

        167

        1.5355

        0.0539

        0.0915

        0.0855

        26.4794

        23.2795

        114

        1.7193

        0.0501

        0.0891

        0.0831

        25.3098

        17.7366

        168

        1.2723

        0.0589

        0.0945

        0.089

        27.6356

        16.1154

        115

        1.5327

        0.054

        0.0916

        0.0855

        26.487

        18.3907

        169

        1.5464

        0.0481

        0.09

        0.0879

        23.8637

        15.3259

        116

        1.9046

        0.0443

        0.0872

        0.0817

        23.5048

        26.679

        170

        1.6108

        0.0523

        0.0905

        0.0844

        25.9367

        25.6348

        117

        1.9575

        0.0501

        0.089

        0.0837

        24.5765

        22.5143

        171

        1.5777

        0.0426

        0.0867

        0.0805

        25.7646

        23.4897

        118

        1.7493

        0.0488

        0.0902

        0.0813

        24.3868

        22.7569

        172

        1.7652

        0.0494

        0.0882

        0.0826

        25.0611

        18.592

        119

        2.0185

        0.0457

        0.0848

        0.0808

        24.0971

        23.3651

        173

        1.9028

        0.0471

        0.086

        0.0814

        24.4656

        22.555

        120

        1.8272

        0.0485

        0.0872

        0.082

        24.7304

        23.985

        174

        1.6029

        0.0523

        0.0906

        0.0844

        25.9687

        18.8271

        121

        1.874

        0.0529

        0.0863

        0.0847

        25.1422

        17.2462

        175

        2.1124

        0.0433

        0.083

        0.0802

        23.6162

        18.1662

        122

        2.1945

        0.0468

        0.0871

        0.0854

        25.1506

        22.1857

        176

        1.669

        0.0457

        0.0897

        0.0837

        26.8666

        12.4178

        123

        1.7075

        0.0502

        0.0893

        0.0833

        25.3843

        22.572

        177

        1.8474

        0.0522

        0.0871

        0.0819

        24.4325

        15.7458

        124

        1.6649

        0.0511

        0.0897

        0.0837

        24.2636

        23.1823

        178

        1.4121

        0.0564

        0.089

        0.0827

        26.7342

        19.6577

        125

        1.5481

        0.0537

        0.0914

        0.0854

        26.4351

        17.2562

        179

        1.5776

        0.0519

        0.0911

        0.0849

        24.538

        22.0483

        126

        1.7938

        0.054

        0.0923

        0.0815

        27.1646

        20.8569

        180

        2.0416

        0.0451

        0.0842

        0.0806

        23.9792

        26.6844

        127

        1.8699

        0.0477

        0.0866

        0.0816

        24.5852

        21.1865

        181

        1.4828

        0.0545

        0.0862

        0.0824

        25.2614

        13.5488

        128

        1.6538

        0.0517

        0.0898

        0.0838

        25.1965

        13.0942

        182

        1.4373

        0.056

        0.0927

        0.0869

        27.137

        18.1476

        129

        1.5966

        0.0524

        0.0907

        0.0845

        26.0095

        15.8454

        183

        2.0372

        0.0452

        0.0844

        0.0806

        24.039

        15.5775

        130

        1.5589

        0.0533

        0.0912

        0.0852

        26.2716

        24.7173

        184

        1.5678

        0.0531

        0.0911

        0.0849

        26.1908

        22.7366

        131

        1.7709

        0.0484

        0.0881

        0.0825

        25.8656

        21.2082

        185

        1.4618

        0.0557

        0.0925

        0.0865

        26.9845

        22.0238

        132

        1.4894

        0.055

        0.0922

        0.0861

        26.772

        21.989

        186

        1.4772

        0.0552

        0.0922

        0.0863

        26.8387

        20.168

        133

        1.5191

        0.0484

        0.0919

        0.0859

        25.7913

        26.2097

        187

        1.6811

        0.0541

        0.0867

        0.08

        23.7655

        26.5459

        134

        1.472

        0.0554

        0.0923

        0.0864

        26.8826

        10.8692

        188

        2.077

        0.0443

        0.0838

        0.0804

        23.7929

        20.0468

        Table-1: Data on quality variables (Contd..)

        S.No

        Y

        S

        P

        Mn

        CO 2

        PM

        S.No

        Y

        S

        P

        Mn

        CO 2

        PM

        189

        1.7989

        0.0502

        0.0876

        0.0839

        26.1635

        14.6393

        243

        1.6276

        0.0519

        0.0903

        0.0842

        25.8598

        22.865

        190

        1.5189

        0.0546

        0.0919

        0.0859

        26.6455

        22.2332

        244

        1.7075

        0.0548

        0.0902

        0.0806

        25.6952

        21.6118

        191

        1.8106

        0.0487

        0.0873

        0.0821

        24.7683

        26.3628

        245

        1.943

        0.0467

        0.0857

        0.0812

        24.339

        26.9357

        192

        1.8709

        0.0477

        0.0866

        0.0816

        24.5765

        17.2731

        246

        1.7473

        0.0498

        0.0888

        0.0829

        25.1725

        22.8612

        193

        2.1254

        0.0519

        0.0829

        0.0802

        26.6377

        23.509

        247

        1.5355

        0.0518

        0.0915

        0.0855

        24.0591

        23.2795

        194

        2.0074

        0.0547

        0.0849

        0.0808

        25.9107

        24.794

        248

        1.8037

        0.0488

        0.0874

        0.0821

        24.7996

        21.1308

        195

        1.4203

        0.0562

        0.093

        0.087

        27.1646

        24.381

        249

        1.6382

        0.0509

        0.0948

        0.0848

        23.5285

        20.6977

        196

        1.884

        0.0474

        0.0863

        0.0815

        24.5131

        23.17

        250

        1.5484

        0.0536

        0.0914

        0.0854

        26.4166

        16.5496

        197

        1.6565

        0.0488

        0.0898

        0.0838

        23.689

        20.5873

        251

        2.1306

        0.0499

        0.0829

        0.0802

        25.2822

        21.9935

        198

        1.4772

        0.0493

        0.0922

        0.0863

        25.8245

        20.168

        252

        2.0813

        0.0443

        0.0835

        0.0804

        23.7546

        18.7359

        199

        1.6467

        0.0514

        0.09

        0.0839

        25.7213

        21.7651

        253

        1.5189

        0.049

        0.0919

        0.0859

        25.2236

        22.2332

        200

        1.5481

        0.0541

        0.0914

        0.0854

        24.7139

        17.2562

        254

        1.9897

        0.046

        0.085

        0.0809

        24.2223

        20.043

        201

        1.9291

        0.0469

        0.0857

        0.0812

        24.3868

        22.9829

        255

        1.7709

        0.0493

        0.0881

        0.0825

        25.0268

        21.2082

        202

        1.5302

        0.0541

        0.0917

        0.0856

        26.5449

        18.5001

        256

        2.1036

        0.0434

        0.0831

        0.0802

        23.6479

        22.6864

        203

        1.9546

        0.0497

        0.0853

        0.0833

        25.7213

        25.9535

        257

        1.9379

        0.0446

        0.0857

        0.0812

        27.7734

        14.7679

        204

        1.5807

        0.0529

        0.091

        0.0848

        26.1647

        16.252

        258

        1.732

        0.0499

        0.0889

        0.083

        25.2513

        15.3479

        205

        1.6842

        0.0454

        0.0894

        0.0835

        24.5708

        25.2096

        259

        1.5566

        0.0533

        0.0913

        0.0853

        26.3249

        17.4542

        206

        1.8441

        0.0477

        0.0879

        0.0837

        26.5449

        17.0859

        260

        1.833

        0.0484

        0.0872

        0.082

        24.7152

        25.7512

        207

        1.6815

        0.0507

        0.0895

        0.0835

        25.5117

        15.0795

        261

        2.2203

        0.0411

        0.0811

        0.0801

        23.2822

        15.1375

        208

        2.1744

        0.0452

        0.0897

        0.0837

        25.9367

        17.3112

        262

        1.787

        0.0504

        0.0882

        0.083

        25.2175

        24.4946

        209

        2.0379

        0.0452

        0.0843

        0.0806

        24.0373

        22.9044

        263

        1.6649

        0.0512

        0.0897

        0.0837

        25.6071

        23.1823

        210

        1.7945

        0.0508

        0.0878

        0.0823

        24.6629

        25.836

        264

        1.6995

        0.0445

        0.0904

        0.083

        26.6287

        22.9649

        211

        1.7356

        0.0485

        0.0888

        0.083

        25.2467

        20.904

        265

        1.9028

        0.0479

        0.086

        0.0814

        25.768

        22.555

        212

        1.4674

        0.0505

        0.0831

        0.0836

        24.7718

        26.1788

        266

        1.8601

        0.0481

        0.0868

        0.0817

        24.6273

        26.4369

        213

        1.5631

        0.0533

        0.0912

        0.0851

        26.2318

        18.9892

        267

        1.8546

        0.0482

        0.087

        0.0818

        24.6572

        24.9651

        214

        1.6128

        0.0499

        0.0868

        0.0818

        24.0489

        22.7546

        268

        1.4231

        0.0562

        0.093

        0.087

        27.1577

        13.5973

        215

        1.944

        0.05

        0.0914

        0.0862

        24.776

        24.3503

        269

        1.8551

        0.0481

        0.0869

        0.0817

        24.6515

        25.5753

        216

        1.6406

        0.0515

        0.0901

        0.084

        25.7433

        20.3064

        270

        1.7214

        0.0501

        0.0891

        0.0831

        25.3033

        20.7909

        217

        1.5899

        0.0526

        0.0908

        0.0846

        26.1104

        25.7896

        271

        1.5663

        0.0531

        0.0911

        0.0849

        26.2078

        25.3239

        218

        1.9928

        0.046

        0.085

        0.0808

        24.2223

        21.6071

        272

        1.4697

        0.0555

        0.0924

        0.0864

        26.9282

        11.0376

        219

        1.5683

        0.0489

        0.0845

        0.0816

        25.3906

        17.1566

        273

        1.5833

        0.0528

        0.0909

        0.0847

        26.1543

        24.3146

        220

        1.6776

        0.0508

        0.0896

        0.0836

        25.5216

        22.9236

        274

        1.5043

        0.0548

        0.0921

        0.086

        26.7054

        24.6675

        221

        1.928

        0.0469

        0.0858

        0.0812

        24.3894

        20.5101

        275

        1.5827

        0.0528

        0.0909

        0.0847

        26.1556

        23.4246

        222

        1.8687

        0.0477

        0.0866

        0.0816

        24.5975

        25.2462

        276

        1.7349

        0.0499

        0.0888

        0.083

        25.2367

        18.5003

        223

        2.0802

        0.0443

        0.0836

        0.0804

        23.7655

        23.3256

        277

        1.4497

        0.0558

        0.0927

        0.0866

        27.0808

        13.3057

        224

        2.0729

        0.0446

        0.0838

        0.0805

        23.8637

        24.1629

        278

        1.8805

        0.0476

        0.0864

        0.0815

        24.5329

        18.114

        225

        1.7519

        0.0497

        0.0887

        0.0829

        25.1506

        15.0428

        279

        1.6882

        0.0505

        0.0894

        0.0835

        25.4659

        24.6686

        226

        2.0187

        0.0576

        0.0847

        0.0807

        24.2159

        25.1585

        280

        1.5218

        0.0544

        0.0918

        0.0857

        26.6085

        18.3717

        227

        1.9351

        0.0468

        0.0857

        0.0812

        24.3491

        22.1453

        281

        1.9861

        0.0462

        0.0852

        0.0809

        24.2288

        24.4741

        228

        1.5776

        0.053

        0.0911

        0.0849

        26.1737

        22.0483

        282

        1.8898

        0.0472

        0.0861

        0.0814

        24.4864

        25.3094

        229

        1.6602

        0.0453

        0.0871

        0.0818

        24.5531

        26.2917

        283

        1.6614

        0.0512

        0.0897

        0.0837

        25.6154

        20.6674

        230

        2.0207

        0.0537

        0.0846

        0.0807

        24.8011

        26.5271

        284

        1.6003

        0.0523

        0.0906

        0.0844

        25.9896

        22.1824

        231

        2.0052

        0.0562

        0.0827

        0.081

        24.7996

        23.4319

        285

        1.5277

        0.0541

        0.0917

        0.0856

        26.5532

        24.2823

        232

        1.7059

        0.0502

        0.0893

        0.0833

        25.3906

        24.2477

        286

        1.462

        0.0556

        0.0924

        0.0865

        26.9662

        18.6614

        233

        2.0263

        0.0476

        0.0896

        0.0827

        25.0762

        26.8603

        287

        1.6941

        0.0504

        0.0893

        0.0834

        25.4225

        24.3083

        234

        2.2487

        0.0409

        0.0808

        0.08

        23.0936

        22.4112

        288

        1.5818

        0.0528

        0.091

        0.0847

        26.1571

        14.2614

        235

        1.8605

        0.048

        0.0868

        0.0817

        24.6248

        25.4641

        293

        2.1692

        0.0425

        0.0826

        0.0801

        23.465

        21.8556

        236

        1.7298

        0.05

        0.089

        0.083

        25.2637

        24.8036

        294

        1.7839

        0.0492

        0.088

        0.0824

        24.9794

        25.0456

        237

        1.5314

        0.054

        0.0917

        0.0855

        26.4927

        24.9941

        295

        1.8407

        0.0484

        0.0871

        0.0819

        24.7004

        22.5337

        238

        1.669

        0.051

        0.0897

        0.0837

        25.5796

        12.4178

        296

        1.5931

        0.0526

        0.0907

        0.0845

        26.0383

        .

        239

        1.5578

        0.0533

        0.0913

        0.0852

        26.3061

        23.3831

        297

        1.403

        0.0565

        0.0931

        0.0872

        27.2239

        14.5255

        240

        1.4373

        0.0562

        0.0927

        0.0869

        24.4998

        18.1476

        298

        1.6521

        0.0513

        0.0899

        0.0839

        25.6849

        16.3442

        241

        1.9045

        0.0471

        0.086

        0.0814

        24.4623

        22.5556

        299

        1.806

        0.0488

        0.0874

        0.0821

        24.7792

        23.1008

        242

        1.9134

        0.0527

        0.0858

        0.0813

        26.3884

        25.1896

        300

        2.0586

        0.0449

        0.0839

        0.0805

        23.8999

        25.0646

        Table-1: Data on quality variables (Contd..)

        S.No

        Y

        S

        P

        Mn

        CO2

        PM

        S.No

        Y

        S

        P

        Mn

        CO2

        PM

        301

        1.705

        0.045

        0.085

        0.084

        24.174

        12.094

        311

        1.929

        0.047

        0.086

        0.081

        24.387

        22.983

        302

        1.778

        0.053

        0.086

        0.083

        25.206

        19.827

        312

        1.804

        0.049

        0.087

        0.082

        24.8

        21.131

        303

        1.895

        0.05

        0.086

        0.085

        25.571

        25.802

        313

        1.42

        0.056

        0.093

        0.087

        27.165

        24.381

        304

        1.816

        0.053

        0.087

        0.082

        24.796

        22.882

        314

        1.535

        0.054

        0.092

        0.085

        26.48

        22.945

        305

        1.831

        0.052

        0.087

        0.084

        25.765

        15.741

        315

        1.763

        0.049

        0.088

        0.083

        25.076

        22.325

        306

        1.855

        0.05

        0.085

        0.084

        24.521

        21.324

        316

        1.877

        0.048

        0.087

        0.082

        24.553

        24.456

        307

        1.886

        0.054

        0.085

        0.084

        24.797

        20.171

        317

        2.036

        0.045

        0.084

        0.081

        24.049

        23.823

        308

        1.581

        0.054

        0.084

        0.086

        25.396

        14.507

        318

        1.732

        0.05

        0.089

        0.083

        25.251

        15.348

        309

        1.829

        0.049

        0.087

        0.084

        24.753

        17.822

        319

        2.133

        0.043

        0.083

        0.08

        23.528

        20.973

        310

        1.957

        0.046

        0.089

        0.083

        25.447

        23.947

        320

        1.674

        0.051

        0.09

        0.084

        25.544

        26.112

        Multivariate control chart

        T²

        T²

        T2 control chart is developed using Minitab 18 by considering data on the six quality characteristics of hot metal and is shown in Fig.1.

        T² Chart of Y, …, PM

        T² Chart of Y, …, PM

        1 6000

        1 4000

        1 2000

        1 0000

        8000

        6000

        4000

        2000

        0

        UMCeLd=ia2n2=6

        1 6000

        1 4000

        1 2000

        1 0000

        8000

        6000

        4000

        2000

        0

        UMCeLd=ia2n2=6

        1 33 65 97

        1 29 1 61 1 93 225

        Sample

        257 289

        1 33 65 97

        1 29 1 61 1 93 225

        Sample

        257 289

        At least one estimated historical parameter is used in the calculations.

        At least one estimated historical parameter is used in the calculations.

        Out-of-control signal for each observation

        Fig. 1: T2 chart of Y, , PM

        Out of control signals of each observation is identified from T2 chart using upper control limit. For each observation in- control/out-of-control class is identifiedand presented in Table-2.

        Table-2: Out-of-control signals

        S.No.

        Y

        S

        P

        Mn

        CO3

        PM

        CLG

        S.No.

        Y

        S

        P

        Mn

        CO3

        PM

        CLG

        1

        1

        1

        1

        1

        1

        1

        1

        51

        1

        1

        1

        1

        1

        1

        1

        2

        0

        0

        0

        0

        0

        0

        3

        52

        1

        1

        1

        1

        1

        1

        1

        3

        0

        0

        0

        0

        0

        0

        3

        53

        1

        1

        1

        1

        1

        1

        1

        4

        1

        1

        1

        1

        1

        1

        1

        54

        1

        1

        1

        1

        1

        1

        1

        5

        0

        0

        0

        0

        0

        0

        3

        55

        0

        0

        0

        0

        0

        0

        3

        6

        1

        1

        1

        1

        1

        1

        1

        56

        1

        1

        1

        1

        1

        1

        1

        7

        1

        1

        1

        1

        1

        1

        1

        57

        1

        0

        1

        0

        0

        0

        2

        8

        1

        1

        1

        1

        1

        1

        1

        58

        0

        0

        0

        0

        0

        0

        3

        9

        1

        1

        1

        1

        1

        1

        1

        59

        1

        1

        1

        1

        1

        1

        1

        10

        0

        0

        0

        0

        0

        0

        3

        60

        1

        1

        1

        1

        1

        1

        1

        11

        0

        0

        0

        0

        0

        1

        11

        61

        1

        1

        1

        1

        1

        1

        1

        12

        1

        1

        1

        1

        1

        1

        1

        62

        0

        0

        0

        0

        0

        0

        3

        13

        1

        1

        1

        1

        1

        1

        1

        63

        1

        1

        1

        1

        1

        1

        1

        14

        1

        1

        1

        1

        1

        1

        1

        64

        0

        0

        0

        0

        0

        0

        3

        15

        1

        1

        1

        1

        1

        1

        1

        65

        0

        0

        0

        0

        0

        0

        3

        16

        1

        1

        1

        1

        1

        1

        1

        66

        0

        0

        0

        0

        0

        0

        3

        17

        1

        1

        1

        1

        1

        1

        1

        67

        0

        0

        0

        0

        0

        0

        3

        18

        1

        1

        1

        1

        1

        1

        1

        68

        0

        0

        0

        0

        0

        0

        3

        19

        1

        1

        1

        1

        1

        1

        1

        69

        1

        1

        1

        1

        1

        1

        1

        20

        0

        0

        0

        0

        0

        0

        3

        70

        0

        0

        0

        0

        0

        0

        3

        21

        1

        1

        1

        1

        1

        1

        1

        71

        1

        1

        1

        1

        1

        1

        1

        22

        1

        1

        1

        1

        1

        1

        1

        72

        1

        1

        1

        1

        1

        1

        1

        23

        1

        1

        1

        1

        1

        1

        1

        73

        1

        1

        1

        1

        1

        1

        1

        24

        0

        0

        0

        0

        0

        1

        11

        74

        0

        0

        0

        0

        0

        1

        11

        25

        1

        1

        1

        1

        1

        1

        1

        75

        0

        0

        0

        0

        0

        1

        11

        26

        1

        1

        1

        1

        1

        1

        1

        76

        0

        0

        0

        0

        0

        1

        11

        27

        0

        0

        0

        0

        0

        0

        3

        77

        1

        1

        1

        1

        1

        1

        1

        28

        0

        0

        0

        0

        0

        0

        3

        78

        1

        1

        1

        1

        1

        1

        1

        29

        1

        1

        1

        1

        1

        1

        1

        79

        1

        1

        1

        1

        1

        1

        1

        30

        1

        1

        1

        1

        1

        1

        1

        80

        1

        1

        1

        1

        1

        1

        1

        31

        0

        0

        0

        0

        0

        0

        3

        81

        1

        1

        1

        1

        1

        1

        1

        32

        0

        0

        0

        0

        0

        1

        11

        82

        0

        0

        0

        0

        0

        0

        3

        33

        1

        1

        1

        1

        1

        1

        1

        83

        0

        0

        1

        0

        0

        1

        12

        34

        1

        1

        1

        1

        1

        1

        1

        84

        1

        1

        1

        1

        1

        1

        1

        35

        1

        1

        1

        1

        1

        1

        1

        85

        1

        1

        1

        1

        1

        1

        1

        36

        0

        0

        0

        0

        0

        1

        11

        86

        1

        1

        1

        1

        1

        1

        1

        37

        0

        0

        1

        0

        1

        1

        9

        87

        1

        1

        1

        0

        0

        1

        13

        38

        0

        0

        0

        0

        0

        0

        3

        88

        0

        0

        0

        0

        0

        1

        11

        39

        0

        0

        1

        0

        0

        1

        10

        89

        1

        1

        1

        1

        1

        1

        1

        40

        0

        0

        0

        0

        0

        0

        3

        90

        1

        0

        0

        0

        0

        1

        14

        41

        1

        1

        1

        1

        1

        1

        1

        91

        1

        1

        1

        1

        1

        1

        1

        42

        0

        0

        0

        0

        0

        0

        3

        92

        1

        1

        1

        1

        1

        1

        1

        43

        1

        1

        1

        1

        1

        1

        1

        93

        1

        1

        1

        1

        1

        1

        1

        44

        0

        0

        0

        0

        0

        0

        3

        94

        1

        1

        1

        1

        1

        1

        1

        45

        1

        1

        1

        1

        1

        1

        1

        95

        0

        0

        0

        0

        0

        0

        3

        46

        1

        0

        1

        0

        0

        0

        2

        96

        0

        0

        0

        0

        0

        1

        11

        47

        1

        1

        1

        1

        1

        1

        1

        97

        1

        1

        1

        1

        1

        1

        1

        48

        0

        0

        0

        0

        0

        0

        3

        98

        1

        1

        1

        1

        1

        1

        1

        49

        1

        1

        1

        1

        1

        1

        1

        99

        1

        1

        1

        1

        1

        1

        1

        50

        1

        1

        1

        1

        1

        1

        1

        100

        1

        1

        1

        1

        1

        1

        1

        Table-2: Out-of-control signals (Contd..)

        S.No.

        Y

        S

        P

        Mn

        CO3

        PM

        CLG

        S.No.

        Y

        S

        P

        Mn

        CO3

        PM

        CLG

        101

        1

        1

        1

        1

        1

        1

        1

        151

        1

        1

        1

        1

        1

        1

        1

        102

        1

        1

        1

        1

        1

        1

        1

        152

        1

        1

        1

        1

        1

        1

        1

        103

        1

        0

        0

        0

        0

        1

        14

        153

        0

        0

        0

        0

        0

        1

        11

        104

        1

        1

        1

        1

        1

        1

        1

        154

        1

        1

        1

        1

        1

        1

        1

        105

        1

        1

        1

        1

        1

        1

        1

        155

        1

        1

        1

        1

        1

        1

        1

        106

        1

        1

        1

        1

        1

        1

        1

        156

        1

        0

        1

        0

        0

        0

        2

        107

        1

        1

        1

        1

        1

        1

        1

        157

        0

        0

        0

        0

        0

        0

        3

        108

        1

        1

        1

        1

        1

        1

        1

        158

        1

        1

        1

        1

        1

        1

        1

        109

        0

        0

        0

        0

        0

        0

        3

        159

        1

        1

        1

        1

        1

        1

        1

        110

        0

        0

        0

        0

        0

        0

        3

        160

        1

        1

        1

        1

        1

        1

        1

        111

        1

        1

        1

        1

        1

        1

        1

        161

        0

        0

        0

        0

        0

        1

        11

        112

        1

        1

        1

        1

        1

        1

        1

        162

        1

        1

        1

        1

        1

        1

        1

        113

        1

        0

        1

        0

        0

        1

        15

        163

        1

        1

        1

        1

        1

        1

        1

        114

        1

        1

        1

        1

        1

        1

        1

        164

        1

        1

        1

        1

        1

        1

        1

        115

        1

        1

        1

        1

        1

        1

        1

        165

        1

        1

        1

        1

        1

        1

        1

        116

        0

        0

        0

        0

        0

        1

        11

        166

        1

        1

        1

        1

        1

        1

        1

        117

        0

        0

        0

        0

        0

        0

        3

        167

        1

        1

        1

        1

        1

        1

        1

        118

        0

        0

        0

        0

        0

        1

        11

        168

        1

        0

        0

        0

        0

        1

        14

        119

        1

        1

        1

        1

        1

        1

        1

        169

        0

        0

        0

        0

        0

        0

        3

        120

        1

        1

        1

        1

        1

        1

        1

        170

        1

        1

        1

        1

        1

        1

        1

        121

        0

        0

        0

        0

        0

        0

        3

        171

        0

        0

        0

        0

        0

        0

        3

        122

        0

        0

        0

        0

        0

        1

        11

        172

        1

        1

        1

        1

        1

        1

        1

        123

        1

        1

        1

        1

        1

        1

        1

        173

        1

        1

        1

        1

        1

        1

        1

        124

        0

        0

        0

        0

        0

        0

        3

        174

        1

        1

        1

        1

        1

        1

        1

        125

        1

        1

        1

        1

        1

        1

        1

        175

        1

        1

        1

        1

        1

        1

        1

        126

        0

        0

        0

        0

        0

        0

        3

        176

        0

        0

        0

        0

        0

        0

        3

        127

        1

        1

        1

        1

        1

        1

        1

        177

        0

        0

        0

        0

        0

        0

        3

        128

        0

        0

        0

        0

        0

        0

        3

        178

        0

        0

        0

        0

        0

        1

        11

        129

        1

        1

        1

        1

        1

        1

        1

        179

        0

        0

        0

        0

        0

        0

        3

        130

        1

        1

        1

        1

        1

        1

        1

        180

        1

        1

        1

        1

        1

        1

        1

        131

        0

        0

        0

        0

        0

        0

        3

        181

        0

        1

        0

        0

        1

        0

        5

        132

        1

        1

        1

        1

        1

        1

        1

        182

        1

        1

        1

        1

        1

        1

        1

        133

        0

        0

        0

        0

        1

        0

        4

        183

        1

        1

        1

        1

        1

        1

        1

        134

        1

        1

        1

        1

        1

        1

        1

        184

        1

        1

        1

        1

        1

        1

        1

        135

        0

        0

        0

        0

        0

        0

        3

        185

        1

        1

        1

        1

        1

        1

        1

        136

        1

        1

        1

        1

        1

        1

        1

        186

        1

        1

        1

        1

        1

        1

        1

        137

        0

        0

        0

        0

        0

        0

        3

        187

        0

        0

        0

        0

        0

        0

        3

        138

        0

        0

        0

        0

        0

        0

        3

        188

        1

        1

        1

        1

        1

        1

        1

        139

        0

        0

        0

        0

        0

        0

        3

        189

        0

        0

        0

        0

        0

        0

        3

        140

        1

        1

        1

        1

        1

        1

        1

        190

        1

        1

        1

        1

        1

        1

        1

        141

        1

        0

        0

        0

        0

        1

        14

        191

        1

        1

        1

        1

        1

        1

        1

        142

        1

        1

        1

        1

        1

        1

        1

        192

        1

        1

        1

        1

        1

        1

        1

        143

        0

        0

        0

        0

        1

        1

        16

        193

        0

        0

        0

        0

        0

        1

        11

        144

        1

        1

        1

        1

        1

        1

        1

        194

        0

        0

        0

        0

        0

        0

        3

        145

        1

        1

        1

        1

        1

        1

        1

        195

        1

        1

        1

        1

        1

        1

        1

        146

        1

        1

        1

        1

        1

        1

        1

        196

        1

        1

        1

        1

        1

        1

        1

        147

        1

        1

        1

        1

        1

        1

        1

        197

        0

        0

        0

        0

        0

        0

        3

        148

        1

        1

        1

        1

        1

        1

        1

        198

        0

        0

        0

        0

        0

        0

        3

        149

        1

        1

        1

        1

        1

        1

        1

        199

        1

        1

        1

        1

        1

        1

        1

        150

        1

        1

        1

        1

        1

        1

        1

        200

        0

        0

        0

        0

        0

        0

        3

        Table-2: Out-of-control signals (Contd..)

        S.No.

        Y

        S

        P

        Mn

        CO3

        PM

        CLG

        S.No.

        Y

        S

        P

        Mn

        CO3

        PM

        CLG

        201

        1

        1

        1

        1

        1

        1

        1

        286

        1

        1

        1

        1

        1

        1

        1

        202

        1

        1

        1

        1

        1

        1

        1

        287

        1

        1

        1

        1

        1

        1

        1

        203

        0

        1

        1

        0

        0

        0

        6

        288

        1

        1

        1

        1

        1

        1

        1

        204

        1

        1

        1

        1

        1

        1

        1

        289

        1

        1

        1

        1

        1

        1

        1

        205

        0

        0

        0

        0

        0

        0

        3

        290

        1

        1

        1

        1

        1

        1

        1

        206

        0

        0

        0

        0

        0

        0

        3

        291

        1

        1

        1

        1

        1

        1

        1

        207

        1

        1

        1

        1

        1

        1

        1

        292

        1

        1

        1

        1

        1

        1

        1

        208

        0

        0

        0

        0

        0

        0

        3

        293

        1

        1

        0

        0

        0

        1

        19

        209

        1

        1

        1

        1

        1

        1

        1

        294

        1

        1

        1

        1

        1

        1

        1

        210

        0

        0

        0

        0

        0

        0

        3

        295

        1

        1

        1

        1

        1

        1

        1

        211

        0

        0

        1

        0

        0

        0

        7

        236

        1

        1

        1

        1

        1

        1

        1

        212

        0

        0

        0

        0

        0

        0

        3

        237

        1

        1

        1

        1

        1

        1

        1

        213

        1

        1

        1

        1

        1

        1

        1

        238

        1

        1

        1

        1

        1

        1

        1

        214

        0

        0

        0

        0

        0

        0

        3

        239

        1

        1

        1

        1

        1

        1

        1

        215

        0

        0

        0

        0

        0

        0

        3

        240

        0

        0

        0

        0

        0

        0

        3

        216

        1

        1

        1

        1

        1

        1

        1

        241

        1

        1

        1

        1

        1

        1

        1

        217

        1

        1

        1

        1

        1

        1

        1

        242

        1

        0

        0

        0

        0

        1

        14

        218

        1

        1

        1

        1

        1

        1

        1

        243

        1

        1

        1

        1

        1

        1

        1

        219

        0

        0

        0

        0

        0

        0

        3

        244

        0

        0

        0

        0

        0

        1

        11

        220

        1

        1

        1

        1

        1

        1

        1

        245

        1

        1

        1

        1

        1

        1

        1

        221

        1

        1

        1

        1

        1

        1

        1

        246

        1

        1

        1

        1

        1

        1

        1

        222

        1

        1

        1

        1

        1

        1

        1

        247

        0

        0

        0

        0

        0

        0

        3

        223

        1

        1

        1

        1

        1

        1

        1

        248

        1

        1

        1

        1

        1

        1

        1

        224

        1

        1

        1

        1

        1

        1

        1

        249

        0

        0

        0

        0

        0

        0

        3

        225

        1

        1

        1

        1

        1

        1

        1

        250

        1

        1

        1

        1

        1

        1

        1

        226

        0

        0

        0

        0

        0

        0

        3

        251

        0

        0

        1

        0

        0

        1

        12

        227

        1

        1

        1

        1

        1

        1

        1

        252

        1

        1

        1

        1

        1

        1

        1

        228

        1

        1

        1

        1

        1

        1

        1

        253

        0

        1

        0

        0

        0

        1

        18

        229

        0

        0

        0

        0

        0

        0

        3

        254

        1

        1

        1

        1

        1

        1

        1

        230

        0

        0

        0

        0

        0

        0

        3

        255

        1

        1

        1

        1

        1

        1

        1

        231

        0

        0

        0

        0

        0

        0

        3

        256

        1

        1

        1

        1

        1

        1

        1

        232

        1

        1

        1

        1

        1

        1

        1

        257

        0

        0

        0

        0

        0

        0

        3

        233

        0

        0

        0

        0

        0

        1

        11

        258

        1

        1

        1

        1

        1

        1

        1

        234

        0

        1

        1

        0

        0

        1

        17

        259

        1

        1

        1

        1

        1

        1

        1

        235

        1

        1

        1

        1

        1

        1

        1

        260

        1

        1

        1

        1

        1

        1

        1

        271

        1

        1

        1

        1

        1

        1

        1

        261

        0

        0

        0

        0

        1

        0

        4

        272

        1

        1

        1

        1

        1

        1

        1

        262

        0

        0

        0

        0

        0

        1

        11

        273

        1

        1

        1

        1

        1

        1

        1

        263

        1

        1

        1

        1

        1

        1

        1

        274

        1

        1

        1

        1

        1

        1

        1

        264

        0

        0

        0

        0

        0

        0

        3

        275

        1

        1

        1

        1

        1

        1

        1

        265

        0

        0

        0

        0

        0

        0

        3

        276

        1

        1

        1

        1

        1

        1

        1

        266

        1

        1

        1

        1

        1

        1

        1

        277

        1

        1

        1

        1

        1

        1

        1

        267

        1

        1

        1

        1

        1

        1

        1

        278

        1

        1

        1

        1

        1

        1

        1

        268

        1

        1

        1

        1

        1

        1

        1

        279

        1

        1

        1

        1

        1

        1

        1

        269

        1

        1

        1

        1

        1

        1

        1

        280

        1

        1

        1

        1

        1

        1

        1

        270

        1

        1

        1

        1

        1

        1

        1

        281

        1

        1

        1

        1

        1

        1

        1

        296

        1

        1

        1

        1

        1

        1

        1

        282

        1

        1

        1

        1

        1

        1

        1

        297

        1

        1

        1

        1

        1

        1

        1

        283

        1

        1

        1

        1

        1

        1

        1

        298

        1

        1

        1

        1

        1

        1

        1

        284

        1

        1

        1

        1

        1

        1

        1

        299

        1

        1

        1

        1

        1

        1

        1

        285

        1

        1

        1

        1

        1

        1

        1

        300

        1

        1

        1

        1

        1

        1

        1

        Table-2: Out-of-control signals (Contd..)

        S.No.

        Y

        S

        P

        Mn

        CO3

        PM

        CLG

        S.No.

        Y

        S

        P

        Mn

        CO3

        PM

        CLG

        301

        0

        0

        0

        0

        0

        1

        11

        311

        1

        1

        1

        1

        1

        1

        1

        302

        0

        0

        0

        0

        0

        0

        3

        312

        0

        1

        0

        1

        1

        1

        21

        303

        0

        0

        1

        0

        0

        0

        8

        313

        1

        1

        1

        1

        1

        1

        1

        304

        0

        0

        0

        0

        0

        0

        3

        314

        0

        1

        0

        0

        0

        1

        18

        305

        0

        0

        1

        1

        0

        1

        20

        315

        1

        1

        1

        1

        1

        1

        1

        306

        1

        0

        0

        0

        0

        1

        14

        316

        0

        0

        0

        0

        0

        1

        11

        307

        0

        0

        0

        0

        0

        0

        3

        317

        1

        1

        1

        1

        1

        1

        1

        308

        0

        0

        0

        0

        0

        0

        3

        318

        1

        1

        1

        1

        1

        1

        1

        309

        0

        0

        0

        0

        0

        0

        3

        319

        1

        1

        1

        1

        1

        1

        1

        310

        0

        0

        0

        0

        0

        0

        3

        320

        0

        0

        0

        0

        0

        1

        11

        Note: 1-in-control signal; 0- out-of-control signal

        Note: CLG groups, 1: All variables in control; 2: S,Mn, CO2 and PM are out of control; 3: Y,S,P, Mn, CO2 and PM out of control; 4: Y,S,P, Mn and PM out of control; 5: Y,P,Mn and CO2 out of control; 6: Y, Mn,CO2 and PM out of control; 7: Y,S, Mn, CO2 and PM out of control; 8: Y,S, Mn, CO2 and PM out of control; 9: Y,S and Mn out of control; 10: Y,S Mn and CO2 out of control; 11: Y,S,P, Mn, and CO2 out of control; 12: Y,S Mn and CO2 out of control; 13: Mn and CO2 out of control; 14: S, P, Mn and CO2 out of control; 15: S, Mn and CO2 out of control; 16: Y, S, P and Mn out of control; 17: Y, Mn and CO2 out of control; 18: Y, P, Mn and CO2 out of control; 19: P, Mn and CO2 out of control; 20: Y, S and CO2 out of control; 21: Y and P out of control;

        Table-3: Testing data set

        S.No.

        Y

        S

        P

        Mn

        CO2

        PM

        321

        1.705

        0.045

        0.085

        0.084

        24.174

        12.094

        322

        1.778

        0.053

        0.086

        0.083

        25.206

        19.827

        323

        1.895

        0.05

        0.086

        0.085

        25.571

        25.802

        324

        1.816

        0.053

        0.087

        0.082

        24.796

        22.882

        325

        1.831

        0.052

        0.087

        0.084

        25.765

        15.741

        326

        1.855

        0.05

        0.085

        0.084

        24.521

        21.324

        327

        1.886

        0.054

        0.085

        0.084

        24.797

        20.171

        328

        1.581

        0.054

        0.084

        0.086

        25.396

        14.507

        329

        1.829

        0.049

        0.087

        0.084

        24.753

        17.822

        330

        1.957

        0.046

        0.089

        0.083

        25.447

        23.947

        331

        1.929

        0.047

        0.086

        0.081

        24.387

        22.983

        332

        1.804

        0.049

        0.087

        0.082

        24.8

        21.131

        333

        1.42

        0.056

        0.093

        0.087

        27.165

        24.381

        334

        1.535

        0.054

        0.092

        0.085

        26.48

        22.945

        335

        1.763

        0.049

        0.088

        0.083

        25.076

        22.325

        336

        1.877

        0.048

        0.087

        0.082

        24.553

        24.456

        337

        2.036

        0.045

        0.084

        0.081

        24.049

        23.823

        338

        1.732

        0.05

        0.089

        0.083

        25.251

        15.348

        339

        2.133

        0.043

        0.083

        0.08

        23.528

        20.973

        340

        1.674

        0.051

        0.09

        0.084

        25.544

        26.112

      3. Multi layer perceptron of neural network model

    MLP of Neural networks is implemented to the case study using data on six quality characteristics of observations as independent variables and class out of control obtained are as dependent variables through neural networks module of SPSS 18.

    Results of neural network analysis are presented in the following sect following outputs of neural network analysis are presented and discussed below.

    MLP Network Information

    • Number of inputs = Six (Quality Parameters).

    • Number of output units = 1(Class of Out of Control)

    • Maximum number of hidden units = 10

    • Training dataset = 94.1% of the sample

    • Testing dataset = 5.9% of the sample.

    • Type of training = Batch training

    • Optimizing Algorithm = scaled congregated method

    • Training options, Initial = 0.0000005

Case Processing Summary

Table-4 gives information about the datasets used to build the ANN model. From the table it is observed that the training dataset contains in 94.1% of the sample and testing dataset contains 5.9% of the sample.

Table-4: Case processing summary

Case Processing Summary

N

Percent

Sample

Training

320

94.1%

Testing

20

5.9%

Valid

340

100.0%

Excluded

0

Total

340

Network Information

The Table-5 shows network information. In the Table-5, the number of neurons in every layer and one independent variable (out of control class) denoted as cluster number (CLG). Automatic architecture selection chose 10 nodes for the hidden

layer, while the output layer had 18 units to code the dependent variable. For the hidden layer the activation function was the hyperbolic tangent, while for the output layer also the softmax function is used.

Table-5: Network information

Input Layer

Factors

1

Y

2

S

3

P

4

Mn

5

CO2

6

PM

Number of Units

1110

Hidden Layer(s)

Number of Hidden Layers

1

Number of Units in Hidden Layer

20

Activation Function

Hyperbolic tangent

Output Layer

Dependent Variables

1

CLG

Number of Units

21

Activation Function

Softmax

Error Function

Cross-entropy

Model Summary

The model summary is shown in Table-6.

Table-6: Model Summary

Training

Cross Entropy Error

0.268

Percent Incorrect Predictions

0.0%

Stopping Rule Used

Training error ratio criterion (.001) achieved

Testing

Cross Entropy Error

0.039

Percent Incorrect Predictions

0.0%

Table-6 provides information related to the results of training and testing sample. Cross entropy error is given for both training and testing sample since is the error function that network minimizes during the training phase. The small value (0.268) of this error indicates the power of the model to predict financial soundness in the training set. The cross entropy error (0.0398) is also very less for the testing data set, meaning that the network model has not been over fitted to the training data. The result justifies the role of testing sample which is to prevent overtraining. From the results, it is observed that, there are no incorrect predictions based on training and testing sample.

Classification Summary

Table-7 displays classification for categorical dependent variable (financial soundness).

Table-7: Classification (Training Data Set)

Sample

Out of Control Class

Predicted

%Correct

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

Training

1

198

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

100.0%

2

3

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

100.0%

3

0

0

72

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

100.0%

4

0

0

0

2

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

100.0%

5

0

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

100.0%

6

0

0

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

100.0%

7

0

0

0

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

100.0%

8

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

100.0%

9

0

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

100.0%

10

0

0

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

100.0%

11

0

0

0

0

0

0

0

0

0

0

22

0

0

0

0

0

0

0

0

0

0

100.0%

12

0

0

0

0

0

0

0

0

0

0

0

2

0

0

0

0

0

0

0

0

0

100.0%

13

0

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

0

100.0%

14

0

0

0

0

0

0

0

0

0

0

0

0

0

6

0

0

0

0

0

0

0

100.0%

15

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0

0

0

0

100.0%

16

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0

0

0

100.0%

17

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0

0

100.0%

18

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

2

0

0

0

100.0%

19

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

0

100.0%

20

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

100.0%

21

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

100.0%

Table-8: Classification (Testing Data Set)

Sample

Out of Control Class

Predicted

%Correct

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

Testing

1

6

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

100%

2

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0%

3

0

0

6

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

100%

4

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0%

5

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0%

6

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0%

7

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0%

8

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

100%

9

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0%

10

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0%

11

0

0

0

0

0

0

0

0

0

0

3

0

0

0

0

0

0

0

0

0

0

100%

12

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.0%

13

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0%

14

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

100%

15

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.0%

16

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.0%

17

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.0%

18

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0

100%

19

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0%

20

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

100%

21

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

100%

Overall Percent

30%

0%

30%

0%

0%

0%

0%

5%

0%

0%

15%

0%

0%

5%

0%

0%

0%

5%

0%

5%

5%

100%

As can be seen, the MLP network classification results, 100% out of control classes are correctly classified both in training and testing sample. Overall 100.0% of the training cases and testing cases were correctly classified.

Importance Analysis

Table-9 gives the impact of each independent variable in the ANN model in terms of relative and normalized importance.

Table-9: Independent variable importance values

Variable

Importance

Normalized Importance

Y

0.155

88.4%

S

0.172

98.1%

P

0.163

93.3%

Mn

0.169

96.5%

CO2

0.175

100.0%

PM

0.166

95.1%

Fig. 2: Showing the normalized importance values

From the table, it is apparent that CO2, and S are showing the high importance in classification of out-of-control signals since the relative importance of these variables are

0.175 and 0.172 respectively. Yield has the lowest effect

relatively on the classification of out-of control signals is obtained since the relative importance of the variable is

0.155. Fig.1 also depicts the importance of the variables,

    1. ., how sensitive is the model is the change of each quality variables variable.

      From the normalized importance values, it is observed that, highest weightage obtained with emission of CO2 during smelting process in classifying the classes correctly. Artificial neural network models are increasingly used in scoring with varying success. According to some statisticians, although these new methods are interesting and sometimes more efficient than traditional statistical techniques, they are also less robust and less well founded. Furthermore, neural networks are unable to explain the results they provide. Finally, they are as black boxes with unknown operating rules.

      5.0 CONCLUDING REMARKS

      In this study, MLP model is proposed and is implemented to monitoring and control of smelting process of blast furnace of an integrated steel plant. This study presents a multilayer perceptron network model for out-of- control condition analysis in multivariate variable processes. In individual control chart method, every variable has its own control chart and variables are considered as independent. With the proposed model, a large number of variables, affecting real processes can be analyzed together. Hence, the loss of time and labor are eliminated. For these reasons, multilayer neural network is considered to be an effective tool for Prediction, analysis and control of complex processes.

      6.0 FUTURE SCOPE OF STUDY

      Future work will need to validate these findings in larger and more diverse samples, there is strong evidence that the proposed model can be used effectively to predict classification of out-of-control signals in respect of multiple variables, in particular to help the management to monitor the quality of hot metal. The method is a data driven method required experimentation to validate the results.

      REFERENCES

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      2. Igor Greovnik, Tadej Kodelja, Robert Vertnik and Boidar arler (2012), Application of artificial neural networks to improve steel Production process, Proceedings of the IASTED International Conference( 2012)June 25 – 27, 2012 Napoli, Italy,

        Artificial Intelligence and Soft Computing ASC, pp.249-255

      3. Edgar A. Ruelas-Santoyo, José A. Vázquez-López, Javier Yañez- Mendiola, Roberto Baeza-Serrato, José A. Jimenez-García and Juan Sanchez-Márquez (2108), System for the recognition of wear patterns on microstructures of carbon steels using a multilayer perceptron, Ingeniería e Investigación vol. 38.No. 1,pp.113-120

      4. Owunna.I and A. E.Ikpe (2019), Modelling and Prediction of the Mechanical Properties of Tig Welded Joint for AISI 4130 Low Carbon Steel Plates using Artificial Neural Network (ANN) Approach, Nigerian Journal of Technology (NIJOTECH), Vol. 38, No. 1, pp. 117 126

      5. Abhulimen,I.U. and J.I. Achebo (2014), Application Of Artificial Neural Network In Predicting The Weld Quality of A Tungsten

        Inert Gas Welded Mild Steel Pipe Joint, International Journal of Scientific & Technology Research, Vol. 3, No. 1, pp.277-285

      6. Tadeusz Wieczorek and Mirosaw Kordos (2010), Neural Network-Based Prediction of Additives in the Steel Refinement Process, Computer Methods in Materials Science, Vol.10, N0.1, pp.1-9

      7. Boran.S and D.D. Diren (2017), Analysis of Out of Control Signals in Multivariate Processes with Multilayer Neural Network, Special issue of the 3rd International Conference on Computational and Experimental Science and Engineering (ICCESEN 2016), Vol.132.No.3-II,pp.1054-1057.

      8. Francisco Aparisi, José Sanz (2010), Interpreting the Out-of- Control Signals of Multivariate Control Charts Employing Neural Networks, International Journal of Computer and Information Engineering, Vol.4,No.1, pp.24-28

      9. Shihua Luo Tianxin Chen and Ling Jian (2018), Using Principal Component Analysis and Least Squares Support Vector Machine to Predict the Silicon Content in Blast Furnace System, IJOE

        Vol. 14, No. 4, pp.149-162

      10. Ishita Ghosh and Nilratan Chakraborty (2018), An Artificial Neural Network Model for the Comprehensive Study of the Solidification Defects During the Continuous Casting of Steel, Computer Communication & Collaboration, Vol. 6, No.1-2, pp.1- 14

      11. Wei Li , Xinchun Wang, Xusheng Wang, Hong Wang (2016), Endpoint Prediction of BOF Steelmaking based on BP Neural Network Combined with Improved PSO, Chemical Engineering Transactions, Vol.51, pp.475-480

      12. Seyed Taghi Akhavan Niaki and Babak Abbasi (2005), Fault Diagnosis in Multivariate Control Charts Using Artificial Neural Networks, Quality and Reliability Engineering · December 2005 DOI: 10.1002/qre.689, pp.1-31

      13. Dipak Laha , Ye Ren and P.N. Suganthan (2015), Modeling of steelmaking process with effective machine learnig Techniques, Expert Systems with Applications, Vol. 42, No.2015, pp. 4687 4696

      14. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, Vol.5, pp.115-133.

      15. Medhat H.A. Awadalla and M. Abdellatif Sadek (2012), Spiking neural network-based control chart pattern recognition,

        Alexandria Enginering Journal, Vol.51.pp.27-35

      16. Barghash, M.A. & Santarisi, N.S.(2004), Journal of Intelligent Manufacturing, Vol.15,

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      19. Mohammad Reza Maleki and Amirhossein Amiri (2015), Simultaneous Monitoring of Multivariate-Attribute Process Mean and Variability Using Artificial Neural Networks, Journal of Quality Engineering and Production Optimization Vol. 1, No. 1, pp. 43-54

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