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Volume 15, Issue 06 (June 2026)

Optimization of Cutting Parameters in Turning AISI 1020 MS by using Taguchi Method

DOI : 10.5281/zenodo.20759766
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Sandeep Yadav, Harsh Gupta, Vinay Maurya, Abhishek Kushwaha, Arpit Kumar, Adarsh Patel, Aman Dwivedi, 2026, Optimization of Cutting Parameters in Turning AISI 1020 MS by using Taguchi Method, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 15, Issue 06 , June – 2026

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Optimization of Cutting Parameters in Turning AISI 1020 MS by using Taguchi Method

Sandeep Yadav (1), Harsh Gupta (2), Vinay Maurya (3), Abhishek Kushwaha (4), Arpit Kumar (5), Adarsh Patel (6), Aman Dwivedi (7)

(1) Assistant professor, Mechanical Engineering Department, Axis Institute of Technology & Management, Kanpur, U.P., India

(2,3,4,5,6,7) Students, Mechanical Engineering Department, Axis Institute of Technology & Management, Kanpur, U.P., India

Abstract

In modern manufacturing, the selection of optimal cutting parameters is critical for improving product quality, maximizing production efficiency, and reducing machining costs. This investigation focuses on three fundamental machining variables cutting speed, feed rate, and depth of cutas the primary control factors.

The experimental results will be analysed using the Signal-to-Noise (S/N) ratio to identify the optimal parameter combination for achieving minimum surface roughness and maximum material removal rate. Furthermore, Analysis of Variance (ANOVA) will be performed to

determine the percentage contribution and statistical significance of each cutting parameter on the response variables.

Key Words: MRR, Cutting Parameters, ANOVA, Taguchi, signal to Noise Ratio (S/ N), L-9 orthogonal array

  1. Introduction

    1. Cutting Parameter Optimization

      In the highly competitive manufacturing landscape, achieving optimal machining performance will be essential for improving product quality, reducing cycle times, and minimizing production costs. Optimization of cutting parameters will be crucial for controlling key performance indicators such as surface roughness, material removal rate (MRR), tool wear, and cutting forces.

      Statistical techniques, such as the Taguchi method, will be employed to analyse multiple variables and their interactions with a drastically reduced number of experimental runs while maintaining statistical reliability.

      .

      Fig.1 Drive in a metal cutting

      In all metal removal operations, the primary variables are cutting speed, feed rate, and depth of cut (DOC). The geometric configuration of these parameters during the turning process is illustrated in Figure 2.

      Fig 2 Turning of metal [2]

      Contemporary manufacturing standards necessitate superior dimensional precision and high-quality surface finishes. Achieving such rigorous requirements through manual operations was exceptionally challenging, even for skilled technicians. Furthermore, manual processes are often time-intensive due to the constant adjustments required to avoid overcutting. The emergence of Computer Numerical Control (CNC) technology has revolutionized the industry by enabling automated machining with the flexibility to manage complex production tasks efficiently and will continue to play a vital role in future manufacturing advancements.

    2. Taguchi Design

      Genichi Taguchi, from Japans Nippon Telegraph and Telephone Corporation, introduced a robust statistical framework for product development known as the Taguchi Methods. This approach utilizes specialized experimental designs based on Orthogonal Arrayssuch as L9L_9L9, L18L{18}L18, L27L{27}L27, and L36L{36}L36to identify the ideal configurations for control parameters.

      Central to this methodology is the Signal-to-Noise (S/N) ratio, which functions as the objective function to minimize variability and achieve optimal performance. The procedural steps were illustrated in the flowchart and will be used to guide the experimental design and analysis in future studies shown in Figure 3.

      Fig 3 Flow Chart of Taguchi method

  2. Problem Statement

    There is a critical need for a systematic and scientifically robust approach to optimize cutting parameters. The Taguchi method provides an effective solution by employing statistical design of experiments to identify optimal parameter settings with minimal experimental effort. This study aims to address the existing challenges by applying Taguchi methodology to optimize machining parameters for turning AISI 1020 mild steel, thereby improving surface quality, enhancing tool life, and increasing productivity.

  3. Results and Discussion

    Factor A (Spindle speed)

    Factor B (Depth of Cut)

    Factor C (Feed)

    Level

    Value (rpm)

    Level

    Value (mm)

    Level

    Value (mm/rev)

    A1

    250

    B1

    0.5

    C1

    0.086

    A2

    400

    B2

    1.25

    C2

    0.093

    A3

    600

    B3

    2

    C3

    0.1128

    Table 3.1: Variable factor of Experiment

    Table 3.2: Column Assignment of L9 OA

    S.No.

    Speed(A) rpm

    Depth(B)mm

    Feed(C) mm/rev

    1

    1

    1

    1

    2

    1

    2

    2

    3

    1

    3

    3

    4

    2

    1

    2

    5

    2

    2

    3

    6

    2

    3

    1

    7

    3

    1

    3

    8

    3

    2

    1

    9

    3

    3

    2

    Table 3.3: Variance of Means For surface Roughness

    Factors

    DOF

    SS

    V

    F-RATIO

    P%

    Speed(A)

    2

    3.15352

    3.15352

    3.89

    20.50%

    DOC(B)

    2

    1.59945

    1.59945

    1.97

    33.60%

    Feed(C)

    2

    0.03573

    0.03573

    0.04

    95.8%

    Error

    2

    0.81101

    0.81101

      1. Experiment Table for a Calculate Surface Roughness

        Table 3.4: Experiment Data

        Trial No.

        Surface

        (m)

        Roughness

        Average (m)

        S/N ratio dB

        R1

        R2

        R3

        1

        4.590

        4.445

        5.610

        4.881

        -13.770

        2

        4.390

        3.656

        5.510

        4.518

        -13.098

        3

        5.970

        3.756

        7.490

        5.738

        -15.175

        4

        2.720

        3.221

        3.410

        3.117

        -9.8747

        5

        3.450

        4.090

        4.330

        3.956

        -11.945

        6

        3.850

        2.422

        4.950

        3.740

        -11.457

        7

        3.010

        3.390

        3.780

        3.393

        -10.611

        8

        3.850

        3.834

        4.850

        4.718

        -13.475

        9

        5.340

        2.973

        6.670

        4.994

        -13.968

        Fig 3.1 Main Effects Plot for Means for Surface Roughness

        Fig 3.2 SN Ratios for Surface Roughness

        Fig 3.3 Residual Plots for Surface Roughness

        Table 3.5: Result of the Confirmatory Experiment

        Variable Factors

        Surface roughness, (m)

        Average, (m)

        Range with 95% CL

        Spindle Speed, rpm (Factor A)

        Depth of Cut, mm (Factor B)

        Feed mm/rev (Factor C)

        400

        0.5

        0.093

        R1

        R2

        R3

        3.117

        1.388Ra(POP)3.329

        0.418 Ra (CE) 4.299

        2.720

        3.221

        3.410

        These parameters provided better machining performance and lower surface roughness.

      2. Manual Calculation of MRR in g/s:

        Formula Used:

        MRR = Mass Removed / Machining Time

        Sample Manual Calculation Given:

        • Initial weight = 1526 g

        • Final weight= 1500 g

        • Machining time= 180 sec

          Mass removed = 1526 1500 = 26 g

          MRR = 26 / 180

          MRR 0.144 g/s

      3. Experiment Table for a Calculate Material Removal Rate:

        Table 3.6: Variance of Means for Material Removal Rate

        Factors

        DOF

        SS

        V

        F-RATIO

        P%

        Speed(A)

        2

        0.10799

        0.05399

        3.16

        24.00%

        DOC(B)

        2

        0.05019

        0.02509

        1.47

        40.50%

        Feed(C)

        2

        0.04342

        0.02171

        1.27

        44.00%

        Error

        2

        0.03414

        0.01707

        Total

        8

        0.23573

        Table 3.7: Experiment Data

        SPEED(A)

        DOC(B)

        FEED(C)

        MRR(g/sec)

        S/N Ratio(n)

        1

        1

        1

        0.144

        -16.8327

        1

        2

        2

        0.1699

        -15.3961

        1

        3

        3

        0.185

        -14.6565

        2

        1

        2

        0.29629

        -10.5656

        2

        2

        3

        0.2620

        -11.614

        2

        3

        1

        0.7111

        -2.9626

        3

        1

        3

        0.3259

        -9.7383

        3

        2

        1

        0.388

        -8.2233

        3

        3

        2

        0.37037

        -8.6195

        Calculation of Material removal rate: Material removal rate (MRR) has been calculated from the difference of weight of work piece before and after experiment

        Control parameter and their values:

        • for low (speed, feed, depth of cut)

        • for medium (speed, feed, depth of cut)

        • for high (speed, feed, depth of cut)

    Table 3.8: Calculation of S/N Ratio

    MRR

    MRR2

    1/MRR2

    S/N Ratio(n)

    0.144

    0.020736

    48.22530

    -16.8327

    0.1699

    0.028866

    34.642832

    -15.3961

    0.185

    0.034225

    29.218407

    -14.6565

    0.29629

    0.087787

    11.391208

    -10.5656

    0.2626

    0.068958

    14.501580

    -11.6141

    0.711

    0.505521

    1.978157

    -2.9626

    0.3259

    0.106210

    9.4153092

    -9.7383

    0.388

    0.150544

    6.6425762

    -8.2233

    0.3707

    0.137418

    7.277067

    -8.6195

    The analysis of variance (ANOVA) is another optimizing tool mentioned in the factorial design method to further optimize above parameters as discussed in section.

    ANOVA is a statically based, objective decision-making tool for detecting any differences in average performance of groups of items tested. An ANOVA table consists of sum of squares, corresponding degree of freedom, the F-ratio corresponding to the ratios of two mean squares, and the contribution proportions from each of the control factors. These contribution proportions can be used to assess the importance of each factor for interested multiple performance characteristics (MPCs).

    Signal-to-noise ratio is also called as SNR or S/N, is defined as the ratio of signal power to the noise power corrupting the signal. The Signal to Noise Ratio (SNR) is the defining factor when it comes to quality of measurement. A high SNR guarantees clear acquisitions with low distortions and artifacts caused by noise. The better your SNR, the better the signal stands out, the better the quality of your signals, and the better you ability to get the results you desire.

    Table 3.9: Experiment Performed

    Trial No.

    Levels

    (SPEED)

    A(rpm)

    (DEPTH OF CUT) B(mm)

    (FEED)

    C(mm/rev)

    MRR(g/sec)

    1

    1

    1

    1

    0.144

    2

    1

    2

    2

    0.1699

    3

    1

    3

    3

    0.185

    4

    2

    1

    2

    0.2962

    5

    2

    2

    3

    0.2626

    Fig 3.4 Main Effects Plot for Means For MRR

    The graph shows that maximum value of material removal rate occurs at the level 2 of the experiment i.e. the optimum value is B2.

    Fig 3.5 SN Ratio for MRR

    Fig 3.6 Residual Plots for MRR

    Table 4.10: Result of Confirmatory Experiment

    6

    2

    3

    1

    0.711

    7

    3

    1

    3

    0.3259

    8

    3

    2

    1

    0.388

    9

    3

    3

    2

    0.3703

    Performance characteristics

    Optimal parameter level

    Predicted parameter level

    Material removal rate

    A2,B3,C1

    0.7111

    These parameters provided better machining performance and higher Material Removal Rate.

  4. CONCLUSIONS

Using the Taguchi method to optimize lathe machining of AISI 1020 is highly practical, as industries continuously seek to improve machining speed (higher MRR) without compromising surface quality or increasing tool wear.

This study successfully applied the Taguchi optimization method to investigate and improve CNC turning parameters for mild steel (AISI 1020).

Based on the Signal-to-Noise (S/N) ratio and ANOVA results, the following conclusions were drawn:

Surface Finish: Feed rate was identified as the most significant factor influencing surface roughness. A lower feed rate combined with a higher spindle speed was found to produce the best surface finish.

Productivity (MRR): Spindle speed and depth of cut were observed to be the most influential parameters in maximizing material removal rate (MRR).

Optimal Combination: The Taguchi L9 orthogonal array effectively determined an optimal combination of cutting speed, feed rate, and depth of cut, achieving a balance between high productivity and good surface quality.

Industrial Impact: Replacing trial-and-error machining with this statistically optimized parameter setting can significantly reduce machining time, minimize tool wear, and improve the dimensional accuracy of AISI 1020 components in industrial applications.

4. FUTURE WORK AND SCOPE

The future scope of this methodology involves enhancing its intelligence, improving sustainability, and ensuring seamless integration with modern digital workflows.

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