Rejection Analysis and Quality Control of Castings at Inducto Cast

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Rejection Analysis and Quality Control of Castings at Inducto Cast

Chintan Desai1, Kishan Garala2, Keval Doshi3, Yashodhar Mehta4, Ela Jha5

1,2,3,4,5G.H. Patel College of Engineering & Technology, Anand, Gujarat

AbstractCasting is a traditional process widely used in industries. Technological development has not yet been observed in this area. The process of casting generally leads to defects in the components produced. There are two ways to bring about improvement: either by applying quality control tools or by applying theoretical knowledge. Pareto Analysis is used to select the product which has maximum rejection and affects the revenue of the company. The defects in the product are analysed using Fish Bone diagram. Based on that, firstly, an optimum moulding sand composition is obtained by Taguchi method. Secondly an optimum gating system design for the product is achieved by Taguchi method. Various iterations are performed in E-foundry software. The optimum design is further simulated on AutoCast software to provide assurance

KeywordsCasting,Taguchi,Pareto,E-foundry

  1. INTRODUCTION

    Inducto Cast is a sand casting industry that manufactures components made of either cast iron or spheroidal graphite cast iron. The objective is to identify the main problems incurred in the company during the manufacturing of various products. And then solving the problems by carrying out the rejection analysis and applying different quality improvement tools and techniques. The main target is to reduce the rejection of the product and to increases productivity by different productivity improvement tools and technique.

    Fig. 1 Casting process flow chart

  2. PRODUCT SELECTION
    1. Pareto Analysis

      Pareto Analysis is a statistical technique in decision making that is used for the selection of a limited number of tasks that produce significant overall effect. It uses the Pareto Principle (also known as the 80/20 rule) the idea that by doing 20% of the work we can generate 80% of benefit of doing the whole job. Or in terms of quality improvement, a large majority of problems (80%) are produced by a few key causes (20%). Out of various products, one product has been identified.

      Table 1 Rejection Data for March-June 2017

      Fig. 2 Graph of Percentage Rejection vs Part Name

      Fig. 3 Pareto chart for product selection

      From pareto chart S2K3-22 is selected for further analysis.

    2. Fish Bone Diagram

      Fish-Bone diagram is a tool that is useful for identifying and organising the known or possible causes of quality, or lack of it. One of the important benefits of contracting it is it helps to determine the root causes of a problem or quality characteristic using a structured approach. Fish-Bone diagram can be used to figure out various causes related to defects incurred in casting. The causes can be further segregated into four different categories:

      • Man
      • Material
      • Method
      • Machine

        . Various Defects commonly incurred during the process for which the Fish-Bone diagram is formulated are as follows:

      • Blow hole
      • Cold Shut
      • Sand Inclusion

    Fig. 3 Fish bone diagram for cold shut

    Fig. 6 Fish bone diagram for sand inclusion

    As observed from the Fish-Bone Diagram for various defects, the significant causes for defects incurred in casting are Improper Sand Composition and Improper Gating design. So, bringing about a variation in the sand composition and preparing a new gating system design would be the main motive to reduce the defects.

  3. SAND TESTING
    1. Grain Size Test

      Fig. 2 Fish bone diagram for blow holes

      The grain size and distribution influence many sand properties such as permeability, Flowability, refractoriness, surface fineness and strength. The finer the sand grains, the finer is the molding sand as whole. Fine grain sands give good surface finish but possess low permeability. For same clay content, the green strength is higher in case of fine sands as compared to coarse sands. Coarse and uniformly graded sand imparts high permeability, good refractoriness and high flowability. Normally the foundry sand possesses the grain size between 0.1 to 1.0 mm. The fine-grained sands are used to make intricate and small size castings. Coarse grained sands are used to make large castings. (Refer Appendix 2). 100 grams of sand is taken for the test.

      Sieve No

      .

      Sand retained on each sieve, gPercentage of sand retained, (A)Multi plier (B)Product (A*B)
      2704630.672006134
      2007449.331406906.2
      Pan2617.333005199
      TOTAL14697.3318239.2
      Sieve No

      .

      Sand retained on each sieve, gPercentage of sand retained, (A)Multi plier (B)Product (A*B)
      2704630.672006134
      2007449.331406906.2
      Pan2617.333005199
      TOTAL14697.3318239.2

       

      SampleHardnessAverage Hardness
      1.6576.5
      2.72
      3.79
      4.90
      SampleHardnessAverage Hardness
      1.6576.5
      2.72
      3.79
      4.90

       

      C. Sand Hardness Test

      Table 2 Multiplying Factor for Mesh opening

      AFS Grain Fineness number = (Total Product) / (Total Percent of Sand Retained)

      = 18239.2

      97.33

      = 187.40

      The achieved GFN being comparatively higher is suitable for smaller castings with good surface finish and high flowability. However smaller sizes are not permeable enough. Molds made of very fine-grained sands will be closely packed and have little space between the sand grains that wouldnt allow entrapped air and gases to escape leading to porosity defects. Hence, to obtain sound casting without porosity defect GFN value should lie between certain ranges.

    2. Permeablity Test

    The permeability number of the sand sample can be calculated from the following equation:

    Calculation

    Where,

    P=AFS standard permeability number V=Volume of air in cm3 =2000 cm3

    H=Height of specimen in cm=5.08(or, 2 inches) A=Cross-sectional area of specimen in cm2= 20.268 cm2 p=Air pressure in g/cm2 = 10 g/cm2

    t=Time in minutes

    The above equation can be simplified as

    Where, t = time in seconds=35 seconds

    P = 85.92

    For th given composition of sand, permeability lies in the range for heavy grey iron. As such it will allow entrapped air and generated gases to escape through mould. And hence it is not the governing cause for defects such as blow holes.

    Table 3 Observation Table for Hardness number

    A hard rammed mold generally reads 90, while a soft mold reads 50 to 60. The average value for the sample obtained is

    76.5 which is above 50 and sufficient to retain the sand in normal pouring conditions. Hence hardness is not the affecting parameter for severe penetration by the liquid metal and washing of the sand.

    1. Optimum Sand Composition using Taguchi Analysis

      The following factors were used to perform sand testing to find out the optimal composition:

      • Bentonite content
      • Moisture content
    FactorsLevel 1Level 2Level 3Level 4
    Moisture content2%3%4%5%
    Bentonite content2%3%4%5%

    Table 4 Factor table for optimum sand composition

    Based on the Taguchi method applied for the sand testing, we found out the optimum composition of Sand mixture.

    Moist

    ure TConte hnt

    e

    Bento nite conte ntDry Compr essive strengt h(psi)Green Compr essive strengt h (psi)SNRA1Mean
    s 2% u

    b

    2%652.811.9433.9
    s 2% c3%714.516.0537.75
    r 2% i4%756.519.2340.75
    p2% t5%688.421.4338.2
    f 3% o2%862.510.9644.25
    r 3%3%923.914.8247.95
    t 3% h4%965.417.6450.7
    e3%

    p

    5%1017.220.1354.1
    e4% r2%9929.02950.5
    m4% e3%1063.513.8854.75
    a4% b4%1135.117.1559.05
    i 4% l

    i

    5%1206.519.2563.25
    t 5% y2%10529.0253.5
    5%

    o

    3%1193.313.3761.15
    f 5%

    T

    4%1464.215.4775.1
    p% e5%1525.517.8178.75
    Moist

    ure TConte hnt

    e

    Bento nite conte ntDry Compr essive strengt h(psi)Green Compr essive strengt h (psi)SNRA1Mean
    s 2% u

    b

    2%652.811.9433.9
    s 2% c3%714.516.0537.75
    r 2% i4%756.519.2340.75
    p2% t5%688.421.4338.2
    f 3% o2%862.510.9644.25
    r 3%3%923.914.8247.95
    t 3% h4%965.417.6450.7
    e3%

    p

    5%1017.220.1354.1
    e4% r2%9929.02950.5
    m4% e3%1063.513.8854.75
    a4% b4%1135.117.1559.05
    i 4% l

    i

    5%1206.519.2563.25
    t 5% y2%10529.0253.5
    5%

    o

    3%1193.313.3761.15
    f 5%

    T

    4%1464.215.4775.1
    p% e5%1525.517.8178.75

     

    Fig. 4 Main effects plot for mean

  4. GATING SYSTEM DESIGN
    1. Feeder Design

      Calculation of pouring time based on experimental rules:-

      t = SW seconds

      where W : weight of metal poured = 932.55 pound S : 2.20 for thickness more than 15mm

      T = (2.20) x (932.55)

      = 67.1seconds

      1. Calculation of Pouring rate:-

        Assume initial pouring rate = (1.5)*(average pouring rate) To calculate the optimum pouring rate for different metals, following method can be used:

        For ferrous metals:-

        W : weight of casting = 423kg

        t : critical casting thickness = 10mm P = 0.5 for weight [0,500]

        The plots generated by Minitab software is as follows:

        Here R is obtained without consideration of fluidity and friction. Hence, flow rate has to be corrected for metal fluidity and the effect of friction in the gating system. The

        adjusted pouring rate can be calculated as:- Ra

        For liquid cast iron, take coefficient of friction (f) = 0.15

        Ra = 5.104kg/s

      2. Average filling rate:-

        Average filling rate =

        = = 6.30kg/s

        Fig. 7 Main effects plot for SN ratio

      3. Velocity of flow: –

        For Fe based alloy, taking velocity as 500mm/s

      4. Effective metal head of casting: –

        hp : effective metal head of casting H : height of sprue = 230mm

        p : height of casting in cope = 290mm p : height of casting = 590mm

        cm-1

        Assuming height of riser to be equal to height of sprue:- hs : height of sprue = 230mm

        hr : height of riser = 230mm Volume of riser :

      5. Choke Area:-

        = 158.728mm

        Area of riser :

        Vr = rr2h

        = rr2 (230)

        = 72.22 rr2 cm3

        Ar = 2rr2 + 2rrh

        = 6.28 rr2 + 144rr

        A1 : choke area

        W : casting weight

        : density of molten metal = 7000kg/m3 hp : effective height of metal head

        c : discharge co-efficient

        g : gravitational acceleration = 9.8m/s2 t : pouring time

        Now,

        Let a = 0.33

        b = 0030

        c = 1.00

        x = =

        x = + c

        = 6.3936 x 10-4 m2

      6. Sprue exit area:-

        = 28.53mm

        Q = A1V1

        rr = 7.955 cm

        Vr = 72.22 (7.955)2 = 4570 cm3

        5.104 = 7000 * (6.3936 x 10-4) * V1

        V1 = 1.140m/s = 1140mm/s Also, V2 = 500mm/s

        Using continuity equation. A1V1 = A2V2 (6.3936 x 10-4) * 1140 = A2 * 500

        A2 = 1.45 x 10-3m2

      7. Gate Thickness

    To avoid hot junction,

    Gate Modulus = (local casting modulus)

    = (wall thickness)

    = (70)

    = 35mm

    Riser is design by Caines Method as follows:

    1. Caines Method Vc = 0.02443 x 106 cm3 Ac = 2.6394 x 104 cm2

      Taking number of risers = 4

      Volume of each riser :

      V =

      = 1142.5 cm3

      2

      2

       

      V = r1 h

      2

      2

       

      = r1 (23)

      r1 = 3.98cm Diameter of riser = 79.5mm

    2. Modulus Method Vc = 0.02443 x 106 cm3 Ac = 2.6394 x 104 cm2

      cm-1

      Assuming height of riser to be equal to height of sprue: – Mc : modulus of casting

      Mr : modulus of riser

      D : diameter of riser (cm)

      Mc =

      =

      = 0.926

      Now,

      Mr = 0.2D

      but

      Mr = 0.2 Mc D = 6 Mc

      = (6 * 0.926)

      = 5.55cm

      solution. The following factors were used to find the parameters for optimum gating system design:

      • Number of Risers
      • Diameter of Riser
      • Pouring Temperature
    • The factor table is given below:
    FactorsLevel 1Level 2Level 3
    Number of risers346
    Diameter of riser (mm)55.679.5102
    Pouring temperature (oC)125013501450
    FactorsLevel 1Level 2Level 3
    Number of risers346
    Diameter of riser (mm)55.679.5102
    Pouring temperature (oC)125013501450

     

    The diameter of riser obtained from various methods is as follows:

    From Caines method:79.5mm From Modulus mehod:55.5mm Current riser diameter:102mm

  5. ANALYSIS AND SIMULATION

Casting simulation is a powerful tool which can be used to visualize progressive solidification of molten metal inside a mold cavity. It helps to identify the defects like hot spots, cold shut, and shrinkage cavity. The defect is generally eliminated by connecting a feeder which is designed to solidify later than the hot spot. The defect can also be eliminated by changing the design of the gating system.

Fig. 5 Thermal analysis of current gating system

Total feeder volume = 5.495 x 106 mm3 Sprue upper diameter D = 45 mm Sprue lower diameter D = 35 mm Height of sprue = 230 mm

Number of risers = 4 Diameter of risers = 50 mm Casting Yield = 81.63%

  1. Analysis using Taguchi Method

    Taguchi Method is a systematic process which uses orthogonal array to choose the optimum iteration. As such it eliminates the chances of missing out the most optimal

    Table 7 Factor table for casting simulation

    Number of risersDiameter of risers (mm)Pouring temperature (oC)
    355.61250
    379.51350
    31021450
    455.61350
    479.51450
    41021250
    655.61450
    679.51250
    61021350

    Table 8 Different combinations posssibe using three parameters (L9 matrix)

    Num ber of risersDiam eter of risers (mm)Pouri ng temp (oC)Castin g yield (%)Sou nd casti ngSnra 1Mean
    355.6125081.0757.536.4369.29
    379.5135078.8763.436.8971.14
    3102145075.1210038.5887.56
    455.6135089.1264.637.3876.86
    479.5145088.2310039.4294.12
    4102125082.5672.937.7677.73
    655.6145080.0564.837.0572.43
    679.5125077.4069.437.2873.40
    6102135072.9374.837.3773.87

    Table 9 Finalized iteration specification of gating system

    Fig. 10 Thermal analysis of proposed gating system iteration

    The output parameter which were considered to find the optimum gating design are:

    Casting Yield: It should be as high as possible because a higher value would indicate a significant material savings which would further result into cost savings.

    Sound Casting: Sound casting indicates the tendency to generate the hotspots. Higher the value lower is the tendency to generate the hotspot. The simulation was carried out on E- foundry software where the hotspots generated were observed using a thermal plot.

    Mean of both the parameters was taken into account for carrying out the Taguchi analysis. Based on the observations made, the iteration having the maximum Signal to noise ratio was selected as optimum iteration.

    Fig. 11 Main effects plot for SN ratio

    Fig. 12 Main effect plot for mean

    Based on Taguchi Method, the optimum parameters for riser design are:

    FactorOptimum value
    Number of risers4
    Diameter of risers79.5mm
    Pouring temperature1450oC
  2. Validation of selected casting parameter using Auto cast software

    Fig. 13 Schematic diagram of proposed casting system

    Iteration with 4 number of risers, diameter of riser 79.5 mm, and pouring temperature 1450oc is selected and validated in AutoCast. To have low heat rejection in the riser we have introduced blind risers with insulating cap on top of each. That will lessen the amount of metal needed in riser compared to through risers. Proposed gating system and components are as shown below.

    • Shrinkage Porosity: Captured in Gating system
    • Hard Zones: Occurred in Gating system
    • Unfilling (Misrun): Inherently absent
    • Cold Shut: Inherently absent

      p>

      From the detailed simulation of major casting defects present, it is found that misrun or unfilling and cold shut is inherently absent.The hard zone formed due to quenching effect because

      Current Time = 57.31sCurrent Time = 151.74sCurrent Time = 225.21s
      Volume Solidified= 5.30%Volume Solidified= 30.20%Volume Solidified= 45.04%
      Current Time = 429.09sCurrent Time = 560.02sCurrent Time = 852.5s
      Volume Solidified= 70.07%Volume Solidified= 80.01%Volume Solidified= 95.01%
      Current Time = 57.31sCurrent Time = 151.74sCurrent Time = 225.21s
      Volume Solidified= 5.30%Volume Solidified= 30.20%Volume Solidified= 45.04%
      Current Time = 429.09sCurrent Time = 560.02sCurrent Time = 852.5s
      Volume Solidified= 70.07%Volume Solidified= 80.01%Volume Solidified= 95.01%

       

      of convective heat transfer in surrounding air is trapped in the gating system.As shown in the figure given below no traces of hardzones were found in actual casting. Shrinkage porosity is not present as the all the hot spots were trapped in mainly three location. i.e. top two risers and gating system as shown in figure.

      Fig. 14 Hard zones in proposed casting

      Fig. 15 Shrinkage porosity in proposed casting

  3. Solidification time and temperature gradient

    Solidification starts from the region having higher surface area to volume ratio and ends where the ratio is minimum. Hence as it is observed from the pictures at different time frame, solidification will start from gating system, will progress to the main body and ultimately last solidification will takes place at the risers where shrinksges will be trapped. The solidification time as per the software is 24.62 min.

    Fig. 16 Volume solidification at different interval of time

  4. Advantage of proposed riser placement over current riser riser placementt

    When riser placed as per current system:

    TC2: Riser solidifies at the time of pouring When blind risers placed as proposed system:

    TC1: Risers solidifies at last

    Fig. 17 Temperature cross section in proposed casting

    As shown in temperature profile cross-section it is evident that higher most temperature (yellow and orange) is at opposite to the sprue while lower temperature is nearer to the sprue. Hence from theoratical knowledge of placement of riser, the best possible location of placing riser is at the opposite to the location of sprue. While the current location of riser is nearer to sprue where it tends to solidify earliest.

    The graph plotted of temperature vs time depicts the difference between temperature of riser placed at location 1 (Proposed location) and location 2 (Current location) with the time of solidification

    Fig. 18 Solidification time vs. Temperature plot at two location of riser placement

  5. Total savings per annum using the proposed design

Tangible benefits in terms of cost incurred:

  1. Material saved in terms of yield:
    ParametersCurrent CastingProposed Casting
    Yield(%)81.6388.23
    Weight of the component(kg)227227
    Weight of the gating system(kg)51.0831.28
    Total Weight (kg)278.08258.28
    Material cost(Rs.30/kg for CI)8342.407748.40
    Processing cost(Rs.24/kg for CI)6673.926193.92
    Total cost(Rs.)15016.3213942.32
    Number of components produced annually5151
    Cost incurred annually(Rs.)7,65,832.327,11,058.32
  2. Rejection reduction due to removal of the defects:-
ParametersCurrent Casting
Number of rejected components produced annually51
Weight of the component(kg)227
Material cost(Rs.30/kg of CI)6810
Processing cost(Rs.24/kg for CI)5448
Total cost(Rs.)12258
Number of components rejected per annum9
Cost incurred annually due to rejected component(Rs)1,10,322

Total Savings Per Annum = (Savings due to improving Yield)

+ (Savings by removing the defects)

= 54,774 + 1,10,322

= Rs.1,65,096

V REFERENCEN

  1. Defect Minimization in Casting through Process Improvement- A Literature Review
  2. Advanced Techniques in Casting Defects and Rejection Analysis: A Study in an Industry (IJIERT, ISSN: 2394-3696, Volume 2, Issue 9, SEP.-2015)
  3. An Application of Pareto Analysis and cause effect Diagram for Minimization of Defects in Manual Casting Process (IJMAPE, ISSN: 2320-2092, Volume-2, Issue-2, Feb.-2014)
  4. Defects Analysis Using E foundry and Flow Simulation for Crank Case Casting (Kalpa Publication in engineering, Volume 1, 2017, ICRISET2017)
  5. Reducing Rejection Rate of Casting Using Simulation Model. (IJIRSET, ISSN (Print): 2347-6710, volume 2, Issue 1, December 2013)
  6. Defects, Causes, and their Remedies in Casting Process A Review (IJRAT, Vol 2, No 3, March 2014)
  7. Optimization of green sand casting process parameters of a foundry by using Taguchis method -Sushil Kumar
  8. efoundry.iitb.ac.in
  9. Principles of Metal Casting – Richard Heine, Carl Loper, Philip Rosenthal
  10. IOSR Journal of Mechanical and Civil Engineering (IOSR- JMCE) [e-ISSN: 2278-1684, p-ISSN: 2320-334X, Volume 14, Issue 2 Ver. I (Mar. – Apr. 2017), PP 09-13, www.iosrjournals.org]
  11. Pareto Analysis of Critical Success Factors for Total Quality Management Targeting the Service Industry (IJCA Volume 121 No.14, July 2015)
  12. A study of the permeability of sand (Published by The University, Iowa City)
  13. Defects Analysis Using E foundry and Flow Simulation for Crank Case Casting (Kalpa Publication in engineering, Volume 1, 2017, ICRISET2017)
  14. Foundry Gating System (United States Patent, US4907640, Mar. 13, 1990)
  15. Design and Analysis of Riser for Sand Casting (IJSRTM, Volume 1(2), April 2013)
  16. Feeder Sprue System for a Casting Mold (United States Patent, US4913218, Apr 3 1990)

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