Investigating the effects of electric discharge machining parameters on material removal rate and surface roughness on AISI D2 steel using RSM-GRA integrated approach

The present research focuses on optimizing the process parameters of die-sinking electric discharge machining on tool steel. The basic objective of this research is to investigate the influence of two categorical factors including dielectric type and electrode polarity, and two numeric factors inclu...

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Published inInternational journal of advanced manufacturing technology Vol. 101; no. 5-8; pp. 1255 - 1265
Main Authors Hanif, Muhammad, Ahmad, Wasim, Hussain, Salman, Jahanzaib, Mirza, Shah, Abdul Hakim
Format Journal Article
LanguageEnglish
Published London Springer London 01.04.2019
Springer Nature B.V
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Abstract The present research focuses on optimizing the process parameters of die-sinking electric discharge machining on tool steel. The basic objective of this research is to investigate the influence of two categorical factors including dielectric type and electrode polarity, and two numeric factors including discharge current, and Spark/Discharge Gap on material removal rate (MRR) and surface roughness ( R a ) for machining of AISI D2 steel. Box-Bhenken design based on response surface methodology (RSM) was applied for experimental design. For estimation and evaluation, the effects of the process parameters on response variables, RSM has been integrated with grey relational analysis (GRA). Ranking of factors has been done with respect to the grey relational grade. (ANOVA) was further performed for determining the significance of grey relational grade. ANOVA results reveal that that polarity having 50% of percentage contribution was the most significant factor affecting the performance measures followed by the spark gap, discharge current, and dielectric type. The grey relational grades were further optimized through desirability function and the optimal condition for input parameters was obtained. The optimum levels were discharge current at 15 A, dielectric type of kerosene oil, spark gap at 6 mm, and polarity of positive has been determined. The confirmatory tests were run for verifying and validating the results and improvement in productivity (MRR) up to 17.23 mm 3 /min and quality ( R a ) up to 3.86 μm at an optimum have been observed.
AbstractList The present research focuses on optimizing the process parameters of die-sinking electric discharge machining on tool steel. The basic objective of this research is to investigate the influence of two categorical factors including dielectric type and electrode polarity, and two numeric factors including discharge current, and Spark/Discharge Gap on material removal rate (MRR) and surface roughness ( R a ) for machining of AISI D2 steel. Box-Bhenken design based on response surface methodology (RSM) was applied for experimental design. For estimation and evaluation, the effects of the process parameters on response variables, RSM has been integrated with grey relational analysis (GRA). Ranking of factors has been done with respect to the grey relational grade. (ANOVA) was further performed for determining the significance of grey relational grade. ANOVA results reveal that that polarity having 50% of percentage contribution was the most significant factor affecting the performance measures followed by the spark gap, discharge current, and dielectric type. The grey relational grades were further optimized through desirability function and the optimal condition for input parameters was obtained. The optimum levels were discharge current at 15 A, dielectric type of kerosene oil, spark gap at 6 mm, and polarity of positive has been determined. The confirmatory tests were run for verifying and validating the results and improvement in productivity (MRR) up to 17.23 mm 3 /min and quality ( R a ) up to 3.86 μm at an optimum have been observed.
The present research focuses on optimizing the process parameters of die-sinking electric discharge machining on tool steel. The basic objective of this research is to investigate the influence of two categorical factors including dielectric type and electrode polarity, and two numeric factors including discharge current, and Spark/Discharge Gap on material removal rate (MRR) and surface roughness (Ra) for machining of AISI D2 steel. Box-Bhenken design based on response surface methodology (RSM) was applied for experimental design. For estimation and evaluation, the effects of the process parameters on response variables, RSM has been integrated with grey relational analysis (GRA). Ranking of factors has been done with respect to the grey relational grade. (ANOVA) was further performed for determining the significance of grey relational grade. ANOVA results reveal that that polarity having 50% of percentage contribution was the most significant factor affecting the performance measures followed by the spark gap, discharge current, and dielectric type. The grey relational grades were further optimized through desirability function and the optimal condition for input parameters was obtained. The optimum levels were discharge current at 15 A, dielectric type of kerosene oil, spark gap at 6 mm, and polarity of positive has been determined. The confirmatory tests were run for verifying and validating the results and improvement in productivity (MRR) up to 17.23 mm3/min and quality (Ra) up to 3.86 μm at an optimum have been observed.
Author Shah, Abdul Hakim
Jahanzaib, Mirza
Hanif, Muhammad
Ahmad, Wasim
Hussain, Salman
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Issue 5-8
Keywords Surface roughness
Die-sinking EDM
Material removal rate
AISI D2 steel
RSM
GRA
Language English
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PublicationTitleAbbrev Int J Adv Manuf Technol
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Springer Nature B.V
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Snippet The present research focuses on optimizing the process parameters of die-sinking electric discharge machining on tool steel. The basic objective of this...
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SubjectTerms CAE) and Design
Chromium molybdenum vanadium steels
Computer-Aided Engineering (CAD
Design of experiments
Die sinking
Dielectrics
Electric discharge machining
Engineering
Industrial and Production Engineering
Integrated approach
Kerosene
Material removal rate (machining)
Mechanical Engineering
Media Management
Optimization
Original Article
Polarity
Process parameters
Process planning
Response surface methodology
Surface roughness
Tool steels
Variance analysis
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Title Investigating the effects of electric discharge machining parameters on material removal rate and surface roughness on AISI D2 steel using RSM-GRA integrated approach
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