Process Parameters Optimization Using Taguchi's Orthogonal Array and Grey Relational Analysis during Hard Turning of AISI D2 Steel in Forced Air-Cooled Condition

4th International Conference on Mechanical and Aeronautical Engineering

机械制造;航空航天工程

Rajbongshi, S.K.^1 ; Sarma, D.K.^1

National Institute of Technology Meghalaya, Shillong

793003, India^1

关键词: Environment friendly;    Grey relational analysis;    Grey relational grade;    Machining operations;    Optimization of process parameters;    Performance parameters;    Process parameters optimizations;    Taguchi's orthogonal arrays;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/491/1/012032/pdf
DOI  :  10.1088/1757-899X/491/1/012032

学科分类：航空航天科学

来源: IOP

【 摘 要 】

Optimization of process parameters play an important role in machining operations. The present work deals with a multi-objective optimization method (Grey relational analysis) and Taguchi's orthogonal array (OA) technique in machining of AISI D2 steel using coated carbide tool in forced air-cooled condition considering it to be environment friendly. The process parameters considered are cutting speed, feed and depth of cut having three levels each. The performance parameters are surface roughness, flank wear and cutting force. Taguchi's orthogonal array L9 is considered for the experimental design. Grey relational analysis technique is used for finding the optimal settings of the process parameters to obtain the value of surface roughness, flank wear and cutting force. The optimum grey relational grade has been found out to get the optimal setting of the experimental run. Then level wise grey relational grade has been found out from the experimental run for each parameter. The optimal settings based on the rank of the grey relational grade in the experimental run and level wise optimal settings of the parameters from the grey relational grade have been compared. The improvement of performance parameters is confirmed from the grey relational prediction analysis in comparison to optimal values in the experimental run.