Multi-response optimization of laser-assisted jet electrochemical machining parameters based on gray relational analysis

Author(s):  
Anup Malik ◽  
Alakesh Manna
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anup Malik ◽  
Neel Sanghvi

Purpose The purpose of this paper is to optimize the laser-assisted jet electrochemical machining parameters, namely, supply voltage, inter-electrode gap, duty cycle and electrolyte concentration during machining of WC-Co composite using grey relational analysis and fuzzy logic. Design/methodology/approach In this work, experiments were carried out as per the Taguchi methodology and an L16 orthogonal array was used to study the influence of various combinations of process parameters on material removal rate, hole taper angle and surface roughness height. As a dynamic approach, the multiple response optimization was carried out using grey relational analysis and fuzzy logic. Findings The process parameters were optimized using grey relational analysis and fuzzy logic for different machining conditions such as balanced manufacturing, high-speed manufacturing and high-quality manufacturing. The research documented in this paper can be scaled up for case studies regarding industrial applications to compare optimization methods for manufacturing processes that are already being carried out. Originality/value An attempt to optimize material removal rate, hole taper angle and surface roughness height together by a combined approach of grey relational analysis and fuzzy logic has not been previously done.


2018 ◽  
Vol 7 (2.32) ◽  
pp. 143
Author(s):  
Thella Babu Rao ◽  
Venu Pilli ◽  
Nallamotu Revanth Sai Venkat ◽  
Nagandla Pavan ◽  
Thalari Shiva Ram

This paper presents optimization of plasma heat assisted turning process for machining hardened EN24 die steel (53HRC) by using gray relational analysis. Flank wear and surface roughness (Ra) are experimentally measured as the process performance characteristics under varying conditions of preheating temperature, cutting speed and cutting length. The plasma heating approach was implemented to preheat the workpiece. The machining experiments were conducted according to the L16 design of experiments. Since the chosen machining performance indicators are found with confliction for the chosen process variables, the problem is treated as multi-response optimization problem to minimize the tool wear and surface roughness simultaneously. Therefore, the problem was solved by implementing the gray relational analysis and the derived optimal machining conditions were analysed and reported.  


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