Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis

Multi-response optimization of minimum quantity lubrication parameters using Taguchi-based grey relational analysis in turning of difficult-to-cut alloy Haynes 25

This paper presents an effective approach for the optimization of laser cutting process of St-37 steel with multiple performance characteristics based on the grey relational analysis. Sixteen experimental runs based on the Taguchi method of orthogonal arrays were performed to determine the best factor level condition. The response table and response graph for each level of the machining parameters were obtained from the grey relational grade. In this study, the laser cutting parameters such as laser power and cutting speed are optimized with consideration of multiple-performance characteristics, such as workpiece surface roughness, top kerf width and width of heat affected zone (HAZ). By analyzing the grey relational grade, it is observed that the laser power has more effect on responses rather than cutting speed. It is clearly shown that the above performance characteristics in laser cutting process can be improved effectively through this approach.

Use of the grey relational analysis to determine optimum laser cutting parameters with multi-performance characteristics

The optimal parameters process of plasma arc welding (PAW) by the Taguchi method with Grey relational analysis is studied. The Grey relational grade is used to find optimal PAW parameters with multiple response performance characteristics. The welding parameters (welding current, welding speed, plasma gas flow rate, and torch stand-off) are optimized with consideration of the multiple response performance characteristics (the penetration of root, the weld groove width, and the weld pool undercut). As a result, the improvement percentage of the Grey relational grade with the multiple performance characteristics is 31.8%. It is shown that the multiple response performance characteristics are greatly improved through this study.

Optimization of Plasma Arc Welding Parameters by Using the Taguchi Method with the Grey Relational Analysis

Glass fiber–reinforced polymer (GFRP) composite materials are an economic alternative to the engineering materials because of their superior properties. The present work focuses on the use of Grey relational analysis for optimizing the drilling parameters on the surface roughness and the thrust force in the drilling of GFRP composites. Taguchi's L9, 3-level orthogonal array is used for the experimentation. Optimal machining parameters are determined by the Grey relational grade obtained from the Grey relational analysis for multiperformance characteristics. Experimental results show that the machining performance in the composite machining process can be improved at optimal drilling conditions.

Analysis on Drilling of Glass Fiber–Reinforced Polymer (GFRP) Composites Using Grey Relational Analysis

This article addresses an approach based on the Taguchi method with grey relational analysis for optimizing turning operations with multiple performance characteristics. A grey relational grade obtained from the grey relational analysis is used to solve the turning operations with multiple performance characteristics. Optimal cutting parameters can then be determined by the Taguchi method using the grey relational grade as the performance index. Tool life, cutting force, and surface roughness are important characteristics in turning. Using these characteristics, the cutting parameters, including cutting speed, feed rate, and depth of cut are optimized in the study. Experimental results have been improved through this approach.

C. L. Lin

Papers

Use of the Taguchi Method and Grey Relational Analysis to Optimize Turning Operations with Multiple Performance Characteristics