Multi-response optimization of Ti-6A1-4V turning operations using Taguchi-based grey relational analysis coupled with kernel principal component analysis
作者机构：School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan 430074 People's Republic of China
出 版 物：《Advances in Manufacturing》 (先进制造进展（英文版）)
年 卷 期：2019年第7卷第2期
页 面：142-154页
核心收录：
学科分类：08[工学]
基 金：from the National Science and Technology Major Project of China
主 题：Ti-6A1-4V Taguchi method Grey relational analysis (GRA) Kernel principal component analysis (KPCA) Multi-response optimization
摘 要：Ti-6A1-4V has a wide range of applications, especially in the aerospace field;however, it is a difficultto- cut material. In order to achieve sustainable machining of Ti?6A1-4V, multiple objectives considering not only economic and technical requirements but also the environmental requirement need to be optimized simultaneously. In this work, the optimization design of process parameters such as type of inserts, feed rate, and depth of cut for Ti-6A1-4V turning under dry condition was investigated experimentally. The major performance indexes chosen to evaluate this sustainable process were radial thrust, cutting power, and coefficient of friction at the toolchip interface. Considering the nonlinearity between the various objectives, grey relational analysis (GRA) was first performed to transform these indexes into the corresponding grey relational coefficients, and then kernel principal component analysis (KPCA) was applied to extract the kernel principal components and determine the corresponding weights which showed their relative importance. Eventually, kernel grey relational grade (KGRG) was proposed as the optimization criterion to identify the optimal combination of process parameters. The results of the range analysis show that the depth of cut has the most significant effect, followed by the feed rate and type of inserts. Confirmation tests clearly show that the modified method combining GRA with KPCA outperforms the traditional GRA method with equal weights and the hybrid method based on GRA and PCA.
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