Multi-response optimization of Ti-6Al-4V turning operations using Taguchi-based grey relational analysis coupled with kernel principal component analysis

Abstract

Ti-6Al-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-6Al-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-6Al-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.

Parametric optimization while turning Ti-6Al-4V alloy in Mist-MQCL (Green environment) using the DEAR method

“…Published results for machining alloy under a dry environment, the surface roughness was high compared to current results for cutting speed, feed, and doc variation under MQCL. The mean of Ra varied between 1.0 and 2.4 microns in dry cut, whereas in MQCL 0.2 to 2.31 microns for higher speeds, that is a decrease of Ra values by 25% [24].…”

“…Optimization of milling parameters while machining Ti6Al4V alloy using 'Technique for Order Preference by Similarity to Ideal Solution' (TOPSIS) was explored [23]. GRA, coupled with PCA, 'principal component analysis,' is proposed to optimize radial force, power, and friction simultaneously [24].…”

“…The cutting tool employed is TiAlN coated via a physical vapor deposition method on carbide rhombic shaped, nose radius of 0.8 mm insert ('Kennametal MAKE CNMG120408M5 KC5010'). The coating selected for this study provides better heat and wear-resistant while machining difficult to cut metals while mist cooling [8,15,24,28]. One explanation given is the formation of a protective insulating layer, Al 2 O 3 is reported to form on the tool for TiAlN coated [29].…”

Experimental Analysis and Optimization of the Controllable Parameters in Turning of en Aw-2011 Alloy; Dry Machining and Alternative Cooling Techniques

“…To obtain favorable process output values such as radial thrust force, cutting power and coefficient of friction when dry turning of Ti-6Al-4V. Li et al [24] combined the Taguchi-based grey relational analysis and the kernel principal component analysis (KPCA) to optimize machining parameters such as type of inserts, feed rate and depth of cut.…”

Optimization of Operating Parameters of Tig Welding of Incoloy (800ht) through Response Surface Methodology

“…The weights calculated from the principal components showed the relative importance. Finally kernel grey relational grade was used as the optimization criterion to identify the optimal set of input parameters [12]. Suman Chatterjee et.al.…”