Application of grey relational analysis in high-speed machining of hardened AISI D6 steel

Abstract

This study reports an optimization of finish turning of hardened AISI D6 (60 HRC) cold work tool steel with ceramic and cubic boron nitride cutting tools using grey relational analysis. Optimization of the process parameters was performed using quality characteristics, i.e. tool wear, surface roughness, machining force and specific cutting force. Analysis of variance was used for observing the most influencing machining parameters on the quality characteristics. Evaluation of tool wear type and wear mechanism was done by tool makers' microscope and scanning electron microscope analysis. The optimal process parameters were calculated using grey relation grade and confirmation test was performed. Based on analysis of variance results, the feed rate is the most significant controllable machining factor for the finish hard turning of AISI D6 according to the quality characteristics. Flank and crater wears were the prominent wear types on the ceramic and cubic boron nitride inserts. Grey relational analysis was successfully applied on optimization of high-speed finish hard turning of hardened AISI D6 using multi-performance characteristics.

Investigation and modeling of cutting forces and surface roughness when hard turning of AISI 52100 steel with mixed ceramic tool: cutting conditions optimization

“…The results showed that the surface roughness was affected by feed rate for the three cutting tool types. The same effect of feed rate was detected by Kacal and Yildirim [3] during hard turning of AISI D6 cold work tool steel with ceramic and CBN inserts. They applied the gray relational analysis (GRA) to optimize the cutting conditions for surface roughness, cutting power, tool wear, and specific cutting force.…”

Surface Roughness Analysis for Economical Feasibility Study of Coated Ceramic Tool in Hard Turning Operation

“…Because surface roughness affects several functional attributes such as corrosion resistance, tribological characteristics, fatigue strength, and wear resistance of machined components, various researchers have employed methods which includes experimental, statistical, and analytical approaches in hard turning operation with various workpiece materials [18CrMo4, 42CrMo4, SEA8620, AISI 1040, 1045 for modeling and optimization using response surface methodology (RSM) (Elbah et al 2013;Hessainia et al 2013;Shihab et al 2014;Azam et al 2015;Meddour et al 2015;Bouzid et al 2015), Taguchi method (Gunay and Yucel 2013;Rashid et al 2016;Zerti et al 2016;Panda et al 2016;Das et al 2017a), ANN (Asiltürk and Çunkaş 2011;Pontes et al 2012;Asiltürk 2012;Mia and Dhar 2016), GRA (Sahoo and Sahoo 2013a;Kacal and Yildirim 2012;Senthilkumar et al 2014), GA (Batish et al 2014;Bouacha and Terrab 2016), and particle swarm optimization (PSO) (Stryczek and Pytlak 2014;Yue et al 2016) to attain the surface quality and dimensional finishing condition similar to costly cylindrical grinding. For example, Hessainia et al (2015) found that response surface methodology represents a powerful approach and can offer to scientific researchers as well industrial metal workers a helpful optimization procedure for various combinations of the workpiece and the cut material tool.…”

Optimal Performance of Caps’ Pressing Process Using Taguchi-Grey Method

“…Somashekhar et al (2011) conducted multi-objective optimization of micro wire electric discharge machining parameters using grey relational analysis with Taguchi method. Kacal & Yıldırım (2013) applied grey relational analysis in high-speed machining of hardened AISI D6 steel. This research, therefore, utilizes the grey relational analysis for optimizing the performance of the manufacturing process of bottle caps for multiple quality characteristics.…”