Multi-objective optimization for sustainable turning Ti6Al4V alloy using grey relational analysis (GRA) based on analytic hierarchy process (AHP)

Abstract

Sustainable machining necessitates energy efficient processes, longer tool lifespan and greater surface integrity of the products in modern manufacturing. However, when considering Ti-6Al-4V alloy these objectives turn out to be difficult to achieve as titanium alloys pose serious machinability challenges, especially at elevated temperatures. In this research, we investigate the optimal machining parameters required for turning of Ti-6Al-4V alloy. Turning experiments were performed to optimize four response parameters i.e. specific cutting energy (SCE), wear rate (R), surface roughness (R a ) and material removal rate (MRR) with uncoated H13 carbide inserts in the dry cutting environment. Grey relational analysis (GRA) combined with the analytic hierarchy process (AHP) was performed to develop a multi-objective function. Response surface optimization was used to optimize the developed multi-objective function and determine the optimal cutting condition. As per the ANOVA, the interaction of feed rate and cutting speed (f × V) was found to be the most significant factor influencing the grey relational grade (GRG) of the multiobjective function. The optimized machining conditions increased the MRR and tool life by 34 % and 7 %, whereas, reducing the specific cutting energy and surface roughness by 6 % and 2 % respectively. Using Taguchi based GRA by analytic hierarchy process (AHP) weights method, the benefits of high-speed machining Ti6Al4V through multiresponse optimization were achieved.

The Analysis of Key Technologies for Sustainable Machine Tools Design

“…There are other studies on machining process optimization to enhance MT use efficiency, which select four indexes, i.e., specific cutting energy, wear rate, surface roughness, and material removal rate [110,111]. The optimized objects of a sustainable machining process focus on cutting parameters of the condition of specific machine tools, such as spindle speed, cutting depth and feed rate for turning process.…”

Understanding Risk Assessment in the Context of Fractional Ownership using Ethereum Smart Contract

“…Decision-makers' comparisons of potentially tricky criteria and alternatives [31]. ELECTRE No weighting of the criteria but no criteria can be preponderant or negligible in relation to the others.Use fuzzy logic [32] .…”

Statistical analysis of energy consumption, tool wear and surface roughness in machining of Titanium alloy (Ti-6Al-4V) under dry, wet and cryogenic conditions

“…thermal softening occurring at higher temperatures (Sun et al, 2010b) in opposition to high temperature strength (Ezugwu et al, 2003) associated with titanium alloys. Reasons for increasing SCE with speed can be ascribed to work hardening of titanium alloy during machining and aggravated tool wear at higher cutting speeds (Jaffery and Mativenga, 2008;Younas et al, 2019). In case of cryogenic cooling, work hardening is the major contributor towards SCE as compared to thermal softening.…”

“…Optimization can be performed by determining the best machining conditions for a particular response. Researchers have optimized different machining responses of various work piece materials taking into consideration diverse input parameters (Debnath et al, 2016;Asiltürk and Neşeli, 2012;Younas et al, 2019). Prasanna et al (2014) considered feed, speed and air pressure during dry drilling of Ti-6Al-4V to optimize overcut, taper circularity and cutting forces.…”

“…Surface roughness was mainly affected by cutting speed. Zain et al (2010) optimized surface roughness using simulated annealing in end milling. Feed, speed and rake angle were the input parameters.…”