Multi-Response Optimization in High-Speed Machining of Ti-6Al-4V Using TOPSIS-Fuzzy Integrated Approach.

Adel T Abbas,Monis Luqman,Hussien Hegab,Italo Tomaz,Neeraj Sharma,Saqib Anwar

doi:10.3390/ma13051104

Abstract

Titanium alloys are widely used in various applications including biomedicine, aerospace, marine, energy, and chemical industries because of their superior characteristics such as high hot strength and hardness, low density, and superior fracture toughness and corrosion resistance. However, there are different challenges when machining titanium alloys because of the high heat generated during cutting processes which adversely affects the product quality and process performance in general. Thus, optimization of the machining conditions while machining such alloys is necessary. In this work, an experimental investigation into the influence of different cutting parameters (i.e., depth of cut, cutting length, feed rate, and cutting speed) on surface roughness (Rz), flank wear (VB), power consumption as well as the material removal rate (MRR) during high-speed turning of Ti-6Al-4V alloy is presented and discussed. In addition, a backpropagation neural network (BPNN) along with the technique for order of preference by similarity to ideal solution (TOPSIS)-fuzzy integrated approach was employed to model and optimize the overall cutting performance. It should be stated that the predicted values for all machining outputs demonstrated excellent agreement with the experimental values at the selected optimal solution. In addition, the selected optimal solution did not provide the best performance for each measured output, but it achieved a balance among all studied responses.

Highlights

Titanium alloys are widely used in various applications including aerospace, marine, biomedicine, energy, and chemical industries
TOPSIS-fuzzy performance index (TFPI) predicted that a length cutting 40 speed m/min, depth of cut of 0.3 mm, feed feed rate of 0.15with mm/rev, and a cutting mmof were the optimal machining parameters rate of 0.15 mm/rev, and a cutting length 40 mm were the optimal machining parameters setting
It should be stated that the predicted values for all machining outputs demonstrated an excellent agreement with the experimental values at the selected optimal outputs demonstrated an excellent agreement with the experimental values at the selected optimal solution

Summary

Introduction

Titanium alloys are widely used in various applications including aerospace, marine, biomedicine, energy, and chemical industries. Kechagias et al [10] used full factorial and Taguchi design for machinability prediction in the turning of titanium alloys They showed that resultant cutting forces increased with an increase in the feed rate and depth of cut and concluded that the design of the experimental technique could effectively be used to evaluate the machinability of such materials. These studies focused only on analyzing the influence of each cutting parameter and did not cover surface integrity or machining costs. It helps to provide different solutions that can offer enough flexibility for the decision maker to select the most appropriate cutting conditions based on the desired objectives

Materials and Methods

Optimization Methodology

The overallmethodology methodology for modeling when high‐speed machining

BPNN Modeling

Results

Results Validation

Conclusions and Future Work