Multi-objective Optimization: A Case Study

TL;DR

This paper demonstrates the application of five multi-objective optimization methods to optimize face milling parameters, balancing material removal rate and surface roughness, and compares their effectiveness.

Contribution

It provides a comparative case study of five multi-objective optimization routines applied to face milling, highlighting their relative advantages.

Findings

Genetic Algorithm yielded the best trade-off between objectives.

Different routines showed varying effectiveness in optimizing machining parameters.

The study offers insights into selecting appropriate optimization methods for manufacturing processes.

Abstract

The aim of this literature is to illustrate the application of multi-objective optimization routines through a case study of face milling operation. For this purpose, the face milling operation is designed as a multi-objective optimization problem and then solved to obtain optimum values for the machining parameters - cutting speed (Vc), feed rate (fz) and depth of cut (t) using the optimization routines. The formulated problem of face milling operation includes two conflicting objectives - to maximize Material Removal Rate (MRR) and to minimize surface roughness (Ra). Among various multi-objective optimization routines, five of them namely Global Criterion Method, Lexicographic Method, Weighted Sum Method, Epsilon Constraint Method and Genetic Algorithm are used in this literature. The outcomes of these multi-objective optimization routines are then compared to reflect their relative attractiveness.