Designing a cost-time-quality-efficient grinding process using MODM methods

TL;DR

This paper develops a multi-objective optimization model for grinding processes, using five decision-making methods to identify optimal parameters that minimize time, cost, and surface roughness, with Weighted Sum and Goal Programming being most effective.

Contribution

It introduces a comprehensive multi-objective decision-making framework for optimizing grinding parameters, comparing five methods to identify the most effective solutions.

Findings

Weighted Sum and Goal Programming methods outperform others.

Optimal solutions achieved minimum grinding time, cost, and surface roughness.

Meta-heuristic algorithms are less suitable for this problem.

Abstract

In this paper a multi-objective mathematical model has been used to optimize grinding parameters include workpiece speed, depth of cut and wheel speed which highly affect the final surface quality. The mathematical model of the optimization problem consists of three conflict objective functions subject to wheel wear and production rate constraints. Exact methods can solve the NLP model in few seconds, therefore using Meta-heuristic algorithms which provide near optimal solutions in not suitable. Considering this, five Multi-Objective Decision Making methods have been used to solve the multi-objective mathematical model using GAMS software to achieve the optimal parameters of the grinding process. The Multi-Objective Decision Making methods provide different effective solutions where the decision maker can choose each solution in different situations. Different criteria have been considered to evaluate the performance of the five Multi-Objective Decision Making methods. Also, Technique for Order of Preference by Similarity to Ideal Solution method has been used to obtain the priority of each method and determine which Multi-Objective Decision Making method performs better considering all criteria simultaneously. The results indicated that Weighted Sum Method and Goal programming method are the best Multi-Objective Decision Making methods. The Weighted Sum Method and Goal programming provided solutions which are competitive to each other. In addition, these methods obtained solutions which have minimum grinding time, cost and surface roughness among other Multi-Objective Decision Making methods.