Abstract

In this paper, a new, hybrid genetic algorithm-sequential quadratic programming is used for the resolution of cutting conditions. It used for the resolution of a multi-pass turning optimization case by minimizing the production cost under a set of machining constraints. The result indicates that the proposed hybrid genetic algorithm-sequential quadratic programming is effective when compared to other techniques carried out by different researchers.

Highlights

  • The selection of optimal cutting parameters, like the number of passes, depth of cut for each pass, feed and speed, is a very important issue for every machining process [1]

  • The current paper focuses on the application of a new optimization technique, hybrid genetic algorithm-sequential quadratic programming, to determine the optimal machining parameters that minimize the unit production cost in multi-pass turnings

  • This paper presents a hybrid Genetic Algorithm (GA)-Sequential Quadratic Programming (SQP) optimization for solving the multi-pass turning operations problem

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Summary

Introduction

The selection of optimal cutting parameters, like the number of passes, depth of cut for each pass, feed and speed, is a very important issue for every machining process [1]. The optimization problem of machining parameters in multi-pass turnings becomes very complicated when plenty of practical constraints have to be considered [3]. Mathematical programming techniques like graphical methods [4], linear programming [5], dynamic programming [6,7] and geometric programming [8,9] had been used to solve optimization problems of machining parameters in multi-pass turnings. These traditional methods of optimization do not fare well over a broad spectrum of problem domains. It is proposed to use the hybrid genetic algorithm-sequential quadratic programming for the machining optimization problems

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