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
Summary
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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