Determination of optimal cutting parameters is one of the most important elements in any process planning of metal parts. In this paper, a new optimization technique, firefly algorithm, is used for determining the machining parameters in a multipass turning operation model. The objective considered is minimization of production cost under a set of machining constraints. The optimization is carried out using firefly algorithm. An application example is presented and solved to illustrate the effectiveness of the presented algorithm.

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The determination of optimal cutting parameters is one of the most important elements in any process planning of metal parts. In this paper, a new hybrid genetic algorithm by using sequential quadratic programming is used for the optimization of cutting conditions. It is used for the resolution of a multipass turning optimization case by minimizing the production cost under a set of machining constraints. The genetic algorithm (GA) is the main optimizer of this algorithm whereas SQP Is used to fine tune the results obtained from the GA. Furthermore, the convergence characteristics and robustness of the proposed method have been explored through comparisons with results reported in literature. The obtained results indicate that the proposed hybrid genetic algorithm by using a sequential quadratic programming is effective compared to other techniques carried out by different researchers.

IntroductionThe 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]. Several cutting constraints must be considered in machining operations. In turning operations, a cutting process can possibly be completed with a single pass or by multiple passes. Multi-pass turning is preferable over single-pass AbstractIn 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.turning in the industry for economic reasons [2]. The optimization problem of machining parameters in multi-pass turnings becomes very complicated when plenty of practical constraints have to be considered [3]. Traditionally, 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. However, these traditional methods of optimization do not fare well over a broad spectrum of problem domains. Moreover, traditional techniques may not be robust. Numerous constraints and multiple passes make machining optimization problems complicated and hence these techniques are not ideal for solving such problems as they tend to obtain a local optimal solution. Thus, meta-heuristic algorithms have been developed to solve machining economics problems because of their power in global searching. There have been some works regarding optimization of cutting parameters [2,3,[10][11][12][13][14] for different situations, authors have been trying to bring out the utility and advantages of genetic algorithm, evolutionary approach and simulated annealing. It is proposed to use the hybrid genetic algorithm-sequential quadratic programming for the machining optimization problems.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. Cutting process modelDecision variables: In the constructed optimization problem, six decision variables are considered: cutting speeds in rough and finish machining (V r , V s ), feed rates in rough and finish machining (f r , f s ) and depth of cut for each pass of rough and finish machining ( )Objective function: Based on the minimum unit production DOI: 10.4172/2168-9873.1000101 Journal of Applied Mechanical Engineering . 1000 1000 ConstraintsThere are some constraints which affect the selection of the optimal cutting conditions and will be taken into account. The constraints in ...