For years, there has been increasing attention placed on the metal removal processes such as turning and milling operations; researchers from different areas focused on cutting conditions optimization. Cutting conditions optimization is a crucial step in Computer Aided Process Planning (CAPP); it aims to select optimal cutting parameters (such as cutting speed, feed rate, depth of cut, and number of passes) since these parameters affect production cost as well as production deadline. This paper deals with multipass turning operation optimization using a proposed Hybrid Genetic Simulated Annealing Algorithm (HSAGA). The SA-based local search is properly embedded into a GA search mechanism in order to move the GA away from being closed within local optima. The unit production cost is considered in this work as objective function to minimize under different practical and operational constraints. Taguchi method is then used to calibrate the parameters of proposed optimization approach. Finally, different results obtained by various optimization algorithms are compared to the obtained solution and the proposed hybrid evolutionary technique optimization has proved its effectiveness over other algorithms.
This study is on the integrated planning problem of maintenance and production within the frame work of a system subject to periodic preventive replacements with minimal repairs in case of unplanned failures. A model was developed using the overall cost by considering the interdependence between the maintenance plan and the production schedule. The overall cost contains two parts: the costs of launching a product, production, storage and breaking on the demand and the preventive and corrective maintenance costs for multi-periods and multi-products systems. The purpose of this integration is to find simultaneously the optimal cycle T at which the preventive maintenance takes place and the optimal values of lot-size by adding the setup time constraint. Using the mixed integer linear programming these optimal values minimize the total cost over a finite horizon. The results show that the proposed model performs quite well and opens new research direction for future improvements.
In this paper we present a multi-optimization technique based on genetic algorithms to search optimal cuttings parameters such as cutting depth, feed rate and cutting speed of multi-pass turning processes. Tow objective functions are simultaneously optimized under a set of practical of machining constraints, the first objective function is cutting cost and the second one is the used tool life time. The proposed model deals multi-pass turning processes where the cutting operations are divided into multi-pass rough machining and finish machining. Results obtained from Genetic Algorithms method are presented in Pareto frontier graphic; this technique helps us in decision making process. An example is presented to illustrate the procedure of this technique.
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