Multipass Turning Operation Process Optimization Using Hybrid Genetic Simulated Annealing Algorithm

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.

Intelligent Selection of Machining Parameters in Multipass Turnings Using Firefly Algorithm

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.

Optimization of Multipass Turning Parameters by Genetic Algorithm

Optimization of cutting parameters is one of the key obstacles for CAD/CAM integration. In this work optimum cutting parameters, the best sequence, number, and type of passes of turning operation are determined by Genetic Algorithm (GA). Proposed optimization strategy ensures that no constraint will be violated at the optimum condition and determines the optimum number and type of passes such as rough, finish and semi-finish passes to complete a multipass turning operation. Here objective function is the unit production cost and constraints are limits of cutting force, power, tool life, stability condition, tool chip interface temperature, surface finish, feed rate to depth of cut ratio and the available rotational speed of spindle of machine tool.