Machining parameters selection for stock removal turning in process plans using a float encoding genetic algorithm

In view of the expensive investment of a CNC machine, the machining parameters should be optimized by considering the production cost and machining constraints. The machined parts on a CNC turning machine commonly have continuous finished profiles, and the raw materials are cylindrical stocks. This paper introduces a mathematical model to optimize machining parameters for cylindrical stock removal turning. A float encoding genetic algorithm (FEGA) is proposed to solve the formulated machining model. An illustrative example for the developed machining model is solved using the FEGA optimization method. From the computational results, it is demonstrated that the proposed optimization algorithm can adequately resolve the machining models. The developed machining model and the FEGA optimization method can be integrated into a computer-aided process planning system (CAPP) to provide consistent and effective process plans.