An integrated approach of process planning and cutting parameter optimization for energy-aware CNC machining

Process plans and cutting parameters are important components in the computer aided process planning. Reasonable choice of process plans and cutting parameters can significant affect the energy consumption as well as traditional economic performances of manufacturing systems. Traditionally, process planning and cutting parameter optimization were carried out separately and sequentially at a machine tool level or a machining process level, which limits the potential for energy savings. Considering the fact that the functions of two components in different operation levels are usually complementary, the energy consumption of CNC machining system can be further improved if they are tightly integrated. In this paper, an integrated approach of process planning and cutting parameter optimization is proposed to minimize the total energy consumption of CNC machining and to balance machine workloads in workshop. Firstly, the energy characteristics of machining process are explicitly analyzed by considering multiple process flexibilities and the cutting parameters. Then a multi-objective integration model of process planning and cutting parameter optimization is proposed to take the minimum energy consumption and balance of machine workloads as the optimization objectives, which is solved by a Multi objective Simulated Annealing algorithm. To verify the energy-saving performance of the proposed multi-objective integration problem, case studies have been conducted and the experimental results show that: i) the energy-oriented integration problem can achieve more energy savings compared with energy-oriented process planning or energy-oriented cutting parameter optimization; ii) the energy oriented integration of process planning and cutting parameter optimization does not always yield a solution that addresses the need for balance of machine workloads; iii) The proposed multi-objective integration method can strike a balance between the total energy consumption and the machine workload. (C) 2017 Elsevier Ltd. All rights reserved.