To overcome the difficulties in previous researches about energy-efficient design of parts, a method to estimate machining-related energy consumption of parts at the design phase is proposed. The binary tree is constructed to describe the structure of a part, and each node in the binary tree represents one feature in the part. The material embodied energy, theoretical cutting energy consumption and air-cutting energy consumption of a feature can be calculated based on its design and manufacturing parameters. At the design phase, manufacturing parameters of a feature can be obtained by the method of feature mapping from design parameters. By adding up above three types of energy consumption, total energy consumption of a feature can be calculated. Further, by adding up total energy consumption of all features in a part, the energy consumption of this part can be estimated. The proposed method was demonstrated by estimating the energy consumption of a shaft part designed by an auto parts manufacturer, and meanwhile the measured energy consumption of the shaft part was acquired by experimental measurement. The estimation accuracy is analysed and verified by comparing the estimated value and measured value.

Machining allowance distribution and related parameter optimization of machining processes have been well-discussed. However, for energy saving purposes, the optimization priorities of different machining phases should be different. There are often significant incoherencies between the existing research and real applications. This paper presents an improved method to optimize machining allowance distribution and parameters comprehensively, considering energy-saving strategy and other multi-objectives of different phases. The empirical parametric models of different machining phases were established, with the allowance distribution problem properly addressed. Based on previous analysis work of algorithm performance, non-dominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition were chosen to obtain Pareto solutions. Algorithm performances were compared based on the efficiency of finding the Pareto fronts. Two case studies of a cylindrical turning and a face milling were carried out. Results demonstrate that the proposed method is effective in trading-off and finding precise application scopes of machining allowances and parameters used in real production. Cutting tool life and surface roughness can be greatly improved for turning. Energy consumption of rough milling can be greatly reduced to around 20% of traditional methods. The optimum algorithm of each case is also recognized. The proposed method can be easily extended to other machining scenarios and can be used as guidance of process planning for meeting various engineering demands.