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Minimising the machining energy consumption of a machine tool by sequencing the features of a part

Hu, Luoke, Peng, Chen, Evans, Steve, Peng, Tao, Liu, Ying, Tang, Renzhong, Tiwari, Ashutosh
Energy 2017 v.121 pp. 292-305
algorithms, case studies, cutting, energy, energy costs, models, specific energy
Increasing energy price and emission reduction requirements are new challenges faced by modern manufacturers. A considerable amount of their energy consumption is attributed to the machining energy consumption of machine tools (MTE), including cutting and non-cutting energy consumption (CE and NCE). The value of MTE is affected by the processing sequence of the features within a specific part because both the cutting and non-cutting plans vary based on different feature sequences. This article aims to understand and characterise the MTE while machining a part. A CE model is developed to bridge the knowledge gap, and two sub-models for specific energy consumption and actual cutting volume are developed. Then, a single objective optimisation problem, minimising the MTE, is introduced. Two optimisation approaches, Depth-First Search (DFS) and Genetic Algorithm (GA), are employed to generate the optimal processing sequence. A case study is conducted, where five parts with 11–15 features are processed on a machining centre. By comparing the experiment results of the two algorithms, GA is recommended for the MTE model. The accuracy of our model achieved 96.25%. 14.13% and 14.00% MTE can be saved using DFS and GA, respectively. Moreover, the case study demonstrated a 20.69% machining time reduction.