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

Increasing energy price and emission reduction requirements are new challenges faced by modernmanufacturers. A cons iderable amount of their energy consumption is attributed to the machining en-ergy consumption of machine tools (MTE), including cutting and non-cutting energy consumption (CEand NCE). The value of MTE is affected by the processing sequence of the features within a specific partbecause both the cutting and non-cutting plans vary based on different feature sequences. This articleaims to understand and characterise the MTE while machining a part. A CE model is developed to bridgethe knowledge gap, and two sub-models for specific energy consumption and actual cutting volume aredeveloped. Then, a single objective optimisation problem, minimising the MTE, is introduced. Twooptimisation approaches, Depth-First Search (DFS) and Genetic Algorithm (GA), are employed togenerate the optimal processing sequence. A case study is conducted, where five parts with 11e15features are processed on a machining centre. By comparing the experiment results of the two algo-rithms, GA is recommended for the MTE model. The accuracy of our model achieved 96.25%. 14.13% and14.00% MTE can be saved using DFS and GA, respectively. Moreover, the case study demonstrated a20.69% machining time reduction.