%0 Journal Article %A Hu, Luoke %A Peng, Chen %A Evans, Steve %A Peng, Tao %A Liu, Ying %A Tang, Renzhong %A Tiwari, Ashutosh %D 2017 %T Minimising the machining energy consumption of a machine tool by sequencing the features of a part %U https://repository.lboro.ac.uk/articles/journal_contribution/Minimising_the_machining_energy_consumption_of_a_machine_tool_by_sequencing_the_features_of_a_part/9560150 %2 https://repository.lboro.ac.uk/ndownloader/files/17192258 %K Machining energy %K Machine tools %K Feature sequencing %K Cutting volume %K Depth-First Search %K Genetic Algorithm %K Mechanical Engineering not elsewhere classified %K Mechanical Engineering %X 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. %I Loughborough University