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

Luoke Hu, Chen Peng, Steve Evans, Tao Peng, Ying Liu, Renzhong Tang and Ashutosh Tiwari

Energy, 2017, vol. 121, issue C, 292-305

Abstract: 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.

Keywords: Machining energy; Machine tools; Feature sequencing; Cutting volume; Depth-First Search; Genetic Algorithm (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:121:y:2017:i:c:p:292-305

DOI: 10.1016/j.energy.2017.01.039

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