Minimising the energy consumption of tool change and tool path of machining by sequencing the features. (15th March 2018)

• Record Type: Journal Article
• Authors: Hu, Luoke; Liu, Ying; Peng, Chen; Tang, Wangchujun; Tang, Renzhong; Tiwari, Ashutosh
• Is Part Of: Energy. Volume 147(2018)
• Page Start: 390
• Page End: 402
• Publication Date: 2018-03-15
• Subjects: Energy -- Machine tools -- Tool change and tool path -- Feature sequencing -- Depth-first search -- Genetic algorithm; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2018.01.046
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: A considerable amount of energy is consumed by machine tools during the run-time operations such as tool change and tool path. The value of this part of energy is affected by the processing sequence of features of a part (PSFP) because the tool path and tool change plan will vary based on the different PSFP. This paper firstly aims to understand the relationship between the PSFP and the energy consumption of tool change and tool path during the feature transitions. Then, a model is introduced for the single objective optimisation problem that minimises the energy consumption of machine tools during the feature transitions which include all the tool path and tool change operations. Finally, optimisation approaches including depth-first search and genetic algorithm are modified and applied to find the optimal PSFP which results in the minimisation of the energy consumption of feature transitions (EFT). In the case study, the optimal and near-optimal sequences of features, in terms of the minimum EFT, of a part which has 15 actual features and is processed by a machining centre have been found. The optimal PSFP achieves a 28.60% EFT reduction, which validates the effectiveness of the developed model and optimisation approaches. Besides, a 27.95% time reduction of feature transitions benefits from the EFT minimisation. Highlights: Energy consumed for tool change and tool path is reduced by sequencing the features. The effect of the feature sequence on energy consumed for feature transitions is analysed. Energy consumed for rapid and normal feeding activities and tool change is modelled. The optimal feature sequence is obtained by depth-first search and genetic algorithm. The approach achieves a 28.60% energy consumption reduction of feature transitions.