Minimising the energy consumption of tool change and tool path of machining by sequencing the features
- 1. State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027 (China)
- 2. School of Engineering, University of Glasgow, University Ave, Glasgow, G12 8QQ (United Kingdom)
- 3. Department of Mechanical and Aerospace Engineering, Syracuse University, Syracuse, NY, 13244 (United States)
- 4. Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, S1 3JD (United Kingdom)
Description
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. 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.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.energy.2018.01.046Additional details
Identifiers
- DOI
- 10.1016/j.energy.2018.01.046;
- PII
- S0360544218300574;
Publishing Information
- Journal Title
- Energy (Oxford)
- Journal Volume
- 147
- Journal Page Range
- p. 390-402
- ISSN
- 0360-5442
- CODEN
- ENEYDS
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 53001132
- Subject category
- S32: ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION;
- Descriptors DEI
- ENERGY CONSUMPTION; GENETIC ALGORITHMS; MACHINE TOOLS; OPTIMIZATION; PROCESSING
- Descriptors DEC
- ALGORITHMS; EQUIPMENT; MATHEMATICAL LOGIC; TOOLS
Optional Information
- Copyright
- Copyright (c) 2018 Elsevier Ltd. All rights reserved.