Data-driven sustainability improvement in laser metal deposition (LMD)
This study improves the sustainability of laser metal deposition (LMD) process by reducing material and energy waste caused by the constant blowing out of powder during dry-run movement. To achieve this, the study formulates the LMD path planning problem as a modified travelling salesman problem usi...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/167373 |
| _version_ | 1826127828447395840 |
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| author | Liu, Zhuo |
| author2 | Wong Jia Yiing, Patricia |
| author_facet | Wong Jia Yiing, Patricia Liu, Zhuo |
| author_sort | Liu, Zhuo |
| collection | NTU |
| description | This study improves the sustainability of laser metal deposition (LMD) process by reducing material and energy waste caused by the constant blowing out of powder during dry-run movement. To achieve this, the study formulates the LMD path planning problem as a modified travelling salesman problem using mixed-integer linear programming, while considering LMD-specific constraints such as line approach direction and material filling direction. To solve this problem effectively and efficiently, two meta-heuristics, genetic algorithm, and simulated annealing, are proposed. A comparison study is conducted, and the results show that simulated annealing outperforms genetic algorithm in terms of fitness and search time. Thus, by implementing the proposed algorithm reduces the distance of dry-run movement and improves the sustainability of LMD process. The proposed approach has the potential to significantly improve the state-of-the-art of LMD path planning, particularly in computer-aided manufacturing (CAM) and sustainable manufacturing. |
| first_indexed | 2024-10-01T07:14:52Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/167373 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:14:52Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1673732023-07-07T16:04:47Z Data-driven sustainability improvement in laser metal deposition (LMD) Liu, Zhuo Wong Jia Yiing, Patricia School of Electrical and Electronic Engineering EJYWong@ntu.edu.sg Engineering::Electrical and electronic engineering This study improves the sustainability of laser metal deposition (LMD) process by reducing material and energy waste caused by the constant blowing out of powder during dry-run movement. To achieve this, the study formulates the LMD path planning problem as a modified travelling salesman problem using mixed-integer linear programming, while considering LMD-specific constraints such as line approach direction and material filling direction. To solve this problem effectively and efficiently, two meta-heuristics, genetic algorithm, and simulated annealing, are proposed. A comparison study is conducted, and the results show that simulated annealing outperforms genetic algorithm in terms of fitness and search time. Thus, by implementing the proposed algorithm reduces the distance of dry-run movement and improves the sustainability of LMD process. The proposed approach has the potential to significantly improve the state-of-the-art of LMD path planning, particularly in computer-aided manufacturing (CAM) and sustainable manufacturing. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-26T00:09:33Z 2023-05-26T00:09:33Z 2023 Final Year Project (FYP) Liu, Z. (2023). Data-driven sustainability improvement in laser metal deposition (LMD). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167373 https://hdl.handle.net/10356/167373 en A1138-221 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Liu, Zhuo Data-driven sustainability improvement in laser metal deposition (LMD) |
| title | Data-driven sustainability improvement in laser metal deposition (LMD) |
| title_full | Data-driven sustainability improvement in laser metal deposition (LMD) |
| title_fullStr | Data-driven sustainability improvement in laser metal deposition (LMD) |
| title_full_unstemmed | Data-driven sustainability improvement in laser metal deposition (LMD) |
| title_short | Data-driven sustainability improvement in laser metal deposition (LMD) |
| title_sort | data driven sustainability improvement in laser metal deposition lmd |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/167373 |
| work_keys_str_mv | AT liuzhuo datadrivensustainabilityimprovementinlasermetaldepositionlmd |