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...

Full description

Saved in:
Bibliographic Details
Main Author: Liu, Zhuo
Other Authors: Wong Jia Yiing, Patricia
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/167373
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary: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.