Knowledge Graph Learning for Vehicle Additive Manufacturing of Recycled Metal Powder
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Published:2023-10-12
Issue:10
Volume:14
Page:289
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ISSN:2032-6653
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Container-title:World Electric Vehicle Journal
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language:en
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Short-container-title:WEVJ
Author:
Fang Yuan1, Chen Mingzhang23ORCID, Liang Weida4, Zhou Zijian3, Liu Xunchen3
Affiliation:
1. Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education, College of Chemistry and Materials Science, South-Central University for Nationalities, Wuhan 430074, China 2. Department of Mechanical Engineering, National University of Singapore, Singapore 138600, Singapore 3. School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China 4. School of Computer Science, National University of Singapore, Singapore 138600, Singapore
Abstract
Research on manufacturing components for electric vehicles plays a vital role in their development. Furthermore, significant advancements in additive manufacturing processes have revolutionized the production of various parts. By establishing a system that enables the recovery, processing, and reuse of metal powders essential for additive manufacturing, we can achieve sustainable production of electric vehicles. This approach holds immense importance in terms of reducing manufacturing costs, expanding the market, and safeguarding the environment. In this study, we developed an additive manufacturing system for recycled metal powders, encompassing powder variety, properties, processing, manufacturing, component properties, and applications. This system was used to create a knowledge graph providing a convenient resource for researchers to understand the entire procedure from recycling to application. To improve the graph’s accuracy, we employed ChatGPT and BERT training. We also demonstrated the knowledge graph’s utility by processing recycled 316 L stainless steel powders and assessing their quality through image processing. This experiment serves as a practical example of recycling and analyzing powders using the established knowledge graph.
Funder
National Natural Science Foundation of China China Postdoctoral Science Foundation Fundamental Research Funds for the Central Universities
Subject
Automotive Engineering
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