Knowledge Graph Learning for Vehicle Additive Manufacturing of Recycled Metal Powder

• Published in: World electric vehicle journal 2023-10, Vol.14 (10), p.289
• Main Authors: Fang, Yuan, Chen, Mingzhang, Liang, Weida, Zhou, Zijian, Liu, Xunchen
• Format: Article
• Language: English
• Subjects: Additive manufacturing; Alloys; Artificial intelligence; BERT; Chatbots; ChatGPT; Circular economy; Electric vehicles; Graphs; Image processing; Image quality; knowledge graph; Knowledge representation; Language; Manufacturing industry; metal powder; Metal powders; Natural language; Powder metallurgy; Production costs; Quality assessment; Recycling; Resource recovery; Search engines; Semantics; Sustainable development; Sustainable production; sustainable vehicle manufacturing
• ISSN: 2032-6653; 2032-6653
• DOI: 10.3390/wevj14100289

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.