Knowledge Graph Learning for Vehicle Additive Manufacturing of Recycled Metal Powder

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...

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Bibliographic Details
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
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Summary: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.
ISSN:2032-6653
2032-6653
DOI:10.3390/wevj14100289