AI-driven design: powered by large language model and intelligent computation
To meet the extreme precision requirements of nanometer-scale semiconductor manufacturing and micrometer-level aerospace component processing, the complexity of precision manufacturing equipment design has exceeded the capabilities of traditional design methodologies. Conventional experience-driven...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | International Journal of Extreme Manufacturing |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2631-7990/adea23 |
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| _version_ | 1849413243224719360 |
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| author | Guodong Sa Zhinan Li Zhenyu Liu Jianrong Tan |
| author_facet | Guodong Sa Zhinan Li Zhenyu Liu Jianrong Tan |
| author_sort | Guodong Sa |
| collection | DOAJ |
| description | To meet the extreme precision requirements of nanometer-scale semiconductor manufacturing and micrometer-level aerospace component processing, the complexity of precision manufacturing equipment design has exceeded the capabilities of traditional design methodologies. Conventional experience-driven design approaches exhibit fundamental limitations when confronting high-dimensional parameter spaces, complex multidisciplinary coupling effects, and dynamic performance prediction requirements, rendering trial-and-error iterative optimization processes inefficient and incapable of achieving optimal solutions. Intelligent design offers new pathways to overcome these limitations through the integration of artificial intelligence (AI) with traditional engineering workflows. However, the transition from theoretical concepts to manufacturing practice encounters three critical technical bottlenecks: the sparsity and heterogeneity of design data constrain the development of domain-specific large models, hallucination phenomena in generative design compromise solution trustworthiness, and numerical simulation methods face fundamental trade-offs between computational accuracy and efficiency. This paper conducts comprehensive analysis of the underlying causes of these challenges and proposes a knowledge-generation-simulation integrated intelligent design ecosystem as a development pathway. This approach achieves deep integration of large models with manufacturing domain knowledge, seamless fusion of AI with Computer-Aided Design/Computer-Aided Engineering (CAD/CAE) systems, and comprehensive synthesis of physics-based mechanisms with data-driven methods, driving the evolution of intelligent design from human-dominated iterative processes toward autonomous collaborative innovation systems, thereby providing robust support for technological breakthroughs in precision and extreme manufacturing equipment while facilitating the intelligent transformation of the manufacturing industry. |
| format | Article |
| id | doaj-art-953d7a4877854eed9c724df2dc8edd4e |
| institution | Kabale University |
| issn | 2631-7990 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | International Journal of Extreme Manufacturing |
| spelling | doaj-art-953d7a4877854eed9c724df2dc8edd4e2025-08-20T03:34:10ZengIOP PublishingInternational Journal of Extreme Manufacturing2631-79902025-01-017606300110.1088/2631-7990/adea23AI-driven design: powered by large language model and intelligent computationGuodong Sa0Zhinan Li1https://orcid.org/0000-0002-0422-1076Zhenyu Liu2https://orcid.org/0000-0003-2463-4553Jianrong Tan3State Key Laboratory of CAD&CG, Zhejiang University , Hangzhou, People’s Republic of China; School of Mechanical Engineering of Zhejiang University , Hangzhou, People’s Republic of China; Ningbo Global Innovation Center, Zhejiang University , Ningbo, People’s Republic of ChinaState Key Laboratory of CAD&CG, Zhejiang University , Hangzhou, People’s Republic of China; School of Mechanical Engineering of Zhejiang University , Hangzhou, People’s Republic of ChinaState Key Laboratory of CAD&CG, Zhejiang University , Hangzhou, People’s Republic of China; School of Mechanical Engineering of Zhejiang University , Hangzhou, People’s Republic of ChinaState Key Laboratory of CAD&CG, Zhejiang University , Hangzhou, People’s Republic of China; School of Mechanical Engineering of Zhejiang University , Hangzhou, People’s Republic of ChinaTo meet the extreme precision requirements of nanometer-scale semiconductor manufacturing and micrometer-level aerospace component processing, the complexity of precision manufacturing equipment design has exceeded the capabilities of traditional design methodologies. Conventional experience-driven design approaches exhibit fundamental limitations when confronting high-dimensional parameter spaces, complex multidisciplinary coupling effects, and dynamic performance prediction requirements, rendering trial-and-error iterative optimization processes inefficient and incapable of achieving optimal solutions. Intelligent design offers new pathways to overcome these limitations through the integration of artificial intelligence (AI) with traditional engineering workflows. However, the transition from theoretical concepts to manufacturing practice encounters three critical technical bottlenecks: the sparsity and heterogeneity of design data constrain the development of domain-specific large models, hallucination phenomena in generative design compromise solution trustworthiness, and numerical simulation methods face fundamental trade-offs between computational accuracy and efficiency. This paper conducts comprehensive analysis of the underlying causes of these challenges and proposes a knowledge-generation-simulation integrated intelligent design ecosystem as a development pathway. This approach achieves deep integration of large models with manufacturing domain knowledge, seamless fusion of AI with Computer-Aided Design/Computer-Aided Engineering (CAD/CAE) systems, and comprehensive synthesis of physics-based mechanisms with data-driven methods, driving the evolution of intelligent design from human-dominated iterative processes toward autonomous collaborative innovation systems, thereby providing robust support for technological breakthroughs in precision and extreme manufacturing equipment while facilitating the intelligent transformation of the manufacturing industry.https://doi.org/10.1088/2631-7990/adea23intelligent designmanufacturing-driven designartificial intelligencegenerative designphysics-informed modeling |
| spellingShingle | Guodong Sa Zhinan Li Zhenyu Liu Jianrong Tan AI-driven design: powered by large language model and intelligent computation International Journal of Extreme Manufacturing intelligent design manufacturing-driven design artificial intelligence generative design physics-informed modeling |
| title | AI-driven design: powered by large language model and intelligent computation |
| title_full | AI-driven design: powered by large language model and intelligent computation |
| title_fullStr | AI-driven design: powered by large language model and intelligent computation |
| title_full_unstemmed | AI-driven design: powered by large language model and intelligent computation |
| title_short | AI-driven design: powered by large language model and intelligent computation |
| title_sort | ai driven design powered by large language model and intelligent computation |
| topic | intelligent design manufacturing-driven design artificial intelligence generative design physics-informed modeling |
| url | https://doi.org/10.1088/2631-7990/adea23 |
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