AI-driven design: powered by large language model and intelligent computation

Abstract 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.

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Institutions

Publication Details

Journal
International Journal of Extreme Manufacturing
Published
2025-06-30
DOI
https://doi.org/10.1088/2631-7990/adea23
Citations
2
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
2.65

Funders

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article

AI-driven design: powered by large language model and intelligent computation

Jianrong Tan, Guodong Sa, Zhenyu Liu, Zhinan Li
2 citations
International Journal of Extreme Manufacturing
Model Reduction and Neural Networks
2.65
article

AI-driven design: powered by large language model and intelligent computation

Jianrong Tan, Guodong Sa, Zhenyu Liu, Zhinan Li
article en
2 citations

Abstract

Abstract 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.

International Journal of Extreme ManufacturingVol. 7(6)
Ningbo University (CN), University of Nottingham Ningbo China (CN), Zhejiang University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 10%
Model Reduction and Neural Networks
2.65
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