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...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.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2024YFB3309500)the National Natural Science Foundation of China(Grant Nos.U24B6005,U22A6001)。
文摘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.
