Topology optimization (TO) has developed rapidly recently. However, topology optimization with stress constraints still faces many challenges due to its highly non-linear properties which will cause inefficient comput...Topology optimization (TO) has developed rapidly recently. However, topology optimization with stress constraints still faces many challenges due to its highly non-linear properties which will cause inefficient computation, iterative oscillation, and convergence guarantee problems. At the same time, isogeometric analysis (IGA) is accepted by more and more researchers, and it has become one important tool in the field of topology optimization because of its high fidelity. In this paper, we focus on topology optimization with stress constraints based on isogeometric analysis to improve computation efficiency and stability. A new hybrid solver combining the alternating direction method of multipliers and the method of moving asymptotes (ADMM-MMA) is proposed to solve this problem. We first generate an initial feasible point by alternating direction method of multipliers (ADMM) in virtue of the rapid initial descent property. After that, we adopt the method of moving asymptotes (MMA) to get the final results. Several benchmark examples are used to verify the proposed method, and the results show its feasibility and effectiveness.展开更多
Additive manufacturing(AM),also known as 3D printing,has emerged as a groundbreaking technology that has transformed the manufacturing industry.Its ability to produce intricate and customized parts with remarkable spe...Additive manufacturing(AM),also known as 3D printing,has emerged as a groundbreaking technology that has transformed the manufacturing industry.Its ability to produce intricate and customized parts with remarkable speed and reduced material waste has revolutionized traditional manufacturing approaches.However,the AM process itself is a complex and multifaceted undertaking,with various parameters that can significantly influence the quality and efficiency of the printed parts.To address this challenge,researchers have explored the integration of machine learning(ML)techniques to optimize the AM process.This paper presents a comprehensive review of process optimization for additive manufacturing based on machine learning,highlighting the recent advancements,methodologies,and challenges in this field.展开更多
文摘Topology optimization (TO) has developed rapidly recently. However, topology optimization with stress constraints still faces many challenges due to its highly non-linear properties which will cause inefficient computation, iterative oscillation, and convergence guarantee problems. At the same time, isogeometric analysis (IGA) is accepted by more and more researchers, and it has become one important tool in the field of topology optimization because of its high fidelity. In this paper, we focus on topology optimization with stress constraints based on isogeometric analysis to improve computation efficiency and stability. A new hybrid solver combining the alternating direction method of multipliers and the method of moving asymptotes (ADMM-MMA) is proposed to solve this problem. We first generate an initial feasible point by alternating direction method of multipliers (ADMM) in virtue of the rapid initial descent property. After that, we adopt the method of moving asymptotes (MMA) to get the final results. Several benchmark examples are used to verify the proposed method, and the results show its feasibility and effectiveness.
基金supported by the Provincial Natural Science Foundation of Anhui(No.2208085QA01)the Fundamental Research Funds for the Central Universities(No.WK0010000075)the National Natural Science Foundation of China(Nos.61972368 and 12371383).
文摘Additive manufacturing(AM),also known as 3D printing,has emerged as a groundbreaking technology that has transformed the manufacturing industry.Its ability to produce intricate and customized parts with remarkable speed and reduced material waste has revolutionized traditional manufacturing approaches.However,the AM process itself is a complex and multifaceted undertaking,with various parameters that can significantly influence the quality and efficiency of the printed parts.To address this challenge,researchers have explored the integration of machine learning(ML)techniques to optimize the AM process.This paper presents a comprehensive review of process optimization for additive manufacturing based on machine learning,highlighting the recent advancements,methodologies,and challenges in this field.
