Artificial intelligence in metal forming

被引：0

作者：

Cao, Jian ^{[1
]}

Bambach, Markus ^{[2
]}

Merklein, Marion ^{[3
]}

Mozaffar, Mojtaba ^{[1
,4
]}

Xue, Tianju ^{[1
,5
]}

机构：

[1] Northwestern Univ, Dept Mech Engn, Evanston, IL 60208 USA

[2] Swiss Fed Inst Technol, Adv Mfg, PFA 55,Technoparkstr 1, CH-8005 Zurich, Switzerland

[3] Friedrich Alexander Univ Erlangen Nurnberg, Inst Mfg Technol LFT, Egerlandstr 13, D-91058 Erlangen, Germany

[4] Amazon Robot, 300 Riverpark Dr, N Reading, MA 01864 USA

[5] Hong Kong Univ Sci & Technol, Dept Civil & Environm Engn, Kowloon, Clear Water Bay, Hong Kong, Peoples R China

来源：

CIRP ANNALS-MANUFACTURING TECHNOLOGY | 2024年 / 73卷 / 02期

关键词：

Artificial intelligence; Machine learning; Material characterization; Process design; Process control; ITERATIVE LEARNING CONTROL; TOOL WEAR PREDICTION; CLOSED-LOOP CONTROL; NEURAL-NETWORK APPROACH; FINITE-ELEMENT-METHOD; SURFACE-ROUGHNESS; DUCTILE FRACTURE; INVERSE PROBLEMS; SHEET; OPTIMIZATION;

D O I：

10.1016/j.cirp.2024.04.102

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Forming processes are known for their intricacies in prediction and control due to the complex loading conditions and material flow. This paper will first introduce the AI algorithms used or having potential to be used in forming, and then investigate the state-of-the-art advances of AI-based technologies in forming processes with four main pillars of process simulation, process design and optimization, in-situ process control, and qualification and certification of forming processes and formed products. Future directions of AI in forming for both academic research and industrial applications will be proposed to leverage digitalization and data science to explore new solutions in forming processes. (c) 2024 CIRP. Published by Elsevier Ltd. All rights reserved.

引用

页码：561 / 587

页数：27

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