Big data, machine learning, and digital twin assisted additive manufacturing: A review

Jin, Liuchao1,2,3; Zhai, Xiaoya1,4; Wang, Kang5,6; Zhang, Kang1,7; Wu, Dazhong8; Nazir, Aamer9,10; Jiang, Jingchao11; Liao, Wei-Hsin1,12

2024-08-01

10.1016/j.matdes.2024.113086

MATERIALS & DESIGN   影响因子和分区

0264-1275

1873-4197

Additive manufacturing (AM) has undergone significant development over the past decades, resulting in vast amounts of data that carry valuable information. Numerous research studies have been conducted to extract insights from AM data and utilize it for optimizing various aspects such as the manufacturing process, supply chain, and real-time monitoring. Data integration into proposed digital twin frameworks and the application of machine learning techniques is expected to play pivotal roles in advancing AM in the future. In this paper, we provide an overview of machine learning and digital twin -assisted AM. On one hand, we discuss the research domain and highlight the machine -learning methods utilized in this field, including material analysis, design optimization, process parameter optimization, defect detection and monitoring, and sustainability. On the other hand, we examine the status of digital twin -assisted AM from the current research status to the technical approach and offer insights into future developments and perspectives in this area. This review paper aims to examine present research and development in the convergence of big data, machine learning, and digital twin -assisted AM. Although there are numerous review papers on machine learning for additive manufacturing and others on digital twins for AM, no existing paper has considered how these concepts are intrinsically connected and interrelated. Our paper is the first to integrate the three concepts big data, machine learning, and digital twins and propose a cohesive framework for how they can work together to improve the efficiency, accuracy, and sustainability of AM processes. By exploring latest advancements and applications within these domains, our objective is to emphasize the potential advantages and future possibilities associated with integration of these technologies in AM.

Additive manufacturing Big data Machine learning Digital twin Data-driven

SCI ; EI

英语

其他

Research Grants Council[C4074-22G] ; The Chinese University of Hong Kong[3110174]

Materials Science

Materials Science, Multidisciplinary

WOS:001262091700001

ELSEVIER SCI LTD

20242616531522

Additives ; Big data ; Data integration ; E-learning ; Industrial research ; Machine learning ; Supply chains

Data Processing and Image Processing:723.2 ; Artificial Intelligence:723.4 ; Printing Equipment:745.1.1 ; Chemical Agents and Basic Industrial Chemicals:803 ; Engineering Research:901.3 ; Inventory Control:911.3 ; Industrial Engineering and Management:912 ; Industrial Engineering:912.1 ; Production Planning and Control; Manufacturing:913

MATERIALS SCIENCE

Web of Science

1.Chinese Univ Hong Kong, Dept Mech & Automat Engn, Hong Kong, Peoples R China
2.Southern Univ Sci & Technol, Dept Mech & Energy Engn, Shenzhen 518055, Peoples R China
3.Southern Univ Sci & Technol, Shenzhen Key Lab Soft Mech & Smart Mfg, Shenzhen 518055, Peoples R China
4.Univ Sci & Technol China, Sch Math Sci, Hefei 230026, Peoples R China
5.Hong Kong Polytech Univ, Sch Nursing, Hong Kong, Peoples R China
6.Zhejiang Univ, Sch Mech Engn, Hangzhou 310027, Peoples R China
7.Nano & Adv Mat Inst Ltd, Hong Kong Sci Pk, Hong Kong, Peoples R China
8.Univ Cent Florida, Dept Mech & Aerosp Engn, Orlando, FL 32816 USA
9.King Fahd Univ Petr & Minerals, Dept Mech Engn, Dhahran 31261, Saudi Arabia
10.King Fahd Univ Petr & Minerals, Interdisciplinary Res Ctr Adv Mat, Dhahran 31261, Saudi Arabia
11.Univ Exeter, Dept Engn, Exeter, England
12.Chinese Univ Hong Kong, Inst Intelligent Design & Mfg, Hong Kong, Peoples R China

Jin, Liuchao,Zhai, Xiaoya,Wang, Kang,et al. Big data, machine learning, and digital twin assisted additive manufacturing: A review[J]. MATERIALS & DESIGN,2024,244.

Jin, Liuchao.,Zhai, Xiaoya.,Wang, Kang.,Zhang, Kang.,Wu, Dazhong.,...&Liao, Wei-Hsin.(2024).Big data, machine learning, and digital twin assisted additive manufacturing: A review.MATERIALS & DESIGN,244.

Jin, Liuchao,et al."Big data, machine learning, and digital twin assisted additive manufacturing: A review".MATERIALS & DESIGN 244(2024).