Big data, machine learning, and digital twin assisted additive manufacturing: A review
Chinese University of Hong Kong · Southern University of Science and Technology · +7 more institutions
Abstract
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…
Citation impact
- FWCI
- 30.84
- Percentile
- 100%
- References
- 1,073
Authors
8- LJLiuchao Jin
Chinese University of Hong Kong, Southern University of Science and Technology
- XZXiaoya Zhai
University of Science and Technology of China, Chinese University of Hong Kong
- KWKang Wang
Hong Kong Polytechnic University, Zhejiang University
- KZKang Zhang
Chinese University of Hong Kong, Nano and Advanced Materials Institute
- DWDazhong Wu
University of Central Florida
Topics & keywords
- Big data
- Machine learning
- Computer science
- Artificial intelligence
- Process (computing)
- Field (mathematics)
- Data science
- Domain (mathematical analysis)