International Journal of Computer Integrated Manufacturing
Special Issue on Machine Learning in Additive Manufacturing
Jingchao Jiang, School of Mechanical Engineering, Tongji University, Shanghai, China
Bin Zou, Jikai Liu, School of Mechanical Engineering, Shandong University, Jinan, China
David Rosen, School of Mechanical Engineering, Georgia Institute of Technology, Atlanta
Thirty years into its development, additive manufacturing has become a mainstream manufacturing process. Additive manufacturing fabricates products by adding materials layer-by-layer directly based on a 3D model (Rosen 2007). It is able to manufacture complex parts and allows more freedom of design optimization compared with traditional manufacturing techniques. Machine learning is now a hot technology that has been used in medical diagnosis, image processing, prediction, classification, learning association, regression, etc. (Kotsiantis, Zaharakis, and Pintelas 2006). Currently, focuses are increasingly given to using machine learning in manufacturing industry, including additive manufacturing (Jiang, Yu, et al. 2020; Jiang, Xiong, et al. 2020). Due to the rapid development of machine learning in additive manufacturing, this special issue is dedicated to bringing together researchers with diverse research backgrounds into a common forum, contributing thoughts to this cutting-edge research topic, and accelerating the development of AM technology with the aid of machine learning.
Original, high quality theoretical and empirical research papers are invited for submissions in this special issue. Typical topics include, but not limited to, following topics:
Artificial intelligence in AM
Machine learning aided design for AM
Machine learning for AM optimization
Machine learning for AM decision making
Machine learning for AM process planning
Machine learning in hybrid additive-subtractive manufacturing
State-of-the-art and new perspectives on machine learning in AM
Artificial intelligence integrated AM systems
Machine learning applications in AM
All submissions will be judged for their appropriateness to the journal’s remit and the novelty of their theoretical and practical research contributions.
Manuscript submission: 30 June 2022
Reviewer reports: 30 August 2022
Revised paper submission: 30 September 2022
Final manuscript submissions: 30 December 2022
Approximate publication date: 2023
Authors should follow the “Instructions for authors” presented at the journal website, when preparing manuscripts.
A link to the journal's submission portal can be found on the journal homepage.
Full papers should follow the IJCIM guidelines and clearly indicate that their paper is intended for the special issue on "Machine learning in Additive Manufacturing”. For further enquiries, please contact the managerial guest editor.
Managing Guest Editor
Dr. Jingchao Jiang
School of Mechanical Engineering, Tongji University, Shanghai, China,
Jiang, Jingchao, Yi Xiong, Zhiyuan Zhang, and David W. Rosen. 2020. “Machine Learning Integrated Design for Additive Manufacturing.” Journal of Intelligent Manufacturing, November. Springer, 1–14. doi:10.1007/s10845-020-01715-6.
Jiang, Jingchao, Chunling Yu, Xun Xu, Yongsheng Ma, and Jikai Liu. 2020. “Achieving Better Connections between Deposited Lines in Additive Manufacturing via Machine Learning.” Mathematical Biosciences and Engineering 17 (4): 3382–3394.
Kotsiantis, S. B., I. D. Zaharakis, and P. E. Pintelas. 2006. “Machine Learning: A Review of Classification and Combining Techniques.” Artificial Intelligence Review 26 (3). Springer: 159–190. doi:10.1007/s10462-007-9052-3.
Rosen, David W. 2007. “Computer-Aided Design for Additive Manufacturing of Cellular Structures.” Computer-Aided Design and Applications 4 (5). Taylor & Francis: 585–594. doi:10.1080/16864360.2007.10738493.