TCIM.png

International Journal of Computer Integrated Manufacturing​
Special Issue on Machine Learning in Additive Manufacturing

Guest Editors

  • Jingchao Jiang, University of Auckland, New Zealand

  • Bin Zou, Jikai Liu, School of Mechanical Engineering, Shandong University, Jinan, China

  • David Rosen, School of Mechanical Engineering, Georgia Institute of Technology, Atlanta

Introduction

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.

 

Scope

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.

 

Timeline

  • 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

 

Submission Guidelines

To prepare the manuscripts, authors should follow the “Instructions for authors” presented at the journal website.

Please check and follow the instructions on the electronic submission system via Taylor & Francis . Full papers should follow the IJCIM guidelines and clearly indicate the “Special issue on Machine learning in Additive Manufacturing”. For further enquiries, please contact the managerial guest editor.

 

Managing Guest Editor

Dr. Jingchao Jiang

The University of Auckland, New Zealand

Email: jjia547@aucklanduni.ac.nz

Tel: 86-13242008155

References:

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.