Please find attached the Call for Papers for the Special Issue on Probabilistic Digital Twins in Additive Manufacturing.
Guest Editors
- Zequn Wang, Assistant Professor, Michigan Technological University, USA, zequnw@mtu.edu
- Zhen Hu, Assistant Professor, University of Michigan-Dearborn, USA, zhennhu@umich.edu
- Moon Seung Ki, Associate Professor, Nanyang Technological University, Singapore, skmoon@ntu.edu.sg
- Hong-Zhong Huang, Professor, University of Electronic Science and Technology of China, China, hzhuang@uestc.edu.cn
- Qi Zhou, Associate Professor, Huazhong University of Science and Technology, China, qizhou@hust.edu.cn
Aims & Scope
Additive manufacturing (AM) has made enormous progress over the past decade, as it is capable of producing complex parts with significantly less fabrication constraints compared to existing manufacturing technologies over a broad dimensional scale. Complicated AM process variability is one of the greatest obstacles in performance evaluation and quality control of additively manufactured materials and products, and thus hinders widespread implementation of AM techniques. Digital twin, as a digital replica of a production system or process, has great potential in overcoming quality variability and reliability issues in AM processes. With the development of probabilistic digital twins in AM and uncertainty management techniques, it becomes possible to reduce the computational burden for multi-scale modeling and realize reliable AM processes by taking advantage of large volumes of in situ sensor data to optimize process parameters, detect, and prevent process faults.