Smart Additive Manufacturing: Product and Process Qualification through Innovation in Design, Modeling, Monitoring, Machine Learning, Metrology, and Materials Science

Department of Mechanical Engineering, College of Engineering, Iowa State University, Ames, IA, United States
3D printing; Additive manufacturing; Advanced materials; Computational modeling; Digital thread/Digital twin; Machine leaning and deep learning; Manufacturing processes; Welding

Achieving consistent and repeatable quality is the key to scalability of additive manufacturing (AM). This special issue focuses on novel approaches toward rapid qualification of AM products and processes. The topics of interest can potentially encompass aspects ranging from Process Innovation, Design, Digital Twins, Modeling, Monitoring, Machine Learning, Metrology (including non-destructive evaluation), and Materials Science (including testing). Contributors are urged to augment methodological rigor with practically-relevant experiments.
Artificial intelligence-driven defect detection and localization in metal 3D printing using convolutional neural networks
Gaussian process-based interpretable prediction of melt track morphology through melt pool in additive manufacturing