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

Submission Deadline: 01 December 2025
Special Issue Editors
Prahalada Rao
Virginia Polytechnic Institute and State University, Blacksburg, United States
Interests:

Modeling, Monitoring, and Analytics of Metal Additive Manufacturing

Azadeh Haghighi
University of Illinois-Chicago, Chicago, United States
Interests:

smart decision-support systems, cyber manufacturing, product design

Tuhin Mukherjee
Department of Mechanical Engineering, College of Engineering, Iowa State University, Ames, IA, United States
Interests:

3D printing; Additive manufacturing; Advanced materials; Computational modeling; Digital thread/Digital twin; Machine leaning and deep learning; Manufacturing processes; Welding

Profile:

Tuhin Mukherjee is an Assistant Professor in the Department of Mechanical Engineering at Iowa State University, joining the faculty in 2023. He earned his B.Tech. in Mechanical Engineering from West Bengal University of Technology (2012), his M.Tech. in Mechanical Engineering from the Indian Institute of Technology Bombay (2014), and his Ph.D. in Materials Science and Engineering from The Pennsylvania State University (2019). Between 2019 and 2023 he held post-doctoral positions, most recently at the University of Toronto.
Dr. Mukherjee leads the Science and Technology of Advanced Materials Processing (STAMP) group. His research integrates advanced experiments, mechanistic modeling, machine learning, and digital-twin concepts to understand and control the process–structure–property–performance relationships in additive manufacturing and welding of metals, with the overarching goal of reducing defects and improving reliability

Special Issue Information

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.           

Keywords
Qualification
Quality Assurance
Digital Twins
Materials Science
Machine Learning
Metrology
Monitoring
Sensing
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Materials Science in Additive Manufacturing, Electronic ISSN: 2810-9635 Published by AccScience Publishing