Model-based deep learning for additive manufacturing: New frontiers and applications. (August 2021)

• Record Type: Journal Article
• Authors: Ghungrad, Suyog; Gould, Benjamin; Soltanalian, Mojtaba; Wolff, Sarah Jeannette; Haghighi, Azadeh
• Is Part Of: Manufacturing letters. Volume 29(2021)
• Page Start: 94
• Page End: 98
• Publication Date: 2021-08
• Subjects: Additive manufacturing -- Model-based deep learning -- Artificial intelligence -- Data scarcity -- Process and material variability; Manufacturing industries -- Periodicals; Production engineering -- Periodicals; Manufacturing industries; Periodicals; 670
• DOI: 10.1016/j.mfglet.2021.07.008
• Languages: English
• ISSNs: 2213-8463

Abstract

Abstract: Artificial intelligence has created disruptive possibilities in additive manufacturing towards smarter design, process control, and quality assurance. Nonetheless, the scarcity of data in additive manufacturing significantly limits the wide adoption of artificial intelligent techniques. In this work, we propose the deployment of a novel artificial intelligent structure called model-based deep learning in the context of additive manufacturing which can address scenarios with scarce data but an available underlying iterative mathematical/inference model. Several immediate applications of this technique in the additive manufacturing research as well as a proof of concept on temperature profile prediction in metal AM process are presented.