Machine learning-based image processing for on-line defect recognition in additive manufacturing. Issue 1 (2019)

• Record Type: Journal Article
• Authors: Caggiano, Alessandra; Zhang, Jianjing; Alfieri, Vittorio; Caiazzo, Fabrizia; Gao, Robert; Teti, Roberto
• Is Part Of: CIRP annals. Volume 68:Issue 1(2019)
• Page Start: 451
• Page End: 454
• Publication Date: 2019
• Subjects: Machine learning -- Additive manufacturing -- Fault recognition; Production engineering -- Research -- Periodicals; 670.5
• DOI: 10.1016/j.cirp.2019.03.021
• Languages: English
• ISSNs: 0007-8506

Abstract

Abstract: A machine learning approach for on-line fault recognition via automatic image processing is developed to timely identify material defects due to process non-conformities in Selective Laser Melting (SLM) of metal powders. In-process images acquired during the layer-by-layer SLM processing are analyzed via a bi-stream Deep Convolutional Neural Network-based model, and the recognition of SLM defective condition-related pattern is achieved by automated image feature learning and feature fusion. Experimental evaluations confirmed the effectiveness of the machine learning method for on-line detection of defects due to process non-conformities, providing the basis for adaptive SLM process control and part quality assurance.