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. (C) 2019 Published by Elsevier Ltd on behalf of CIRP.
Caggiano, A., Zhang, J., Alfieri, V., Caiazzo, F., Gao, R., Teti, R. (2019). Machine learning-based image processing for on-line defect recognition in additive manufacturing. CIRP ANNALS, 68(1), 451-454 [10.1016/j.cirp.2019.03.021].
Machine learning-based image processing for on-line defect recognition in additive manufacturing
Caggiano, Alessandra
;
2019-01-01
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. (C) 2019 Published by Elsevier Ltd on behalf of CIRP.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
