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Machine learning-based image processing for on-line defect recognition in additive manufacturing

 
: Caggiano, A.; Zhang, J.; Alfieri, V.; Caiazzo, F.; Gao, R.; Teti, R.

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CIRP Annals. Manufacturing Technology 68 (2019), Nr.1, S.451-454
ISSN: 0007-8506
Englisch
Zeitschriftenaufsatz
Fraunhofer J LEAPT ()

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.

: http://publica.fraunhofer.de/dokumente/N-558998.html