Machine learning-based image processing for on-line defect recognition in additive manufacturing
By A. Caggiano, Jianjing Zhang, Vittorio Alfieri, Fabrizia Caiazzo, Robert X. Gao, Roberto Teti. CIRP annals, 2019
379 citations recorded by the source corpus.
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.
Subfields: Automotive Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/98274fa1ef4d85831c5ebdf506af31eaac4548f4
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In Automotive Engineering
In Mechanical Engineering
Citations in the corpus
- Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, positive): “[108], a vision system based on deep convolutional neural network (DCNN) has been built for process fault detection in SLM.”
- Modeling process–structure–property relationships in metal additive manufacturing: a review on physics-driven versus data-driven approaches (2020, methodology): “(A) Flowchart of the Bi-stream DCNN for recognition of defects induced by improper SLM process conditions employed by Caggiano et al [79].”