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Bio-intelligent selective laser melting system based on convolutional Neural networks for in-process fault identification

 
: Angelone, R.; Caggiano, A.; Teti, R.; Spierings, A.; Staub, A.; Wegener, K.

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Volltext ()

Procedia CIRP 88 (2020), S.612-617
ISSN: 2212-8271
Conference on Intelligent Computation in Manufacturing Engineering (ICME) <13, 2019, Gulf of Naples>
Englisch
Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation
Fraunhofer J LEAPT ()

Abstract
This research work focuses on the development of a bio-intelligent additive manufacturing system based on Selective Laser Melting (SLM) technology in the framework of the Biological Transformation in Manufacturing. With the objective to provide the SLM system with cognition, decision making and self-learning capabilities inspired by human intelligence and cognitive skills, a machine learning approach using convolutional neural networks for in-process fault identification based on automatic image processing is presented. An in-process sensor monitoring system based on a camera installed on the SLM machine tool is employed to acquire images of the scanned layers with the final aim to timely identify possible faults and realize an adaptive layer-by-layer control loop in which the process is self-adjusted based on learning.

: http://publica.fraunhofer.de/dokumente/N-602617.html