A deep neural network for classification of melt-pool images in metal additive manufacturing

Ohyung Kwon, Hyung Giun Kim, Min Ji Ham, Wonrae Kim, Gun-Hee Kim, Jae-Hyung Cho, Nam Il Kim and Kangil Kim ()
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Ohyung Kwon: Korea Institute of Industrial Technology
Hyung Giun Kim: Korea Institute of Industrial Technology
Min Ji Ham: Korea Institute of Industrial Technology
Wonrae Kim: Korea Institute of Industrial Technology
Gun-Hee Kim: Korea Institute of Industrial Technology
Jae-Hyung Cho: WINFORSYS
Nam Il Kim: WINFORSYS
Kangil Kim: Konkuk University

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 2, No 8, 375-386

Abstract: Abstract By applying a deep neural network to selective laser melting, we studied a classification model of melt-pool images with respect to 6 laser power labels. Laser power influenced to form pores or cracks determining the part quality and was positively-linearly dependent to the density of the part. Using the neural network of which the number of nodes is dropped with increasing the layer number achieved satisfactory inference when melt-pool images had blurred edges. The proposed neural network showed the classification failure rate under 1.1% for 13,200 test images and was more effective to monitor melt-pool images because it simultaneously handled various shapes, comparing with a simple calculation such as the sum of pixel intensity in melt-pool images. The classification model could be utilized to infer the location to cause the unexpected alteration of microstructures or separate the defective products non-destructively.

Keywords: Additive manufacturing; Powder bed fusion; Selective laser melting; Melt-pool classification; Deep neural network (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (16)

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DOI: 10.1007/s10845-018-1451-6

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