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.
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This work was supported by the Korea Institute of Industrial Technology as “Development of high efficient production technology for high purity titanium powder and additive manufacturing processing technology (KITECH EO-18-0012)”.
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Kwon, O., Kim, H.G., Ham, M.J. et al. A deep neural network for classification of melt-pool images in metal additive manufacturing. J Intell Manuf 31, 375–386 (2020). https://doi.org/10.1007/s10845-018-1451-6
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DOI: https://doi.org/10.1007/s10845-018-1451-6