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

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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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Acknowledgement

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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Correspondence to Kangil Kim.

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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

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