Journal
OPTICS AND LASER TECHNOLOGY
Volume 130, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.optlastec.2020.106347
Keywords
Neural network; Image segmentation; Spatter extraction; Laser-based powder bed fusion
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Funding
- Key-Area Research and Development Program of Guangdong Province, China [2018B090905001]
- National Key Research and Development Program of China, China [2017YFB1103900]
- Natural Science Foundation of Guangdong Province, China [2018A030313044]
- Science and Technology Program of Shenzhen, China [JCYJ20170816171733384]
- Hubei Provincial Natural Science Foundation of China, China [2017CFB657]
- Fundamental Research Funds for the Central Universities, China [2042018kf0240]
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In situ monitoring of spatter signatures is often employed to improve product quality during laser-based powder bed fusion (LPBF). This paper describes a novel neural network (NN) based image segmentation method for spatter extraction with a simple labeling process and high accuracy results. Use of a 290-1100 nm waveband high-speed camera allowed capturing images with more complete spatter signatures and a more complex background compared with previous LPBF studies. Conventional image segmentation approaches are inadequate to perform spatter extraction because of the complex background. The proposed NN-based image segmentation method split images into a block grid and segmented each block using a parallel convolutional neural network (CNN) and a thresholding neural network (TNN), which permitted extracting 80.48% of spatters in only 70 ms. Furthermore, the ability to extract spatters connected to a molten pool distinguishes the proposed NN-based image segmentation method from conventional image segmentation approaches.
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