A convolutional approach to quality monitoring for laser manufacturing

Carlos Gonzalez-Val (), Adrian Pallas, Veronica Panadeiro and Alvaro Rodriguez
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Carlos Gonzalez-Val: AIMEN Technology Centre
Adrian Pallas: AIMEN Technology Centre
Veronica Panadeiro: University of A Coruña
Alvaro Rodriguez: University of A Coruña

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 3, No 15, 789-795

Abstract: Abstract The extraction of meaningful features from the monitoring of laser processes is the foundation of new non-destructive quality inspection methods for the manufactured pieces, which has been and remains a growing interest in industry. We present ConvLBM, a novel approach to monitor Laser Based Manufacturing processes in real-time. ConvLBM uses a Convolutional Neural Network model to extract features and quality indicators from raw Medium Wavelength Infrared coaxial images. We demonstrate the ability of ConvLBM to represent process dynamics, and predict quality indicators in two scenarios: dilution estimation in Laser Metal Deposition, and location of defects in laser welding processes. Obtained results represent a breakthrough in the 3D printing of large metal parts, and in the quality control of welding processes. We are also releasing the first large dataset of annotated images of laser manufacturing.

Keywords: Neural-networks; Convolutional-neural-networks; Quality-control; Laser-cladding; Laser-welding (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (10)

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DOI: 10.1007/s10845-019-01495-8

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