A Convolutional Approach to Quality Monitoring for Laser Manufacturing
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A Convolutional Approach to Quality Monitoring for Laser ManufacturingAutor(es)
Fecha
2019-10-09Cita bibliográfica
Gonzalez-Val, C., Pallas, A., Panadeiro, V. et al. A convolutional approach to quality monitoring for laser manufacturing. J Intell Manuf 31, 789–795 (2020). https://doi.org/10.1007/s10845-019-01495-8
Resumen
[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.
Palabras clave
Neural networks
Convolutional-neural-networks
Quality control
Laser cladding
Laser welding
Convolutional-neural-networks
Quality control
Laser cladding
Laser welding
Versión del editor
Derechos
Atribución 4.0
ISSN
0956-5515
1572-8145
1572-8145