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One Dimensional Fourier Transform on Deep Learning for Industrial Welding Quality Control

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11507)

Abstract

This paper presents a method for industrial welding quality control. It focuses on the detection of Lack of Fusions (LoF) in joined parts produced in a rotational welding process. The solutions are based on the LeNet and AlexNet networks that are extended with previous convolutional layers based on 1D-pDFT (1D Polar Discrete Fourier Transform) and Gabor filters. The new layers add to the network the ability to deal with the images by means of knowledge arising from the physical process. In this paper a detailed description of the optical setup and the procedure to obtain defectives samples is also given.

Keywords

1D Polar Discrete Fourier Transform Deep learning AlexNet Gabor filter Industrial application Quality control 

Notes

Acknowledgments

This work has been funded by the project KK-2018/00104 (Departamento de Desarrollo Económico e Infraestructuras del Gobierno Vasco, Programa ELKARTEK-Convovatoria 2018).

Contributions

AM, JADB and RM developed the image analysis software and contributed mainly in the writing of the paper. XAV, MM and AGDY developed the inspection system (optics, lighting and automation). AM and JADB made the manual curation for classification by visual inspection of captured images.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.LORTEKOrdiziaSpain

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