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

被引：1

作者：

Muniategui, Ander ^{[1
]}

Ander del Barrio, Jon ^{[1
]}

Angulo Vinuesa, Xabier ^{[1
]}

Masenlle, Manuel ^{[1
]}

Garcia de la Yedra, Aitor ^{[1
]}

Moreno, Ramon ^{[1
]}

机构：

[1] LORTEK, Arranomendia Kalea 4A, Ordizia 20240, Spain

来源：

ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2019, PT II | 2019年 / 11507卷

关键词：

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

D O I：

10.1007/978-3-030-20518-8_15

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：174 / 185

页数：12

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