This letter presents a Convolutional Neural Network (CNN), named WelDeNet, customized to classify welding defects, such as lack of penetration (LP), cracks (CR), porosity (PO) and no defect (ND), by inspecting digitalized radiographic images. A new dataset that collects 24,407 images representing welding defects is also presented. WelDeNet consists of 14 cascaded convolutional layers and achieves a test accuracy of 99.5 %. When hardware implemented within the Raspberry Pi 3B + board, WelDeNet exhibits an inference time of only 134 ms, with CPU and memory utilizations of just 51 % and 47 MB, thus offering a promising solution easy-to-integrate in a real industrial environment.

Welding defects classification through a Convolutional Neural Network

Perri S.
;
Spagnolo F.;Frustaci F.;Corsonello P.
2023-01-01

Abstract

This letter presents a Convolutional Neural Network (CNN), named WelDeNet, customized to classify welding defects, such as lack of penetration (LP), cracks (CR), porosity (PO) and no defect (ND), by inspecting digitalized radiographic images. A new dataset that collects 24,407 images representing welding defects is also presented. WelDeNet consists of 14 cascaded convolutional layers and achieves a test accuracy of 99.5 %. When hardware implemented within the Raspberry Pi 3B + board, WelDeNet exhibits an inference time of only 134 ms, with CPU and memory utilizations of just 51 % and 47 MB, thus offering a promising solution easy-to-integrate in a real industrial environment.
2023
Convolutional Neural Network
Dataset
Welding defect
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/357122
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