Classification of weld defects using machine vision using convolutional neural network

Kanthalakshmi S, Nikitha M. S, P. G
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Abstract

Welding is an important aspect in commercial use of almost every industry. Because weld flaws can cause irregularities or inconsistencies during welding process, welding quality control is a critical step in ensuring the product’s quality and overall longevity. This study focuses on recognizing contamination defects, lack of fusion defects, or if the weld belongs to the good weld category among the defects that occur during the welding process. This category categorization is carried out for the Convolutional Neural Network (CNN) algorithm and the accuracy metric is obtained to evaluate the efficiency of the algorithm for the 3 – class dataset. According to this research, the pure CNN approach gave an accuracy result of 96.1%.
基于卷积神经网络的机器视觉焊缝缺陷分类
焊接是几乎所有工业中商业应用的一个重要方面。由于焊接缺陷会导致焊接过程中的不规则或不一致,因此焊接质量控制是确保产品质量和整体寿命的关键步骤。本研究的重点是在焊接过程中出现的缺陷中识别污染缺陷,缺乏熔合缺陷,或者焊缝是否属于良好焊缝类别。对卷积神经网络(CNN)算法进行了分类,并获得了准确率度量来评估算法对3类数据集的效率。根据本研究,纯CNN方法的准确率为96.1%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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