用于焊接缺陷精确检测和分类的先进 ANN 技术

Faza Ardan Kusuma, Muhamad Fatchan, Ahmad Turmudi Zy, Universitas Pelita Bangsa
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引用次数: 0

摘要

本研究详细介绍了用于检测和分类焊接缺陷的人工神经网络(ANN)算法的实施过程。通过调整大小、自动调整方向、翻转、旋转和注释等技术,共处理了 558 幅焊接工件图像,最终将数据集扩展到 1,288 幅图像。特征提取确定了 12,000 个数据点中的 24 个特征,然后将这些特征浓缩为 5,735 个数据点,供 ANN 模型使用。该模型采用了 100 个隐藏层、ReLU 激活函数和 L-BFGS-B 求解器,运行了 200 次迭代。该配置取得了近乎完美的结果,曲线下面积(AUC)、分类准确率和 F1 分数等指标的平均精度为 0.97。这些结果表明,ANN 模型在检测和分类焊接缺陷方面具有很高的效率,突出了其在焊接行业质量保证方面的潜在应用。对特定缺陷类型(包括气孔、飞溅、裂纹和缺口)的进一步研究可进一步提高检测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advanced ANN Techniques for Precise Detection and Classification of Welding Defects
The implementation of the artificial neural network (ANN) algorithm for detecting and classifying welding defects is detailed in this study. A total of 558 welding workpiece images were processed using techniques such as resizing, auto-orientation, flipping, rotation, and annotation, ultimately expanding the dataset to 1,288 images. Feature extraction identified 24 traits across 12,000 data points, which were then condensed to 5,735 data points for the ANN model. The model employed 100 hidden layers, the ReLU activation function, and the L-BFGS-B solver, running for 200 iterations. The configuration achieved near-perfect results, with metrics such as the area under the curve (AUC), classification accuracy, and F1 score averaging a precision of 0.97. These outcomes demonstrate the ANN model's high efficacy in detecting and classifying welding defects, underscoring its potential application for quality assurance in the welding industry. Further investigation into specific defect types, including porosity, spatter, cracks, and undercuts, could further improve detection accuracy.
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