论文标题
使用卷积神经网络进行工业质量检查
Diamond Abrasive Electroplated Surface Anomaly Detection using Convolutional Neural Networks for Industrial Quality Inspection
论文作者
论文摘要
电镀钻石磨料工具需要在金属表面上的镍涂层，以进行磨料粘合和零件功能。预计电镀镍涂层的磨料工具将具有高质量的零件性能，其镍涂层的厚度为磨料中值直径的50％至60％，镍层的均匀性，电镀层表面上的磨蚀性分布和明亮的光泽。为此，将电镀参数相应设置。由于钻石的光折射，分散性质和反射性明亮的镍表面，使用光学检查仪器对这些磨料电镀零件缺陷的工业质量检查非常具有挑战性。这项挑战所带来的困难要求零件是用主观且昂贵的眼镜手动检查质量的。在这项研究中，我们在生产线中使用卷积神经网络（CNN）模型来检测磨料电镀零件异常，从而使我们能够修复或消除生产链中处于不良状态的零件或元素，并最终降低了手动质量检查成本。我们使用了744个样品来训练我们的模型。我们的模型成功地确定了99％以上的零件。关键字：人工智能，异常检测，工业质量检查，电镀，钻石磨料工具
Electroplated diamond abrasive tools require nickel coating on a metal surface for abrasive bonding and part functionality. The electroplated nickel-coated abrasive tool is expected to have a high-quality part performance by having a nickel coating thickness of between 50% to 60% of the abrasive median diameter, uniformity of the nickel layer, abrasive distribution over the electroplated surface, and bright gloss. Electroplating parameters are set accordingly for this purpose. Industrial quality inspection for defects of these abrasive electroplated parts with optical inspection instruments is extremely challenging due to the diamond's light refraction, dispersion nature, and reflective bright nickel surface. The difficulty posed by this challenge requires parts to be quality inspected manually with an eye loupe that is subjective and costly. In this study, we use a Convolutional Neural Network (CNN) model in the production line to detect abrasive electroplated part anomalies allowing us to fix or eliminate those parts or elements that are in bad condition from the production chain and ultimately reduce manual quality inspection cost. We used 744 samples to train our model. Our model successfully identified over 99% of the parts with an anomaly. Keywords: Artificial Intelligence, Anomaly Detection, Industrial Quality Inspection, Electroplating, Diamond Abrasive Tool
