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Research on Surface Defect Detection Method of Metal Workpiece Based on Machine Learning

机译：基于机器学习的金属工件表面缺陷检测方法研究

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摘要

Some uncontrollable defects will occur on the surface of metal workpieces during processing. The existence of surface defects not only affects the appearance of the finished product, but also affects the quality to a certain extent. Surface defect detection of metal workpieces can effectively improve product quality and production efficiency, and is an important link in the process of product quality control. Although there are many different types of surface defect detection methods, in the actual production process, due to the characteristics of multiple types and irregular distribution of the surface defects of metal workpieces, in most cases, manual inspection or simple machine inspection is still used to detect the surface of metal workpieces. Defect inspections often lead to missed inspections and false inspections. The defect detection efficiency, accuracy and precision of metal workpieces still need to be further improved. This paper studies the method of detecting the surface defects of metal workpieces based on deep learning, provides the surface defect recognition accuracy and defect detection rate of metal workpieces, and provides references for the staff and scientific researchers engaged in metal workpiece defect detection.
机译：在加工过程中，金属工件的表面将发生一些无法控制的缺陷。表面缺陷的存在不仅影响成品的外观，而且影响了一定程度的质量。表面缺陷检测金属工件可以有效提高产品质量和生产效率，是产品质量控制过程中的重要环节。虽然有许多不同类型的表面缺陷检测方法，在实际生产过程中，由于多种类型的特点和金属工件表面缺陷的特点，在大多数情况下，手动检查或简单的机器检查仍然习惯检测金属工件的表面。缺陷检查通常会导致错过的检查和错误检查。金属工件的缺陷检测效率，准确性和精度仍然需要进一步提高。本文研究了基于深度学习检测金属工件表面缺陷的方法，提供了金属工件的表面缺陷识别精度和缺陷检测率，并为工作人员和从事金属工件缺陷检测的科研人员提供参考。

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