Machine-learning-based surface defect detection and categorisation in high-precision foundry

Iker Pastor-López, I. Santos, Aitor Santamaría-Ibirika, Mikel Salazar, Jorge de-la-Peña-Sordo, P. G. Bringas
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引用次数: 12

Abstract

Foundry is an important industry that supplies key castings to other industries where they are critical. Hence, foundry castings are subject to very strict safety controls to assure the quality of the manufactured castings. One of the type of flaws that may appear in the castings are defects on the surface; in particular, our work focuses in inclusions, cold laps and misruns. We propose a new approach that detects imperfections on the surface using a segmentation method that marks the regions of the casting that may be affected by some of these defects and, then, applies machine-learning techniques to classify the regions in correct or in the different types of faults. We show that this method obtains high precision rates.
高精度铸造中基于机器学习的表面缺陷检测与分类
铸造是一个重要的行业,为其他行业提供关键铸件。因此,铸造铸件受到非常严格的安全控制,以确保制造铸件的质量。铸件中可能出现的缺陷类型之一是表面缺陷;我们的工作重点是夹杂物、冷圈和跑错。我们提出了一种新的方法,该方法使用分割方法检测表面上的缺陷,该方法标记出可能受某些缺陷影响的铸件区域,然后应用机器学习技术对正确或不同类型故障的区域进行分类。结果表明,该方法具有较高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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