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Applying Improved Optical Recognition with Machine Learning on Sorting Cu Impurities in Steel Scrap
Journal of Sustainable Metallurgy ( IF 2.4 ) Pub Date : 2020-12-07 , DOI: 10.1007/s40831-020-00300-8
Zhijiang Gao , S. Sridhar , D. Erik Spiller , Patrick R. Taylor

Abstract

Cu impurities in scrap, originating from motors and wires, limit the efficiency of recycling steel scrap, especially for shredded automobile scrap, due to the occurrence of surface hot shortness during hot working resulting from high Cu content. Considering the distinct difference of color between metal Cu and Fe and the potential differences between shapes of shreds depending on Cu content, optical recognition was explored as a method for detecting and separating Cu-rich shreds. In order to optimize detection and minimize effects of surface inhomogeneity, etc., convolutional neural networks (CNNs) were adopted to improve the optical recognition of shredded scrap obtained from industrial sources. The results show that the proposed neural network achieves significantly better recognition on Cu impurities and results in a reduction of Cu content. An optimized accuracy of 90.6% could be obtained for recognizing Cu impurities through applied CNNs architecture with dataset of cropped photographs. This results in an overall reduction of Cu impurities from 0.272 to 0.087 wt% in steel scrap, if the identified Cu-rich parts were removed.

Graphical Abstract



中文翻译:

改进的光学识别和机器学习技术在废钢中铜杂质分类中的应用

摘要

废料中来自电机和电线的铜杂质会限制回收钢废料的效率,尤其是对于汽车碎废料,这是由于高含量的铜在热加工过程中出现了表面热脆性。考虑到金属铜和铁之间颜色的明显差异以及切丝形状之间的潜在差异(取决于铜含量),探索了光学识别方法作为检测和分离富含铜的切丝的方法。为了优化检测并最小化表面不均匀性等的影响,采用了卷积神经网络(CNN)来改善从工业来源获得的切碎废料的光学识别能力。结果表明,所提出的神经网络对铜杂质的识别效果显着提高,并导致铜含量的降低。通过应用带有裁剪照片数据集的CNNs体系结构,识别铜杂质的最佳准确度为90.6%。如果去除了确定的富铜零件,则可将废钢中的铜杂质从0.272 wt%减少到0.087 wt%。

图形概要

更新日期:2020-12-07
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