用于自动检测钣金冲压件裂纹缺陷的深度学习方法

IF 1.9 3区 工程技术 Q3 ENGINEERING, MANUFACTURING
Aru Ranjan Singh, Thomas Bashford-Rogers, Kurt Debattista, Sumit Hazra
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引用次数: 0

摘要

钣金冲压工艺主要用于为从白色家电制造到汽车和航空航天等一系列行业生产大批量产品。然而,这种工艺很容易出现缺陷。由于冲压产品中可能会出现许多潜在缺陷,因此通常需要使用人工检测人员进行检测。然而,人工检测既不可靠又昂贵,尤其是在生产速度与冲压速度相当的情况下。本研究探讨了基于 CNN 的冲压缺陷自动检测。研究进行了两组实验。所有实验都获得了较高的分类准确率、召回率和精确率,证明了 CNN 方法在金属板冲压过程中进行缺陷检测的可行性。此外,这项研究还发现,在有限的数据中,混杂因素可能是一个挑战。第二个实验进一步探讨了颈部小缺陷、刺眼光线和反光对缺陷检测的影响。观察结果表明,该模型难以识别被反射遮挡的缺陷,尤其是颈部小缺陷。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Approach for automatic detection of split defects on sheet metal stamping parts
Sheet metal stamping processes are used primarily for high-volume products produced for a range of sectors, from white goods manufacturing to the automotive and aerospace sectors. However, the process is susceptible to defects. Due to the numerous potential defects that may arise in the stamping product, human inspectors are often deployed for their detection. However, they are unreliable and expensive, especially when operating at production speeds equivalent to the stamping rate. This study investigate CNN-based automatic inspection for stamping defects. The study carried out two sets of experiments. All the Experiments yielded high classification accuracy, recall and precision demonstrating the viability of the CNN method for defect detection in the sheet metal stamping process. Additionally, this study revealed that in limited data confounding factors can be a challenge. The second experiment further explored the impact of small neck defects, harsh lighting and reflections on defect detection. The observations indicated that the model struggled to identify defects occluded by reflections, particularly small neck defects.
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来源期刊
CiteScore
5.10
自引率
30.80%
发文量
167
审稿时长
5.1 months
期刊介绍: Manufacturing industries throughout the world are changing very rapidly. New concepts and methods are being developed and exploited to enable efficient and effective manufacturing. Existing manufacturing processes are being improved to meet the requirements of lean and agile manufacturing. The aim of the Journal of Engineering Manufacture is to provide a focus for these developments in engineering manufacture by publishing original papers and review papers covering technological and scientific research, developments and management implementation in manufacturing. This journal is also peer reviewed. Contributions are welcomed in the broad areas of manufacturing processes, manufacturing technology and factory automation, digital manufacturing, design and manufacturing systems including management relevant to engineering manufacture. Of particular interest at the present time would be papers concerned with digital manufacturing, metrology enabled manufacturing, smart factory, additive manufacturing and composites as well as specialist manufacturing fields like nanotechnology, sustainable & clean manufacturing and bio-manufacturing. Articles may be Research Papers, Reviews, Technical Notes, or Short Communications.
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