使用卷积神经网络对高度变形的波纹板进行现场分类
Maciej Rogalka1, Jakub Krzysztof Grabski1, Tomasz Garbowski2
1Institute of Applied Mechanics, Poznan University of Technology, 60-965 Poznan, Poland.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
卷积神经网络 (CNN) 可以准确地分类波纹板图像,即使有变形. 这种先进的图像分析提高了质量控制,减少了包装行业的浪费.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
- 工业工程 工业工程 工业工程
背景情况:
- 由于其保护性质和对环境的好处,波纹纸板被广泛用于包装.
- 板的完整性受到设计,材料和环境因素的影响,因此质量控制至关重要.
- 现有的质量控制方法可能无法充分解决复杂的变形.
研究的目的:
- 开发和评估一种使用卷积神经网络 (CNN) 分析和分类波纹板图像的新方法.
- 评估CNN在识别变形和确保波纹板质量的有效性.
- 探索机器学习在先进材料质量评估中的潜力.
主要方法:
- 开发了一种具有高分辨率能力的专用成像设备,用于捕获波纹板的详细横截图图像.
- 从七种不同类型的波板样本获取图像数据.
- 优化卷积神经网络 (CNN) 模型以提高图像分类性能.
主要成果:
- 提出的基于CNN的方法在分类波板图像方面取得了高准确性,最佳性能超过99%.
- 该方法在识别变形方面表现出有效性,尽管一些具有挑战性的样本需要进一步的识别精细化.
- 取得了各种波板类型的成功分类.
结论:
- 在波板生产中,CNN提供了一种复杂而准确的质量控制和缺陷检测方法.
- 这项研究有助于提高产品质量和减少包装行业的浪费.
- 这项研究为机器学习在各种行业的材料质量评估中的更广泛应用奠定了基础.
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