| 摘要: |
| 目的 根据金属薄板印刷质量控制的发展趋势和金属薄板印刷的特点， 提出了基于CCD机器视觉的金属薄板印刷机墨层厚度在线检测系统。方法 通过实验获取信号条的墨层厚度 d与实时图片的 RGB 值， 分析由 RGB 值推得的亮度 L*、 饱和度C*ab和色调角 hab与墨层厚度间的对应关系； 通过极限学习机（ELM）对亮度、 饱和度、 色调角与墨层厚度数据进行回归拟合， 建立墨层厚度预测模型。结果 墨层厚度 d与 L*，C*ab和 hab之间存在显著的对应关系。拟合的平均相对误差为2.27%， 最大相对误差为7.33%， 测量误差低于8%。结论 较好地实现了在线墨层厚度检测， 具有很好的实际应用价值。 |
| 关键词: CCD机器视觉 金属薄板 墨层厚度 极限学习机 |
| DOI： |
| 分类号:TS802.4 |
| 基金项目:江苏省前瞻性联合研究项目 （BY20140004-03） |
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| Online Detection of the Ink Film Thickness of Metal Sheet Printing Based on CCD Method |
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MA Sai, CAO Chun-ping, SUN Yu
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Nanjing University of Science and Technology， Nanjing 210094， China
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| Abstract: |
| Objective The detection of ink film thickness of metal sheet printing is one of the key technologies in the printing quality control field. According to the development trend and the characteristics of metal sheet printing, an online detection system was proposed for the ink film thickness of metal sheet printing. Methods First, the ink film thickness d and real-time image's RGB values of the signal bars were obtained through experiments. Then, the relationships of the basic attributes of colors (lightness (L*), saturation (C*ab) and hue (hab)) calculated from the RGB values and the thickness of the ink film were analyzed and the significant corresponding relationships among them were found. Last, an ink film thickness prediction model was established using Extreme Learning Machine (ELM). Results The simulation results showed that the accuracy of the ELM method was much higher than BP and RBF methods with the average relative error of 2.27%, and the maximum relative error of 7.33%. Conclusion It was proved that this detection system could well realize the function of ink film thickness detection and could be applied to the actual testing process. |
| Key words: CCD machine vision metal sheet printing ink film thickness Extreme Learning Machine |
