Realization of a force inspection system with skilled inspector-like sensing ability

本文介绍了一种探头反力特性质量检测系统。该系统，直到现在被认为是难以实现的，自动化的检查方法，利用检查员的手指的触摸。小波变换和神经网络（NN）的方法被应用到系统的探头学习检查员的手指判断。我们提供了一个输入层的神经网络与33个节点对应的时间序列的反作用力的探针和一个输出层与一个节点对应的判断;是一个无缺陷的，有缺陷的，或无法判断。从实验结果，该系统的有效性已被澄清。提出的小波神经网络系统学习熟练检验员的检验结果.回归分析和神经网络的方法被应用到系统的探头学习检查员的手指判断。我们提供了一个输入层的神经网络与四十三个节点对应的时间序列之间的差异的反应力的测试探针和最佳质量的探针，和一个输出层与三个节点对应的判断。从实验结果，该系统的有效性已被澄清。提出的回归分析-神经网络系统对熟练检验员的检验结果进行学习.提出了一种图形表示系统。回归分析-神经网络系统的所有性能都显示在图形表示系统中。图形显示系统支持非熟练检测员的检测技能培训。经过更好训练的神经网络将进一步提高探头检测的速度和准确性，从而降低检测成本。

This paper looks at a system for inspection of the quality ofa probe's reaction force characteristics. This system, until now considered difficult to realize, automates the inspection method utilizing the touching of an inspector's finger.1. Wavelet transformation and a neural network (NN) approach is applied to the system for probes to learn an inspector's finger judgment. We provide an input layer of a NN with thirty-three nodes corresponding to a time series of reaction forces of a probe and an output layer with one node corresponding to ajudgment ; being one of non-defective, defective, or unable to judge. From experimental results, the effectiveness of the system has been clarified. The inspection results of skilled inspectors were learned by the proposed wavelet-neural network system.2. Regression analysis and a NN approach is applied to the system for probes to learn an inspector's finger judgment. We provide an input layer of a NN with forty-three nodes corresponding to a time series difference between reaction forces of the test probe and the best quality probe, and an output layer with three nodes corresponding to a judgment. From experimental results, the effectiveness of the system has been clarified. The inspection results of skilled inspectors were learned by the proposed regression analysis -neural network system.3. A graphical presentation system is proposed. All performances of the regression analysis-neural network system are displayed in the graphical presentation system. The graphical presentation syste*m supports unskilled inspector' s training in inspection skills.In time better trained neural networks will further improve the speed and accuracy of probe inspections, thereby reducing inspection costs.