Reaction Force Inspection System Using Neural Network Classifier

People recognize the quality of a product while in operation by hands or fingers. The operation feeling by hands or fingers is one of the important indexes for the high-grade products. However, skilled inspectors are used to inspect some products because automatic inspection is technologically difficult or too high in cost. This paper looks at a system for inspection of the quality of a product’s reaction force characteristics. This system, until now considered difficult to realize, automates the inspection method utilizing the touching of an inspector´s finger. Neural network classifier is applied to the system for products to learn an inspector´s finger judgment. We provide an input layer of a neural network classifier with nodes corresponding to time-and frequency-domain features of reaction forces of a product and an output layer with three nodes corresponding to a judgment; being one of non-defective, defective, or unable to judge. From experimental results, the effectiveness of the proposed neural network classifier has been clarified.