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.

A new reconfigurable parts feeding system using arrayed vibratory units made by stereolithography has been developed and investigated. The parts feeding motion is achieved by vibration of an inclined pin grid array using piezoelectric actuators. Experiments on moving objects have been successfully performed. The vibratory unit has a large number of inclined pins, each a height of 3 mm and diameter of 0.5 mm. The motions of the parts feeding system has been demonstrated experimentally, and lifetime measurements demonstrated the long-term stability of the system. The functionality of the photopolymer resin pins is unaffected through 110 days continuous use.

This paper proposes a 3D graphics simulation environment for the analysis and evaluation of reconfigurable manufacturing systems. The reconfigurable manufacturing systems consist of transport robots; input stations, output stations, movable manufacturing cells, and objects to process. The layout of the manufacturing cells and the allocation of transport robots are optimized using particle swarm optimization. The effectiveness of the simulator is validated through simulation results of several manufacturing cases.

Looks at a system for inspection of the quality of a 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. Wavelet transformation and a neural network (NN) approach are 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 a judgment; being one of non-defective, defective, or unable to judge. From experimental results, the effectiveness of the system has been clarified.