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In order to produce products with constant quality, manufacturing systems need to be monitored for any unnatural deviations in the state of the process. Control charts have an important role in solving quality control problems; nevertheless, their effectiveness is strictly dependent on statistical assumptions that in real industrial applications are frequently violated. In contrast, neural networks can elaborate huge amounts of noisy data in real time, requiring no hypothesis on statistical distribution of monitored measurements. This important feature makes neural networks potential tools that can be used to improve data analysis in manufacturing quality control applications. In this paper, a neural network system, which is based on an unsupervised training phase, is presented for quality control. In particular, the adaptive resonance theory (ART) has been investigated in order to implement a model-free quality control system, which can be exploited for recognising changes in the state of a manufacturing process. The aim of this research is to analyse the performances of ART neural network under the assumption that predictable unnatural patterns are not available. To such aim, a simplified Fuzzy ART neural algorithm is firstly discussed, and then studied by means of extensive Monte Carlo simulation. r

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In this paper a solution to a manipulation control problem (target identification and grasping) using a red platform in combination with a vision system is proposed. The task for the end-effector is to approach a randomly placed spherical object of known size. The control law is approximated by using an approach based on fuzzy logic. The controller determines the parameters of the target (a spherical object acquired from the vision system mounted on the manipulator) using a vision algorithm. The fuzzy rules are built in a supervised way, after studying the behavior of the system. Experimental results obtained using an industrial manipulator (PUMA 560 with 6 DOF) are presented.

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