Neural networks implementations to control real-time manufacturing systems

The main objective of advanced manufacturing control techniques is to provide efficient and accurate tools in order to control machines and manufacturing systems in real-time operations. Recent developments and implementations of expert systems and neural networks support this objective. This research explores the use of neural networks to control several manufacturing systems in real-time operations: robot manipulators, tool changes, conveyor systems and machine faults diagnosis. The main barrier to wide implementation of neural networks is the huge computation resources (times and capacities) required to train a network. This research represents the use of a multi-layer architecture of networks (input layer, several hidden layers and an output layer) to define single-valued inter-relationships between system participants and to avoid the need for long training processes. The use of neural networks to control the above-mentioned systems was evaluated from the following parameters: the architectures, network training methods, efficiencies and accuracies of networks to perform the task of control, Several conclusions related to neural network implementations to manufacturing systems were produced: (1) the multi-layer architecture fits the complexity of manufacturing systems; (2) neural networks are efficient to control real-time operations of machines; (3) machines which were controlled by neural networks performed accurate results; and (4) the use of several hidden layers can replace the need for long training processes and saves on computation resources. (C) 1998 Published by Elsevier Science Ltd. All rights reserved.