Neural networks implementations to control real-time manufacturing systems

摘要

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 systemsand 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 mainbarrier 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 anoutput 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: thearchitectures, 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 fitsthe 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 replacethe need for long training processes and saves on computation resources.