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- W2006996616 abstract "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." @default.
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- W2006996616 date "1998-10-01" @default.
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- W2006996616 title "Neural networks implementations to control real-time manufacturing systems" @default.
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- W2006996616 doi "https://doi.org/10.1016/s0951-5240(98)00013-5" @default.
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