An artificial neural network for fault detection in the assembly of thread-forming screws

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This paper presents a method, based on a two-layer dynamic Elman neural network, for detecting faults in the assembly of thread-forming screws. Using torque measurements, the method provides a high degree of reliability in detecting assembly faults. The ability of neural networks to learn and to generalize creates an efficient detection system when there is limited or distorted information available about the assembly process.

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