Abstract
The automation of screw insertions is a highly desirable task. An important part of the automation process is the monitoring of the insertion. The paper presents an application of artificial neural networks for monitoring this common manufacturing procedure. The research focuses on the insertion of self-tapping screws. A radial basis artificial neural network is employed to distinguish between successful and failed insertions. The network is tested with tasks of increasing complexity using simulation data. The approach is then validated with the use of experimental data, and the tests results are presented.
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