The automation of screw insertions represents a highly desirable task. An important part of the automation process is the monitoring of the insertion. This paper presents an application of artificial neural network for monitoring this common manufacturing procedure. The research focuses on the insertion of seytapping screws. Artificial neural networks have been employed to distinguish between successful and failed insertions. The networks under investigation use radial basis functionsfor the computation of the data. A range of networks, diflering in size, has been implemented and thoroughly tested. Results and evaluations of the networks from the experiments are presented:
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