Screw fastenings account for a quarter of all assembly operations and automation of the process is highly desirable. This paper presents a novel strategy for monitoring this manufacturing process, focusing on the insertion of self-tapping screws. An artificial neural network (ANN), using “Torque-versus-Insertion-Depth” signature signals as input, is designed to distinguish between successful and failed insertions. The ANN is first tested using simulation data from an analytical model for screw insertions, and then validated using experimental torque signals obtained from an electric screwdriver. The results demonstrate that ANNs can effectively monitor the screw fastening process and cope with a wide range of insertion cases interpolating for unseen insertion signals.
Monitoring of Self-Tapping Screw Fastenings Using Artificial Neural Networks
Contributed by the Manufacturing Engineering Division for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received December 05, 2002; revised April 21, 2004. Associate Editor: K. Danai.
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Althoefer , K., Lara , B., and Seneviratne , L. D. (March 21, 2005). "Monitoring of Self-Tapping Screw Fastenings Using Artificial Neural Networks ." ASME. J. Manuf. Sci. Eng. February 2005; 127(1): 236–243. https://doi.org/10.1115/1.1831286
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