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Enhancement of drilling safety and quality using online sensors and artificial neural networks
Tien-I Liu1, Akihiko Kumagai, Chongchan Lee
1Department of Mechanical Engineering, California State University, Sacramento, CA 95819, USA. liut@csus.edu
Abstract:
Cutting force sensors and neural networks have been used for the occupational safety of the drilling process. The drill conditions have been online classified into 3 categories: safe, caution, and danger. This approach can change the drill just before its failure. The inputs to neural networks include drill size, feed rate, spindle speed, and features that were extracted from drilling force measurements. The outputs indicate the safety states. This detection system can reach a success rate of over 95%. Furthermore, the one misclassification during online tests was a one-step ahead pre-alarm that is acceptable from the safety and quality viewpoint. The developed online detection system is very robust and can be used in very complex manufacturing environments.