Bayesian Network for quality control in the drilling process

Nowadays, the aeronautic industry requires the automation of certain processes to minimize economic costs and to optimize resources, ensuring at the same time the quality of these processes. One of the most important tasks in this sector is the drilling process, the main problem of which lies in the occurrence of burr. Today there is a manual burr elimination task subsequent to drilling and previous to riveting which guarantees the quality of the process, where the permissible burr size is set at under 127 microns, imposed by aeronautic industry. This task increases manufacturing costs and it must be replaced by a monitoring system in order to detect automatically and on-line when the burr is outside this limit and to reduce the number of holes to be removed. This article shows the efficacy of Bayesian networks for predicting burr generation in the drilling process, which is an easy model to interpret and to integrate into the final system. Moreover, the article provides the most influential parameters in the generation of burr in the process.