A Bayesian network for burr detection in the drilling process

One of the most important processes in the aeronautical sector is drilling. The main problem associated with drilling is burr. There is a tolerance level for this burr and it cannot exceed 127 microns, which would provoke structural damage and other problems. Currently, the burr elimination task is carried out visually and manually with the aim of guaranteeing quality in the process. However, it is an expensive procedure and needs to be replaced by a motorized system capable of automatically detecting in which holes the burr exceeds the permitted level and has to be eliminated or reduced. The paper presents a burr prediction model for high speed drilling in dry conditions on aluminium (Al 7075-T6), based on a Bayesian network learned from a set of experiments based on parameters taken from the internal signal of the machine and parameters from the condition process. The paper shows the efficiency and validity of the model in the prediction of the apparition of burr during the drilling and compares the results with other data-mining techniques.