Real-time fuzzy logic control for maximising the tool life of small-diameter drills
Fuzzy Sets and Systems
Machine Learning - Special issue on learning with probabilistic representations
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Learning Bayesian Networks
A Bayesian network model for surface roughness prediction in the machining process
International Journal of Systems Science
Expert Systems with Applications: An International Journal
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
Journal of Intelligent Manufacturing
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A predictive model is presented to optimize deep drilling operations under high speed conditions for the manufacture of steel components such as moulds and dies. The input data include cutting parameters and axial cutting forces measured by sensors on the milling centres where the tests are performed. The novelty of the paper lies in the use of Bayesian Networks that consider the cooling system as an input variable for the optimization of roughness quality in deep drilling operations. Two different coolant strategies are tested: traditional working fluid and MQL (Minimum Quantity Lubrication). The model is based on a machine learning classification method known as Bayesian networks. Various measures used to assess the model demonstrate its suitability to control this type of industrial task. Its ease of interpretation is a further advantage in comparison with other artificial intelligence tools, which makes it a user-friendly application for machine operators.