Journal of Intelligent Manufacturing

, Volume 23, Issue 5, pp 1893–1902 | Cite as

Using artificial intelligence to predict surface roughness in deep drilling of steel components

  • Andres BustilloEmail author
  • Maritza Correa


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.


Deep drilling Bayesian networks Supervised classification Minimum quantity lubrication (MQL) Surface roughness 


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  2. 2.Centro de Automática y Robótica (CAR) UPM-CSICArganda del Rey, MadridSpain
  3. 3.Escuela Politécnica SuperiorUniversidad Autónoma de MadridMadridSpain

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