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Comparison of machine learning algorithms for optimization and improvement of process quality in conventional metallic materials

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Abstract

This paper presents a particular problem dealing with the apparition of burr during the drilling process in the aeronautic industry. This burr cannot exceed a height limit of 127 μm as set out by the aeronautical guidelines and must be eliminated before riveting. If this is not performed, it can cause structural damage which would constitute a danger due to the lack of safety. Moreover, the industry needs to find an automated and optimised process in which the drilling and deburring can be carried out in real time, eliminating those other unnecessary tasks, in order to obtain high-quality pieces. The work presents the applicability of data mining and machine learning techniques so as to obtain a real time burr detection model. This model could be implanted in the computer numerical control of the machine allowing the whole process to be automated and optimised. These techniques can be applied to other types of processes.

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Authors and Affiliations

  1. Diagnosis and Prediction Technologies Unit, Tekniker, Eibar, Gipuzkoa, Spain

    Susana Ferreiro

  2. University of the Basque Country UPV-EHU, San Sebastian, Gipuzkoa, Spain

    Basilio Sierra

Authors
  1. Susana Ferreiro
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  2. Basilio Sierra
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Correspondence to Susana Ferreiro.

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Ferreiro, S., Sierra, B. Comparison of machine learning algorithms for optimization and improvement of process quality in conventional metallic materials. Int J Adv Manuf Technol 60, 237–249 (2012). https://doi.org/10.1007/s00170-011-3578-x

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  • DOI: https://doi.org/10.1007/s00170-011-3578-x

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