Burr Detection Using Image Processing in Milling Workpieces

  1. Virginia Riego del Castillo 1
  2. Lidia Sánchez-González 1
  3. Laura Fernández-Robles 1
  4. Manuel Castejón-Limas 1
  1. 1 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

Book:
15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020
  1. Álvaro Herrero (coord.)
  2. Carlos Cambra (coord.)
  3. Daniel Urda (coord.)
  4. Javier Sedano (coord.)
  5. Héctor Quintián (coord.)
  6. Emilio Corchado (coord.)

Publisher: Springer Suiza

ISBN: 978-3-030-57801-5,  978-3-030-57802-2

Year of publication: 2021

Pages: 751-759

Congress: International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO (15. 2020. Burgos)

Type: Conference paper

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Abstract

Manufacturing processes require to satisfy quality standards in the produced parts. In particular, the edge finishing must be burr-free, avoiding that it yields different problems such as wasting time removing them what increases the production cost and time. A burr can be noticed microscopically, but it can contain imperfections or evidence of poor piece design. In order to detect automatically this imperfections and to evaluate the quality of the edge finishing, this paper proposes a complete vision based method using image processing and linear regression. With the calculated function, the slope is isolated and compared to obtain quality assessment thresholds. Results validate the good performance of the proposed method to differenciate three types of burrs.

Data source: Dialnet