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Automatic detection of defective crankshafts by image analysis and supervised classification
dc.contributor.author | Remeseiro, Beatriz | |
dc.contributor.author | Tarrío-Saavedra, Javier | |
dc.contributor.author | Francisco-Fernández, Mario | |
dc.contributor.author | Penedo, Manuel | |
dc.contributor.author | Naya, Salvador | |
dc.contributor.author | Cao, Ricardo | |
dc.date.accessioned | 2023-11-27T16:03:08Z | |
dc.date.available | 2023-11-27T16:03:08Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Remeseiro, B., Tarrío-Saavedra, J., Francisco-Fernández, M. et al. Automatic detection of defective crankshafts by image analysis and supervised classification. Int J Adv Manuf Technol 105, 3761–3777 (2019). https://doi.org/10.1007/s00170-019-03819-7 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/34340 | |
dc.description | Versión final aceptada de: https://doi.org/10.1007/s00170-019-03819-7 | es_ES |
dc.description | This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect postacceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s00170-019-03819-7 | es_ES |
dc.description.abstract | [Abstract]: A crankshaft is a mechanical component of an engine that performs a conversion of an alternative movement of a piston in a rotational motion of a shaft. It is a critical part and one of the most expensive of an engine. Defects in crankshafts may imply serious failures and, consequently, possible injuries and high costs. Therefore, the manufacture quality is of primordial importance for security and economic reasons. Nowadays, the quality control of crankshafts manufactured by forging in the automotive industry consists, among others, in inspecting them at the final process, using a magnetic particle procedure. This slow and highly stressful technique depends on operators and consumes many human resources, time, and space. This paper presents a methodology to automatically detect defective crankshafts. The proposed procedure is based on digital image analysis techniques, to extract a set of representative features from crankshaft images. Statistical techniques for supervised classification are used to classify the images into defective or not. The experimental results demonstrated the good performance of the proposed method with a classification accuracy over 99%, a 10% higher than the one obtained by manual inspection. Therefore, working time and personnel required for this task can be reduced when using this automated procedure. | es_ES |
dc.description.sponsorship | This work has been partially supported by the Xunta de Galicia (Centro Singular de Investigación de Galicia ED431G/01). Additionally, the research of Ricardo Cao, Mario Francisco-Fernández, Salvador Naya and Javier Tarrío-Saavedra has been partially supported by MINECO grants MTM2014-52876-R and MTM2017-82724-R, and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2016-015); whilst the research of Manuel G. Penedo has been partially supported by grants Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2016-047), all the previous grants through the ERDF. This work has been also supported by FORJACEMIC project (Research into new processes and micro-alloyed steels for hot forging of automotive crankshafts). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2016-015 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2016-047 | es_ES |
dc.language.iso | eng | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2014-52876-R/ES/INFERENCIA ESTADISTICA COMPLEJA Y DE ALTA DIMENSION: EN GENOMICA, NEUROCIENCIA, ONCOLOGIA, MATERIALES COMPLEJOS, MALHERBOLOGIA, MEDIO AMBIENTE, ENERGIA Y APLICACIONES INDUSTRI | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSION | es_ES |
dc.relation.isversionof | https://doi.org/10.1007/s00170-019-03819-7 | |
dc.relation.uri | https://doi.org/10.1007/s00170-019-03819-7 | es_ES |
dc.rights | Todos os dereitos reservados. All rights reserved. | es_ES |
dc.subject | Automotive industry | es_ES |
dc.subject | Forged crankshaft | es_ES |
dc.subject | Quality control | es_ES |
dc.subject | Image analysis | es_ES |
dc.subject | Supervised classification | es_ES |
dc.title | Automatic detection of defective crankshafts by image analysis and supervised classification | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.doi | 10.1007/s00170-019-03819-7 |
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