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Automatic detection of defective crankshafts by image analysis and supervised classification

  • Beatriz RemeseiroEmail author
  • Javier Tarrío-Saavedra
  • Mario Francisco-Fernández
  • Manuel G. Penedo
  • Salvador Naya
  • Ricardo Cao
ORIGINAL ARTICLE
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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.

Keywords

Automotive industry Forged crankshaft Quality control Image analysis Supervised classification 

Notes

Acknowledgements

The authors would also like to thank CIE Galfor S.A. and Vigotec4 companies for their help in the experimental data collection.

Funding

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).

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Research Group VARPA, CITIC, Departamento de ComputaciónUniversidade da CoruñaA CoruñaSpain
  2. 2.Department of Computer ScienceUniversidad de OviedoGijónSpain
  3. 3.Research Group MODES, CITIC and ITMATI, Departamento de MatemáticasUniversidade da CoruñaA CoruñaSpain

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