Automatic detection of defective crankshafts by image analysis and supervised classification
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Automatic detection of defective crankshafts by image analysis and supervised classificationAutor(es)
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2019Cita bibliográfica
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
É version de
https://doi.org/10.1007/s00170-019-03819-7
Resumo
[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.
Palabras chave
Automotive industry
Forged crankshaft
Quality control
Image analysis
Supervised classification
Forged crankshaft
Quality control
Image analysis
Supervised classification
Descrición
Versión final aceptada de: https://doi.org/10.1007/s00170-019-03819-7 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
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