Automatic detection of defective crankshafts by image analysis and supervised classification
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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.
KeywordsAutomotive industry Forged crankshaft Quality control Image analysis Supervised classification
The authors would also like to thank CIE Galfor S.A. and Vigotec4 companies for their help in the experimental data collection.
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).
- 2.Bradski G (2000) The openCV library. Dr. Dobb’s Journal of Software Tools 25:120–126Google Scholar
- 3.Chandna P, Chandra A (2009) Quality tools to reduce crankshaft forging defects: an industrial case study. Int J Ind Syst Eng 3(1):27–37Google Scholar
- 5.Cook RJ (2008) Kappa. Wiley Encyclopedia of Clinical Trials pp 1?7Google Scholar
- 7.Fisher RA (1936) The use of multiple measurements in taxonomic problems. Annals of Genetics 7(2):179–188Google Scholar
- 11.Iborra A, Alvarez B, Jiménez C, Fernández-Merono JM, Fernández C, Suardíaz J (2000) Automated Visual Inspection system (AVI) for crankshaft production processes. Eur J Mech Environ Eng 45(1):29–34Google Scholar
- 12.Jensen FV (1996) An introduction to Bayesian networks, vol 210. Springer, BerlinGoogle Scholar
- 14.Kotsiantis S, Kanellopoulos D, Pintelas P (2006) Handling imbalanced datasets: a review. GESTS International Transactions on Computer Science and Engineering 30(1):25–36Google Scholar
- 15.Kotsiantis SB, Zaharakis I, Pintelas P (2007) Supervised machine learning: a review of classification techniques. Emerging Artificial Intelligence applications in Computer Engineering 160:3–24Google Scholar
- 20.McLaren K (1976) XIII The development of the CIE 1976 (l* a* b*) uniform colour space and colour-difference formula. Color Technol 92(9):338–341Google Scholar
- 22.Pater BV, Thakkar HR, Mehta SB (2014) Review of analysis on forging defects for quality improvement in forging industries. Journal of Emerging Technologies and Innovative Research 1(7):871–876Google Scholar
- 23.R Development Core Team: R (2018) A language and environment for statistical computing. R Foundation for Statistical Computing. Available at http://www.R-project.org
- 24.Seber GAF, Lee AJ (2012) Linear regression analysis, vol 329. Wiley, New YorkGoogle Scholar
- 26.Thottungal AP, Sijo MT (2013) Controlling measures to reduce rejection rate due to forging defects. International Journal of Scientific and Research Publications 3(3):238–243Google Scholar
- 30.Wold H (2006) Partial least squares. Encyclopedia of Statistical Sciences 9:1Google Scholar