PARÇA ISKARTALARININ MAKİNE ÖĞRENMESİ KULLANILARAK AZALTILMASI: OTOMOTİV SEKTÖRÜNDE BİR UYGULAMA

Year 2022, Volume: 27 Issue: 1, 291 - 308, 30.04.2022

Emine Eş Yürek Betül Yağmahan Burak Celal Akyüz Ebubekir Sıddık Samast Nezire Dilan Cetrez

https://doi.org/10.17482/uumfd.963176

Abstract

Bu çalışmada, enjektör imal eden bir firmanın taşlama makinesindeki insan faktörüne bağlı ıskartaların azaltılması amaçlanmıştır. İnsan faktörüne bağlı ıskartalar, makinenin taş değişimi, arıza gibi çeşitli nedenlerle durmasından sonra makine operatörünün, makine ve ürün parametrelerinde yaptığı ayarlamalardan kaynaklanmaktadır. Iskartaların azaltılması amacıyla iki aşamalı bir çözüm yaklaşımı önerilmiştir. İlk aşamada, makine öğrenmesi kullanılarak kalite tahminlenmiştir. Kalite tahminleme, bir sınıflandırma problemi olarak ele alınmıştır. Farklı sınıflandırma algoritmaları modellenerek en yüksek performansa sahip Destek Vektör Makineleri (DVM) algoritması seçilmiştir. İkinci aşamada ise, DVM kullanılarak kaliteli ürün ile sonuçlanması beklenen uygun parametre değerleri belirlenmiştir. Güncel veri dikkate alınarak parametre değerlerinin otomatik olarak revize edilmesi ve operatöre sunulması amacıyla bir öneri sistemi geliştirilmiştir. Bu öneri sistemi ile, taşlama işleminin insan etmenine olan bağlılığının ortadan kaldırılarak dijitalleşmesi amaçlanmıştır.

Keywords

Iskarta azaltma, Kalite sınıflandırma, Makine öğrenmesi, Parametre ayarı, Dijitalleşme

Reducing Part Rejects Using Machine Learning: A Case Study In Automotive Industry

Abstract

This study aims to reduce the human-based rejects in the grinding machine of a company that manufactures injectors. Human-based rejects are caused by the operator's adjustments to the machine and product parameters after the engine stops due to some reasons such as stone change or breakdown. A two-stage solution approach is proposed to reduce rejects. In the first stage, quality is predicted using machine learning. Quality prediction is addressed as a classification problem. Various classification algorithms are modeled, and the outperforming Support Vector Machine (SVM) is selected. In the second stage, the proper parameter values expected to result in a quality product are determined using SVM. A system is developed to automatically revise the parameter values by considering the updated data and recommend them to the operator. This system aims to digitalize the grinding process by removing its dependence on the human factor.

Keywords

Reject reduction, Quality classification, Machine learning, Parameter adjustment, Digitalization

References

• 1. Adesanya A., Abdulkareem A. ve Adesina L.M. (2020) Predicting extrusion process parameters in Nigeria cable manufacturing industry using artifical neural network, Heliyon, 6(7).
• 2. Arif F., Suryana N. ve Hussin B. (2013) A data mining approach for developing quality prediction model in multi-stage manufacturing, International Journal of Computer Applications, 69(22), 35-40.
• 3. Bai Y., Sun Z., Deng, L., Li L., Long J. ve Li C. (2018) Manufacturing quality prediction using intelligent learning approaches: A comparative study, Sustainability, 10(1), 85.
• 4. Chou P.H., Wu M.J. ve Chen K.K. (2010) Integrating support vector machine and genetic algorithm to implement dynamic wafer qualiy prediction system, Expert Systems with Application, 37(6), 4413-4424.
• 5. Ciurana J., Arias G. ve Ozel T. (2009) Neural network modeling and particle swarm optimization (PSO) of process parameters in pulsed laser micromachining of hardened AISI H13 steel, Materials and Manufacturing Processes, 24, 358-368.
• 6. Cunningham P. ve Delany S.J. (2020) k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples). arXiv preprint arXiv:2004.04523.
• 7. Doğan A. ve Birant D. (2021) Machine learning and data mining in manufacturing, Expert Systems with Applications, 166, 114060.
• 8. Feng W., Sun J., Zhang L., Cao C. ve Yang Q. (2016) A support vector machine based naive Bayes algorithm for spam filtering, IEEE 35th International Performance Computing and Communications Conference (IPCCC), 1-8.
• 9. Gao H., Zhang Y., Fu Y., Mao T., Zhou H ve Li D. (2018) Process parameters optimization using a novel classification model for plastic injection molding, Intelligent Journal of Advanced Manufacturing Technology, 94, 357-370.
• 10. Garcia V., Sanchez J.S., Rodriguez-Picon L.A., Mendez-Gonzalez L.C. ve Ochoa-Dominguez H.J., Using regression models for predicting the product quality in a tubing extrusion process, Journal of Intelligent Manufacturing, 30, 2535-2544, 2019.
• 11. Han J., Kamber M. ve Pei J. (2012) Data Mining: Concepts and Techniques, Morgan Kaufmann, Waltham, MA.
• 12. Jurkovic Z., Cukor G., Brezocnik M. ve Brajkovic T. (2018) A comparison of machine learning methods for cutting parameters prediction in high speed turning process, Journal of Intelligent Manufacturing, 29, 1683-1693.
• 13. Köksal G., Batmaz İ. ve Testik M.C. (2011) A review of data mining applications for quality improvement in manufacturing industry, Expert Systems with Applications, 38(10), 13448-13467.
• 14. Kumar D.P., Amgoth T. ve Annavarapu C.S.R. (2019) Machine learning algorithms for wireless sensor networks: A survey, Information Fusion, 49, 1-25.
• 15. Lahouar A. ve Slama J.B.H. (2015) Random forests model for one day ahead load forecasting, IREC2015 The Sixth International Renewable Energy Congress, IEEE, 1-6, March.
• 16. Larose D.T. (2015) Data Mining and Predictive Analytics, John Wiley & Sons.
• 17. Okaro I.A., Jayasinghe S., Sutcliffe C., Black K., Paoletti P. ve Green P.L. (2019) Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning, Additive Manufacturing, 27, 42-53.
• 18. Mohammadi P. ve Wang Z. J. (2016) Machine learning for quality prediction in resistant material manufacturing process, IEEE Canadian Conference on Electrical and Computer Engineering, Vancouver-Canada, 15-18 May.
• 19. Paturi U.M.R. ve Cheruku S. (2020) Application and performance of machine learning techniques in manufacturing sector from the past two decades: A review, Materials Today: Proceedings, (in press).
• 20. Peres R.S., Barata J., Leitao P. ve Garcia G. (2019) Multistage quality control using machine learning in the automotive industry, IEEE Access, 7, 79908-79916.
• 21. Power D.M.W. (2011) Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation, Journal of Machine Learning Technologies, 2(1), 37-63.
• 22. Ramulu V., Ramana E.V. ve Kumar N.K. (2019) Predictive modelling for quality prediction and assurance of extrusion blow moding, International Journal of Innovative Technology and Exploring Engineering, 8(11), 1364-1368.
• 23. Sapounas I., Vosniakos G.C. ve Papazetis G. (2020) A simulation-based robust methodology for operator guidance on injection moulding machine settings, International Journal on Interactive Design and Manufacturing, 14, 519-533.
• 24. Schorr S., Möller M., Heib J., Fang S. ve Bahre D. (2020) Quality prediction of reamed bores based on process data and machine learning algorithm: A contribution to a more sustainable manufacturing, Procedia Manufacturing, 43, 519-526.
• 25. Silva J.A, Abellan-Nebot J.V., Siller H.R. ve Guedea-Elizalde F. (2014) Adaptive control optimisation system for minimising production cost in hard milling operations, International Journal of Computer Integrated Manufacturing, 27(4), 348-360.
• 26. Siryani J., Tanju B. ve Eveleigh T.J. (2017) A machine learning decision-support system improves the internet of things’ smart meter operations, IEEE Internet of Things Journal, 4(4), 1056-1066.
• 27. Strasser S., Tripathi S. ve Kerschbaumer R. (2018) An approach for adaptive parameter setting in manufacturing processes, 7th International Conference on Data Science, Technology and Application, Porto-Portugal, 24-32, 26-28 July.
• 28. Tharwat A. (2019) Parameter investigation of support vector machine classifier with kernel functions, Knowledge and Information Systems, 61(3), 1269-1302.
• 29. Teixidor D., Grzenda M., Bustillo A. ve Ciurana J. (2015) Modeling pulsed laser micromachining of micro geometries using machine-learning techniques, Journal of Intelligent Manufacturing, 26, 801-814.
• 30. Weichert D., Link P., Stoll A., Ihlenfeldt S. ve Wrobel S. (2019) A review of machine learning for the optimization of production processes, The International Journal of Advanced Manufacturing Technology, 104, 1889-1902.
• 31. Widodo A. ve Yang B.S. (2007) Support vector machine in machine condition monitoring and fault diagnosis, Mechanical Systems and Signal Processing, 21(6), 2560-2574.
• 32. Zhang X.D. (2020) Machine learning. In: A Matrix Algebra Approach to Artificial Intelligence, Springer, Singapore, 223-440.