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A Holistic Approach to Part Quality Prediction in Injection Molding Based on Machine Learning

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Advances in Polymer Processing 2020

Abstract

All plastics processing companies have to fulfill the objectives of time, cost and quality. Against this background, those producing in high wage countries are especially challenged, because superior part quality is often the only possibility to prevail in competition. Since this leads to high expenses on quality assurance, for some time already efforts have been made to predict the quality of injection molded parts from process data using machine learning algorithms. However, these did not yet prevail in industry, mainly for two reasons: First, because of the inevitable learning effort that is required to set up a quality prediction model and second, because of the complexity in the application. Current research in the field of transfer learning aiming to shorten learning phases addresses the first challenge. In this paper, we present a holistic approach for the data analysis steps that are necessary once process and quality data have been generated, aiming to minimize the application effort for the operator. This includes the development and application of suitable algorithms for automatic selection of data, process features as well as machine learning algorithms including hyper-parameter optimization and model adaption. Combining the two approaches could bring quality prediction one significant step forward to successful industry application. Beyond this, the presented approach is universally applicable and can therefore be used for other plastics processing methods as well.

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Notes

  1. 1.

    In total 9 downtimes of 5, 15 and 25 min (3 times each), taking place every 100 cycles.

  2. 2.

    Variation of re-grind material fraction from 0 to 100% in steps of 25%, 200 cycles each. Used material: Polypropylene LyondellBasell Moplen HP501H.

  3. 3.

    The parts were weighed with a Sartorius Entris 153I-1S balance with 1 mg reproducibility, part length were extracted algorithmically from images taken with a Canon Eos 5D Mark III DSLR with EF 70-200mm f/4L USM objective.

References

  1. Hopmann, C., Michaeli, W.: Einführung in die Kunststoffverarbeitung, 7th edn. Hanser, Munich (2015)

    Book  Google Scholar 

  2. Hopmann, C., Michaeli, W., Greiff, H., et al.: Technologie des Spritzgießens, 4th edn. Hanser, Munich (2017)

    Book  Google Scholar 

  3. Standard DIN 24450: Maschinen zum Verarbeiten von Kunststoffen und Kautschuk. Beuth, Berlin (1987)

    Google Scholar 

  4. Schiffers, R.: Verbesserung der Prozessfähigkeit beim Spritzgießen durch Nutzung von Prozessdaten und eine neuartige Schneckenhubführung. PhD thesis (2009)

    Google Scholar 

  5. Gierth, M.: Methoden und Hilfsmittel zur prozessnahen Qualitätssicherung beim Spritzgießen von Thermoplasten. PhD thesis (1992)

    Google Scholar 

  6. Hanning, D.: Continuous Process Control. Qualitätssicherung im Kunststoffverarbeitungs-prozess auf Basis statistischer Prozessmodelle. PhD thesis (1994)

    Google Scholar 

  7. Häußler, J.: Eine Qualitätssicherungsstrategie für die Kunststoffverarbeitung auf der Basis künstlicher Neuronaler Netzwerke. PhD thesis (1994)

    Google Scholar 

  8. Vaculik, R.: Regelung der Formteilqualität beim Spritzgießen auf Basis statistischer Prozessmodelle. PhD thesis (1996)

    Google Scholar 

  9. Al-Haj Mustafa, M.: Modellbasierte Ansätze zur Qualitätsregelung beim Kunststoffspritzgießen. PhD thesis (2000)

    Google Scholar 

  10. Schnerr, O.: Automatisierung der Online-Qualitätsüberwachung beim Kunststoffspritzgießen. PhD thesis (2000)

    Google Scholar 

  11. Walter, A.: Methoden des prozessnahen Qualitätsmanagements in der Kunststoffverarbeitung. PhD thesis (2000)

    Google Scholar 

  12. Liedl, P., Haag, G., Müller, H., et al.: Spitzenqualität mit kurzen Zyklen. Kunststoffe 2, 38–40 (2010)

    Google Scholar 

  13. Hopmann, C., Theunissen, M., Heinisch, J.: Von der Simulation in die Maschine – objektivierte Prozesseinrichtung durch maschinelles Lernen. In: VDI Jahrestagung Spritzgießen, Baden-Baden (2018)

    Google Scholar 

  14. Hopmann, C., Theunissen, M., Wipperfürth, J., et al.: Prozesseinrichtung durch maschinelles Lernen. Kunststoffe 6, 36–41 (2018)

    Google Scholar 

  15. Hopmann, C., Wahle, J., Theunissen, M., et al.: Flexibilisierung der Spritzgießfertigung durch Digitalisierung. In: Kunststoffindustrie 4.0 – 29. Internationales Kolloquium Kunststofftechnik, pp. 76–88 (2018)

    Google Scholar 

  16. Tercan, H., Guajardo, A., Heinisch, J., et al.: Tranfer-learning: bridging the gap between real and simulation data for machine learning in injection molding. Procedia CIRP 72, 185–190 (2018)

    Article  Google Scholar 

  17. Hopmann, C., Bibow, P., Heinisch, J.: Internet of Plastics Processing. IPC Madison, USA (2019)

    Google Scholar 

  18. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(3), 1157–1182 (2003)

    Google Scholar 

  19. Charest, M., Finn, R.; Dubay, R.: Integration of artificial intelligence in an injection molding process for on-line process parameter adjustment. In: Annual IEEE International Systems Conference (SysCon), pp. 1–6. IEEE, Vancouver, Canada (2018)

    Google Scholar 

  20. Gao, H., Zhang, Y., Zhou, X., Li, D.: Intelligent methods for the process parameter determination of plastic injection molding. Front. Mech. Eng. 13(1), 85–95 (2018)

    Article  Google Scholar 

  21. Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley, New York (2001)

    MATH  Google Scholar 

  22. Arlot, S., Celisse, A.: A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40–79 (2010)

    Article  MathSciNet  Google Scholar 

  23. Pudil, P., Novovičová, J., Kittler, J.: Floating search methods in feature selection. Pattern Recogn. Lett. 15(11), 1119–1125 (1994)

    Article  Google Scholar 

  24. Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)

    Article  Google Scholar 

  25. Hall, M.A.: Correlation-based feature selection for machine learning. PhD thesis (1999)

    Google Scholar 

  26. Kira, K., Rendell, L.A.: The feature selection problem: traditional methods and a new Algorithm. In: AAAI’92 Proceedings of the Tenth National Conference on artificial Intelligence, pp. 129–134. AAAI, San Jose, California, USA (1992)

    Google Scholar 

  27. Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. In: IEEE Computer Society Bioinformatics Conference, pp. 523–528, IEEE, Stanford, USA (2003)

    Google Scholar 

  28. Hall, M. A., Smith, L. A.: Practical feature subset selection for machine learning. In: ACSC’98 Proceedings of the 21st Australasian Computer Science Conference, pp. 181–191. ACSC, Perth, Australia (1998)

    Google Scholar 

  29. Russell, S.J., Norvig, P.: Artificial intelligence, 2nd edn. Prentice Hall, Pearson Education, Upper Saddle River (2003)

    MATH  Google Scholar 

  30. Alpaydin, E.: Introduction to machine learning, 2nd edn. MIT Press, Cambridge (2010)

    MATH  Google Scholar 

  31. Hagan, M.T., Demuth, H.B., Beale, M.H.: Neural Network Design, 1st edn. PWS, Boston (1996)

    Google Scholar 

  32. Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)

    Article  MathSciNet  Google Scholar 

  33. Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees, 1st edn. CRC Press, Boca Raton (1984)

    MATH  Google Scholar 

  34. Biau, G., Devroye, L., Dujmović, V., Krzyżak, A.: An affine invariant k-nearest neighbor regression estimate. J. Multivar. Anal. 112, 24–34 (2012)

    Article  MathSciNet  Google Scholar 

  35. Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning. Data Mining, Inference, and Prediction, 2nd edn. Springer, New York (2017)

    MATH  Google Scholar 

  36. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  37. Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning, 3rd edn. MIT Press, Cambridge (2008)

    MATH  Google Scholar 

  38. Urban, D., Mayerl, J.: Angewandte Regressionsanalyse: Theorie, Technik und Praxis, 5th edn. Springer VS, Wiesbaden (2018)

    Book  Google Scholar 

  39. Claesen, M., De Moor, B.: Hyperparameter search in machine learning. In: MIC 2015: The XI Metaheuristics International Conference, pp. 1–5, MIC, Agadir, Morocco (2015)

    Google Scholar 

  40. Bengio, Y.: Practical recommendations for gradient-based training of deep architectures. Lecture Notes in Computer Science 7700 LECTURE NO, pp. 437–478 (2012)

    Google Scholar 

  41. Ito, K., Nakano, R.: Optimizing Support Vector regression hyperparameters based on cross-validation. In: Proceedings of the International Joint Conference on Neural Networks, pp. 2077–2082. IEEE, Portland, USA (2003)

    Google Scholar 

  42. Matignon, R.: Data Mining using SAS Enterprise Miner, 1st edn. Wiley-Interscience, Hoboken (2007)

    Book  Google Scholar 

  43. Wilson, D.R., Martinez, T.R.: Improved heterogeneous distance functions. J. Artif. Intell. Res. 6, 1–34 (1997)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Universität Duisburg-Essen, Lotharstraße, 1, 47057, Duisburg, Germany

    Alexander Schulze Struchtrup, Dimitri Kvaktun & Reinhard Schiffers

Authors
  1. Alexander Schulze Struchtrup
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  2. Dimitri Kvaktun
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  3. Reinhard Schiffers
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Corresponding author

Correspondence to Alexander Schulze Struchtrup .

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Editors and Affiliations

  1. Institute of Plastics Processing (IKV), RWTH Aachen University, Aachen, Germany

    Christian Hopmann

  2. Institute of Plastics Processing (IKV), RWTH Aachen University, Aachen, Germany

    Rainer Dahlmann

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Schulze Struchtrup, A., Kvaktun, D., Schiffers, R. (2020). A Holistic Approach to Part Quality Prediction in Injection Molding Based on Machine Learning. In: Hopmann, C., Dahlmann, R. (eds) Advances in Polymer Processing 2020. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-60809-8_12

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  • DOI: https://doi.org/10.1007/978-3-662-60809-8_12

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  • Publisher Name: Springer Vieweg, Berlin, Heidelberg

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  • Online ISBN: 978-3-662-60809-8

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