Abstract
Nowadays significant part of plastic and, in particular, thermoplastic products of different sizes is manufactured using injection molding process. Due to the complex nature of changes that thermoplastic materials undergo during different stages of the injection molding process, it is critically important to control parameters that influence final part quality. In addition, injection molding process requires high repeatability due to its wide application for mass-production. As a result, it is necessary to be able to predict the final product quality based on critical process parameters values. The following paper investigates possibility of using Artificial Neural Networks (ANN) and, in particular, Multilayered Perceptron (MLP), as well as Decision Trees, such as J48, to create models for prediction of quality of dog bone specimens manufactured from high density polyethylene. Short theory overview for these two machine learning methods is provided, as well as comparison of obtained models’ quality.
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References
Statista—The Statistics Portal. Global production of plastics since 1950. 2018 [cited 2018.20.07]; Available from: https://www.statista.com/statistics/282732/global-production-of-plastics-since-1950/
Hernandez-Ortiz, J.P.: Polymer Processing: Modeling and Simulation. Hanser Gardner Publications, Cincinnati (2006)
Tsai, K.-M., Luo, H.-J.: An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm. J. Intell. Manuf. 28(2), 473–487 (2017)
Karbasi, H., Reiser, H.: Smart mold: real-time in-cavity data acquisition. In: First Annual Technical Showcase & Third Annual Workshop, Canada. Citeseer (2006)
Zhao, P., et al.: A nondestructive online method for monitoring the injection molding process by collecting and analyzing machine running data. Int. J. Adv. Manuf. Technol. 72(5–8), 765–777 (2014)
Rosato, D.V., Rosato, M.G.: Injection Molding Handbook. Springer Science & Business Media, Berlin (2012)
Kitayama, S., et al.: Multi-objective optimization of variable packing pressure profile and process parameters in plastic injection molding for minimizing warpage and cycle time. Int. J. Adv. Manuf. Technol. 92(9–12), 3991–3999 (2017)
Yen, C., et al.: An abductive neural network approach to the design of runner dimensions for the minimization of warpage in injection mouldings. J. Mater. Process. Technol. 174(1–3), 22–28 (2006)
Altan, M.: Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods. Mater. Des. 31(1), 599–604 (2010)
Zhu, J., Chen, J.C.: Fuzzy neural network-based in-process mixed material-caused flash prediction (FNN-IPMFP) in injection molding operations. Int. J. Adv. Manuf. Technol. 29(3–4), 308–316 (2006)
Ozcelik, B., Erzurumlu, T.: Determination of effecting dimensional parameters on warpage of thin shell plastic parts using integrated response surface method and genetic algorithm. Int. Commun. Heat Mass Transfer 32(8), 1085–1094 (2005)
Yin, F., Mao, H., Hua, L.: A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters. Mater. Des. 32(6), 3457–3464 (2011)
Che, Z.: PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding. Comput. Ind. Eng. 58(4), 625–637 (2010)
ModeFRONTIER—Design optimization platform [cited 2018.20.07]; Available from: https://www.esteco.com/modefrontier
WEKA—Waikato Environment for Knowledge Analysis [cited 2018 20.07]; Available from: https://www.cs.waikato.ac.nz/ml/weka/
Ogorodnyk, O., Martinsen, K.: Monitoring and control for thermoplastics injection molding a review. Procedia CIRP 67, 380–385 (2018)
Dang, X.-P.: General frameworks for optimization of plastic injection molding process parameters. Simul. Model. Pract. Theory 41, 15–27 (2014)
Kononenko, I., Kukar, M.: Machine Learning and Data Mining: Introduction to Principles and Algorithms. Horwood Publishing, Cambridge (2007)
Acknowledgement
This research is funded by Norwegian Research Council as a part of MegaMould project.
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Ogorodnyk, O., Lyngstad, O.V., Larsen, M., Wang, K., Martinsen, K. (2019). Application of Machine Learning Methods for Prediction of Parts Quality in Thermoplastics Injection Molding. In: Wang, K., Wang, Y., Strandhagen, J., Yu, T. (eds) Advanced Manufacturing and Automation VIII. IWAMA 2018. Lecture Notes in Electrical Engineering, vol 484. Springer, Singapore. https://doi.org/10.1007/978-981-13-2375-1_30
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DOI: https://doi.org/10.1007/978-981-13-2375-1_30
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