Using regression models for predicting the product quality in a tubing extrusion process
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Quality in a manufacturing process implies that the performance characteristics of the product and the process itself are designed to meet specific objectives. Thus, accurate quality prediction plays a principal role in delivering high-quality products to further enhance competitiveness. In tubing extrusion, measuring of the inner and outer diameters is typically performed either manually or with ultrasonic or laser scanners. This paper shows how regression models can result useful to estimate both those physical quality indices in a tube extrusion process. A real-life data set obtained from a Mexican extrusion manufacturing company is used for the empirical analysis. Experimental results demonstrate that k nearest-neighbor and support vector regression methods (with a linear kernel and with a radial basis function) are especially suitable for predicting the inner and outer diameters of an extruded tube based on the evaluation of 15 extrusion and pulling process parameters.
KeywordsRegression models Extrusion process Product quality prediction Support vector regression K nearest-neighbor
The authors would like to acknowledge the financial support from the Spanish Ministry of Economy, Industry and Competitiveness [TIN2013-46522-P], and the Generalitat Valenciana [PROMETEOII/2014/062].
- Batista, G. E. A. P. A., & Silva, D. F. (2009). How k-nearest neighbor parameters affect its performance. In Argentine symposium on artificial intelligence, Mar de Plata, Argentina (pp. 1–12).Google Scholar
- Caruana, R., & Niculescu-Mizil, A. (2004). Data mining in metric space: An empirical analysis of supervised learning performance criteria. In Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, New York, NY (pp. 69–78).Google Scholar
- Chevanan, N., Muthukumarappan, K., & Rosentrater, K. A. (2007). Neural network and regression modeling of extrusion processing parameters and properties of extrudates containing DDGS. Transactions of the American Society of Agricultural and Biological Engineers, 50(5), 1765–1778.Google Scholar
- Guyader, A., & Hengartner, N. (2013). On the mutual nearest neighbors estimate in regression. The Journal of Machine Learning Research, 14, 2361–2376.Google Scholar
- Khan, J. G., Dalu, R. S., & Gadekar, S. S. (2014). Defects in extrusion process and their impact on product quality. International Journal of Mechanical Engineering and Robotics Research, 3(3), 10–18.Google Scholar
- Kramer, O. (2011). Unsupervised K-nearest neighbor regression. ArXiv e-prints arXiv:1107.3600.
- Krömer, P., Snášel, V., Platoš, J., & Abraham, A. (2010). Evolving fuzzy classifier for data mining—An information retrieval approach. In Proceedings of the 3rd international conference on computational intelligence in security for information systems, León, Spain (pp. 25–32).Google Scholar
- Meiabadi, M. S., Vafaeesefat, A., & Sharifi, F. (2013). Optimization of plastic injection molding process by combination of artificial neural network and genetic algorithm. Journal of Optimization in Industrial Engineering, 6(13), 49–54.Google Scholar
- Oberg, E., Jones, F., Horton, H., Ryffel, H., & McCauley, C. (2012). Machinery’s handbook. New York, NY: Industrial Press.Google Scholar
- Oke, S. A., Johnson, A. O., Charles-Owaba, O. E., Oyawale, F. A., & Popoola, I. O. (2006). A neuro-fuzzy linguistic approach in optimizing the flow rate of a plastic extruder process. International Journal of Science & Technology, 1(2), 115–123.Google Scholar
- Ramana, E. V., & Reddy, P. R. (2013). Data mining based knowledge discovery for quality prediction and control of extrusion blow molding process. The International Journal of Advanced Manufacturing Technology, 6(2), 703–713.Google Scholar
- Sharma, R. S., Upadhyay, V., & Raj, K. H. (2009). Neuro-fuzzy modeling of hot extrusion process. Indian Journal of Engineering and Materials Sciences, 16, 86–92.Google Scholar
- Urraca Valle, R., Sodupe Ortega, E., Antoñanzas Torres, J., Alonso García, E., Sanz García, A., & Martínez de Pisón Ascacíbar, F. J. (2013). Comparative methodology of non-linear models for predicting rheological properties of rubber mixtures in industrial lines. In Proceedings of the 17th international congress on project management and engineering, Logroño, Spain (pp. 1346–1357)Google Scholar
- Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques. Burlington, MA: Morgan Kaufmann Publishers.Google Scholar