Abstract
The purpose of this study was to develop an innovative supervisor system to assist the operators in an industrial manufacturing process to help discover new alternative solutions for improving both the products and the manufacturing process.
This paper presents a solution for integrating different types of statistical modelling methods for a usable industrial application in quality monitoring. The two case studies demonstrating the usability of the tool were selected from a steel industry with different needs for knowledge presentation. The usability of the quality monitoring tool was tested in both case studies, both offline and online.
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References
Harding, J., Shahbaz, M., Srinivas, Kusiak, A.: Data mining in manufacturing: a review. J. Manuf. Sci. Eng. 128, 969–976 (2006)
Siirtola, P., Tamminen, S., Ferreira, E., Tiensuu, H., Prokkola, E., Röning, J.: Automatic recognition of steel plate side edge shape using classification and regression models. In: Proceedings of the 9th Eurosim Congress on Modelling and Simulation (EUROSIM 2016) (2016)
Phillips-Wren, G.: Intelligent decision support systems. In: Multicriteria Decision Aid and Artificial Intelligence. Wiley, Chichester (2013)
Logunova, O., Matsko, I., Posohov, I., Luk’ynov, S.: Automatic system for intelligent support of continuous cast billet production control processes. Int. J. Adv. Manuf. Technol. 74, 1407–1418 (2014)
Dumitrache, I., Caramihai, S.: The intelligent manufacturing paradigm in knowledge society. In: Knowledge Management. InTech, pp. 36–56 (2010)
Bi, Z., Xu, L., Wang, C.: Internet of things for enterprise systems of modern manufacturing. IEEE Trans. Ind. Inf. 10(2), 1537–1546 (2014)
Xu, L., He, W., Li, S.: Internet of things in industries: a survey. IEEE Trans. Ind. Inf. 10(4), 2233–2243 (2014)
Akram, M., Saif, A.W., Rahim, M.: Quality monitoring and process adjustment by integrating SPC and APC: a review. Int. J. Ind. Syst. Eng. 11(4), 375–405 (2012)
Kano, M., Nakagawa, Y.: Data-based process monitoring, process control, and quality improvement: recent developments and applications in steel industry. Comput. Chem. Eng. 32(1–2), 12–24 (2008)
Bhadesia, H.: Neural networks in materials science. ISIJ Int. 39(10), 966–979 (1999)
Tamminen, S., Juutilainen, I., Röning, J.: Quantile regression model for impact toughness estimation. In: Perner, P. (ed.) ICDM 2010. LNCS (LNAI), vol. 6171, pp. 263–276. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14400-4_21
Tamminen, S., Juutilainen, I., Röning, J.: Exceedance probability estimation for quality test consisting of multiple measurements. Expert Syst. Appl. 40, 4577–4584 (2013)
Seni, G., Elder, J.: Ensemble methods in data mining: improving accuracy through combining predictions. In: Synthesis Lectures on Data Mining and Knowledge Discovery. Morgan & Claypool, USA (2010)
Natekin, A., Knoll, A.: Gradient boosting machines, a tutorial. Front. Neurorobot. 7 (2013)
Elith, J., Leathwick, J., Hastie, T.: A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008)
Friedman, J.: Stochastic gradient boosting. Comput. Stat. Data Anal. 19, 367–378 (2002)
Juutilainen, I., Tamminen, S., Röning, J.: A tutorial to developing statistical models for predictiong disqualification probability. In: Computational Methods for Optimizing Manufacturing Technology, Models and Techniques, pp. 368–399. IGI Global, USA (2012)
Inselberg, A.: Visual data mining with parallel coordinates. Comput. Stat. 13(1), 47–63 (1998)
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Tamminen, S. et al. (2018). From Measurements to Knowledge - Online Quality Monitoring and Smart Manufacturing. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2018. Lecture Notes in Computer Science(), vol 10933. Springer, Cham. https://doi.org/10.1007/978-3-319-95786-9_2
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DOI: https://doi.org/10.1007/978-3-319-95786-9_2
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