Analysis of Production Process Parameters by Using Data Mining Methods

Abstract:

This article deals with knowledge discovery in databases (abbr. KDD) and methodology of this process. The authors give an identification of production parameters and their influence on a production process. Knowledge discovery in the production databases is minimally used for the process of planning and control. There are many problems that occur in the production process. It is important to indentify the impact of manufacturing parameters on the system for managers. New discovered knowledge from production systems will help make the right decision to fulfill the objectives. Using the KDD in the control of production systems, it can be achieved better understanding of system control and can help predict a future behavior of system. The authors formulated general knowledge for improve parameters of analyzed production process. The objectives, steps and some results of the project are presented in this article

• Periodical: Applied Mechanics and Materials (Volume 309)
• Pages: 342-349
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.309.342
• Online since: February 2013
• Authors: Pavel Važan, Pavol Tanuska, Dominika Jurovatá, Michal Kebisek
• Keywords: Data Mining (DM), Knowledge Discovery in Databases, Production System, Simulation

References

[1] A. Trnka. Results of application data mining algorithms to (lean) six sigma methodology. International Journal of Engineering 2012/1 ISNN 1584-2665, (2012).

[2] J. Han, M. Kamber. Data Mining: Concepts and Techniques, Morgan-Kaufmann, Academic Press, San Francisco (2001).

[3] J. Jackson. Data mining: A conceptual overview, In Communications of the Association for Information Systems, Vol. 8, pp.267-296 (2002).

[4] P. Chapman et al. CRISP-DM 1. 0 – Step-by-step data mining guide, [online] Available on the Internet: <http: /www. spss. ch/ upload/1107356429_CrispDM1. 0. pdf>, [cited: 2012-04-10; 10: 00 CET] (2000).

[5] U. M. Fayyad et al. From data mining to knowledge discovery: an overview, In Advances in knowledge discovery and data mining. AAAI Press / The MIT Press, (1996).

[6] D. Larose. Data Mining Methods and Models, John Wiley & Sons Ltd, New Jersey (2006).

[7] P. Važan, O. Moravčík. The alternative procedure of lot size determination in flexible manufacturing systems, Annals of DAAAM for 2007 & Proceedings of the 18th International DAAAM, October 24-27; Zadar, Croatia, (2007).

[8] T. Hill, P. Lewicki. STATISTICS: Methods and Applications. StatSoft, Tulsa, OK, (2007).

[9] Sherrod, H. P. Introduction to Support Vector Machine (SVM) Models, [online] Available on the Internet: http: /www. dtreg. com/svm. htm, [cited: 2012-07-20; 13: 00 CET].

[10] Fred, Y. W; Kang, K. Y. Applications of neural network in regression analysis, Computers &amp; Industrial Engineering, Volume 23, Issues 1–4, pages 93-95, ISSN 0360-8352, [online] Available on the Internet: http: /www. sciencedirect. com/ science/article/pii/ 036083529290071Q, [cited: 2012-06-12; 15: 00 CET] (1992).

DOI: 10.1016/0360-8352(92)90071-q