The Use of Modern Statistical Methods to Optimize Production Systems in Foundries

• Autorzy: Szymszal J. , Gajdzik B. , Kaczmarczyk G.

Identyfikatory

DOI

10.1515/afe-2016-0061

• Języki publikacji: EN

Abstrakty

EN

Conducting reliable and credible evaluation and statistical interpretation of empirical results related to the operation of production systems in foundries is for most managers complicated and labour-intensive. Additionally, in many cases, statistical evaluation is either ignored and considered a necessary evil, or is completely useless because of improper selection of methods and subsequent misinterpretation of the results. In this article, after discussing the key elements necessary for the proper selection of statistical methods, a wide spectrum of these methods has been presented, including regression analysis, uni- and multivariate correlation, one-way analysis of variance for factorial designs, and selected forecasting methods. Each statistical method has been illustrated with numerous examples related to the foundry practice.

Słowa kluczowe

EN

production of computer-aided; foundry production; quality of castings; statistical methods; forecasting

PL

wytwarzanie wspomagane komputerem; produkcja w odlewni; jakość odlewów; metody statystyczne; prognozowanie

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2016
• Tom: Vol. 16, iss. 3
• Strony: 115--120
• Opis fizyczny: Bibliogr. 7 poz., rys., tab., wykr.

Twórcy

autor

Szymszal J.

• Silesian University of Technology, Production Engineering Department, ul. Krasińskiego 8, 40-019 Katowice, Poland

autor

Gajdzik B.

• Silesian University of Technology, Production Engineering Department, ul. Krasińskiego 8, 40-019 Katowice, Poland

autor

Kaczmarczyk G.

• Silesian University of Technology, Production Engineering Department, ul. Krasińskiego 8, 40-019 Katowice, Poland

Bibliografia

• [1] Szymszal, J., Maliński, M. (1999). Contemporary mathematical statistics in spreadsheets. Katowice: Ed. SlAM. (in Polish).
• [2] Aczel, A.D. (2000). Statistics in the management. Warszawa: PWE. (in Polish).
• [3] Stanisz, A. (2006). Accessible course in statistics. Vol. 1-3. Kraków. StatSoft Polska Sp. z o.o. (in Polish).
• [4] Szymczak, T., Gumienny, G., Walas, K. & Pacyniak, T. (2015). Effect of Tungsten and Molybdenum on the Crystallization, Microstructure and Properties of Silumin 226. Archives of Foundry Engineering. 15(3), 61-66.
• [5] Giętka, T., Ciechacki, K., Szykowy. T. (2015). Influence of the temperature of breaking on the ADI cast irons impact resistance. Archives of Foundry Engineering. 15(2), 87-92. (in Polish).
• [6] Soiński, M.S., Skurka, K., Jakubus, A. & Kordas, P. (2015) . Structure of Foundry Production in the World and in Poland over the 1974-2013 Period. Archives of Foundry Engineering. 15(2), 69-76.
• [7] Krawiec, S. (2014). Adaptive models of exponential smoothing as instruments of short-term forecasting quantitative phenomena. Katowice: Ed. Silesian University of Technology. (in Polish).

Uwagi

PL

Opracowane ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.