Methodology of Fault Diagnosis in Ductile Iron Melting Process

• Autorzy: Perzyk M. , Kozlowski J.
• Języki publikacji: EN

Abstrakty

EN

Statistical Process Control (SPC) based on the Shewhart’s type control charts, is widely used in contemporary manufacturing industry, including many foundries. The main steps include process monitoring, detection the out-of-control signals, identification and removal of their causes. Finding the root causes of the process faults is often a difficult task and can be supported by various tools, including data-driven mathematical models. In the present paper a novel approach to statistical control of ductile iron melting process is proposed. It is aimed at development of methodologies suitable for effective finding the causes of the out-of-control signals in the process outputs, defined as ultimate tensile strength (Rm) and elongation (A5), based mainly on chemical composition of the alloy. The methodologies are tested and presented using several real foundry data sets. First, correlations between standard abnormal output patterns (i.e. out-of-control signals) and corresponding inputs patterns are found, basing on the detection of similar patterns and similar shapes of the run charts of the chemical elements contents. It was found that in a significant number of cases there was no clear indication of the correlation, which can be attributed either to the complex, simultaneous action of several chemical elements or to the causes related to other process variables, including melting, inoculation, spheroidization and pouring parameters as well as the human errors. A conception of the methodology based on simulation of the process using advanced input - output regression modelling is presented. The preliminary tests have showed that it can be a useful tool in the process control and is worth further development. The results obtained in the present study may not only be applied to the ductile iron process but they can be also utilized in statistical quality control of a wide range of different discrete processes.

Słowa kluczowe

EN

quality management; information technology; foundry industry; process fault diagnosis; ductile iron; data driven model

PL

zarządzanie jakością; technologia informatyczna; przemysł odlewniczy; diagnostyka uszkodzeń; żeliwo ADI; model danych

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2016
• Tom: Vol. 16, iss. 4
• Strony: 101--108
• Opis fizyczny: Bibliogr. 10 poz., tab., wykr.

Twórcy

autor

Perzyk M.

• Institute of Manufacturing Technologies, Warsaw University of Technology, Narbutta 85, 02-524 Warszawa, Poland

autor

Kozlowski J.

• Institute of Manufacturing Technologies, Warsaw University of Technology, Narbutta 85, 02-524 Warszawa, Poland

Bibliografia

• [1] Perzyk, M., Kozlowski, J. & Zarzycki, K. (2013). Application of computational intelligence methods in control and diagnosis of production processes. In Kazmierczak J. (Ed.). Systems supporting production engineering. 2013, 104-125.
• [2] Sobczak J.J. (Ed.). (2013). Foundryman’s Handbook (Poradnik Odlewnika). Kraków, Poland: Wydawnictwo Stowarzyszenia Technicznego Odlewników Polskich (in Polish).
• [3] (2004). The Sorelmetal Book of Ductile Iron. Montreal (Quebec) Canada: Rio Tinto Iron & Titanium Inc.
• [4] StatSoft, Inc. (2014). STATISTICA (data analysis software system), version 12. Retrieved April 13, 2016, from www.statsoft.com.
• [5] Hoyer, R.W. & Ellis W.C. (1996). A Graphical Exploration of SPC Part 2: The probability structure of rules for interpreting control charts. Quality progress. 29(5), 57-64.
• [6] Montgomery, D.C. (2010). Statistical Quality Control: A Modern Introduction. (6th ed.). John Wiley & Sons, Inc.
• [7] Perzyk, M. & Rodziewicz, A. (2015). Application of Special Cause Control charts to green sand process. Archives of Foundry Engineering. 15(4), 55-60.
• [8] Perzyk, M., Biernacki, R. & Kozłowski, J. (2008). Data mining in manufacturing: significance analysis of process parameters. Journal of Engineering Manufacture. 222, 1503-1516.
• [9] Perzyk, M. & Kochanski, A. (2003). Detection of causes of casting defects assisted by artificial neural networks. Journal of Engineering Manufacture. 217, 1279-1284.
• [10] Zhang G. (1990). A new diagnosis theory with two kinds of quality. Total Quality Management. 1(2), 249-257.

Uwagi

PL

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