Abstract
The application of data mining techniques in the design of modern foundry materials allows achieving higher product quality indicators. Designing of a new product always requires thorough knowledge of the effect of alloying elements on the microstructure and hence also on the properties of the examined material. The conducted experimental studies allow for a qualitative assessment of the indicated relationships, but it is the use of intelligent computational techniques that enables building an approximation model of the microstructure and, owing to this, make predictions with high precision. The developed model of prediction supports the technology-related decisions as early as at the stage of casting design and is considered the first step in selecting the type of material used.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Witten I, Frank E (2000) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, New York
Glowacz A (2016) Fault diagnostics of DC motor using acoustic signals and MSAF-RATIO30-EXPANDED. Arch Electr Eng 65(4):733–744
Quinlan JR (1986) Induction on decision trees, machine learning. Kluwer Academic Publishers, Boston
Balasubramanian SA, Manickavasagam J, Natarajan T, Balakrishnan J (2015) An experimental analysis of forecasting the high frequency data of matured and emerging economies stock index using data mining techniques. Int J Oper Res 23(4):406–426
Wilk-Kolodziejczyk D, Regulski K, Gumienny G (2016) Comparative analysis of the properties of the Nodular Cast Iron with Carbides and the Austempered Ductile Iron with use of the machine learning and the Support Vector Machine. Int J Adv Manuf Technol 87(1):1077–1093
Regulski K, Szeliga D, Kusiak J (2014) Data exploration approach versus sensitivity analysis for optimization of metal forming processes. Key Eng Mater 611612:1390–1395
Kluska-Nawarecka S, Wilk-Kolodziejczyk D, Regulski K, Dobrowolski G (2011) Rough sets applied to the rough-cast system for steel castings. In: Nguyen NT, Kim C-G, Janiak A (eds) Intelligent information and database systems. Part II. Lecture notes in computer science, vol 6592, no 2011. Springer, pp 52–61
Pietrowski S (1998) Alloyed cast iron with compacted graphite. Solidification Met Alloys 37:105–111
Guzik E (2006) Selected issues on the structure and properties of ausferritic cast iron. Arch Foundry Eng 21 (1/2):33–42
Founding – Compacted (compacted) graphite cast irons PN-EN 16079:2012
Skvarenina S, Shin YC (2006) Laser-assisted machining of compacted graphite iron. Int J Mach Tools Manuf 46(1):7–17
Pietrowski S (2000) Compendium of knowledge about compacted cast iron. Solidification Met Alloys 2(44):279–292
Cueva G, Sinatora A, Guesser WL, Tschiptschin AP (2003) Wear resistance of cast irons used in brake disc rotors. Wear 255:1256–1260
Guzik E, Dzik S (2009) Structure and mechanical properties of compacted cast iron in cylinder head casting. Arch Foundry Eng 9(1):175–18
Guzik E, Kleingartner T (2009) A study on the structure and mechanical properties of compacted cast iron with pearlitic-ferritic matrix. Arch Foundry Eng 9(3):55–60
Sun XJ, Li YX, Chen X (2007) Controlling melt quality of compacted graphite iron. Mater Sci Eng A 466(1):1–8
Pietrowski S (1998) A mechanism of the compacted graphite crystallization in cast iron. Solidification Met Alloys 37:97–104
Mierzwa P, Soiński M (2011) The effect of thermal treatment on the mechanical properties of compacted cast iron. Arch Foundry Eng Spec Issue 10(1):133–138
Andrsova Z, Volesky L (2012) The potential of isothermally hardened iron with compacted graphite. COMAT 2012, Plzeň
Pytel A, Gazda A (2014) Evaluation of selected properties in austempered compacted cast iron (AVCI). Trans Foundry Res Inst 54(4):23–31
Soinski MS, Jakubus A (2014) Initial assessment of abrasive wear resistance of austempered cast iron with compacted graphite. Arch Metall Mater 59(3):1073–1076
Onal O, Ozturk AU (2010) Artificial neural network application on microstructure–compressive strength relationship of cement mortar. Adv Eng Softw 41(2):165–169
Cruz D, Talbert DA, Eberle W, Biernacki J (2016) A neural network approach for predicting microstructure development in cement. In: Internationall Conf of Artificial Intelligence ICAI
Sundararaghavan V, Zabaras N (2005) Classification and reconstruction of three-dimensional microstructures using support vector machines. Comput Mater Sci 32(2):223–239
Ramkishore S, Madhumitha P, Palanichamy P (2015) Comparison of logistic regression and support vector machine for the classification of microstructure and interfacial defects in zircaloy-2, Soft Computing and Machine Intelligence (ISCMI) International Conference
Rauch L, Chmura A, Gronostajski Z, Polak S, Pietrzyk M (2016) Cellular automata model for prediction of crack initiation and propagation in hot forging tools. Arch Civ Mech Eng 16(3):437–447
Yang H, Wu Ch, Li HW, Fan XG (2011) Review on cellular automata simulations of microstructure evolution during metal forming process: Grain coarsening, recrystallization and phase transformation. Sci China Technol Sci 54(8):2107–2118
Pietrowski S Influence of reaction chamber shape on cast iron spheroidization process in-mold. Arch Foundry Eng 10(1):115–122
Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19(1):1–67
Abraham A, Steinberg D, Philip NS (2001) Rainfall forecasting using soft computing models and multivariate adaptive regression splines. IEEE SMC Trans Special Issue Fusion Soft Comput Hard Comput Ind Appl 1:1–6
Butte NF, Wong WW, Adolph AL, Puyau MR, Vohra FA, Zakeri IF (2010) Validation of cross-sectional time series and multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents using doubly labeled water. J Nutr 140(8):1516–1523
De Andrs J, Lorca P, de Cos Juez F, Snchez-Lasheras F (2011) Bankruptcy forecasting: a hybrid approach using fuzzy c-means clustering and multivariate adaptive regression splines (MARS). Expert Syst Appl 38 (3):1866–1875
Plonsky L, Oswald FL (2016) Multiple Regression as a flexible alternative to ANOVA in L2 research. Stud Second Lang Acquis 39(3):1–14
Behera AK, Verbert J, Lauwers B, Duflou JR (2013) Tool path compensation strategies for single point incremental sheet forming using multivariate adaptive regression splines. Comput-Aided Des 45(3):575–590
Mukhopadhyay A, Iqbal A (2009) Prediction of mechanical property of steel strips using multivariate adaptive regression splines. J Appl Stat 36(1):1–9
Beccali M, Cellura M, Brano VL, Marvuglia A (2004) Forecasting daily urban electric load profiles using artificial neural networks. Energy Convers Manag 45:2879–2900
Hippert H, Pedreira CE, Souza RC (2001) Neural networks for short term load forecasting: a review and evaluation. IEEE Trans Power Syst 16:44–55
Jakubski J, Malinowski P, Dobosz St M, Major-Gabryś K (2013) ANN Modelling for the analysis of the Green Moulding Sands properties. Arch Metall Mater 58(3):961–964
Sztangret L, Szeliga D, Kusiak J, Pietrzyk M (2012) Application of inverse analysis with metamodelling for identification of metal flow stress. Can Metall Q 51:440–446
Rauch L, Sztangret L, Pietrzyk M (2013) Computer system for identification of material models on the basis of plastometric tests. Arch Metall Mater 58(3):737–743
Tomar D, Agarwal S (2015) A comparison on multi-class classification methods based on least squares twin support vector machine. Knowl-Based Syst 81:131–147
Laurain V (2015) An instrumental least squares support vector machine for nonlinear system identification. Automatica 54:340–347
Santos CM, Escobedo JF, Teramoto ÉT, Silva SH (2016) Assessment of ANN and SVM models for estimating normal direct irradiation (Hb). Energy Convers Manag 126:826–836
Breinman L, Friedman JH, Olshen RA, Stone CJ (1993) Classification and regression trees. Chapman and Hall, UK
Kass GV (1980) An exploratory technique for investigatin large quantities of categorical data. Appl Stat 29:119–127
Regulski K, Jakubski J, Opaliński A, Brzeziński M, Gowacki M (2016) The prediction of moulding sand moisture content based on the knowledge acquired by data mining techniques. Arch Metall Mater 61(3):1363–1368
Kluska-Nawarecka S, Gorny Z, Wilk-Kolodziejczyk D (2007) The logic of plausible reasoning in the diagnosis of castings defects. Arch Metall Mater 52(3):375–380
Gorny Z, Kluska-Nawarecka S, Wilk-Kolodziejczyk D (2013) Heuristic models of the toughening process to improve the properties of non-ferrous metal alloys. Arch Metall Mater 58(3):849–852
Smyksy K, Ziolkowski E, Wrona R, Brzezinski M (2013) Performance evaluation of rotary mixers through monitoring of power energy parameters. Arch Metall Mater 58(3):911– 914
Maciol P, Regulski K (2016) Development of semantic description for multiscale models of thermo-mechanical treatment of metal alloys. JOM 68(8):2082–2088
Maciol A, Wrona R, Stawowy A, Maciol P (2007) An attempt at formulation of ontology for technological knowledge comprised in technical standards. Arch Metall Mater 52(3):381–388
Rojek G, Kusiak J (2012) Industrial control system based on data processing. Lect Notes Comput Sci 7268:502–510
Kluska-Nawarecka S, Wilk-Kolodziejczyk D, Dajda J, Macura M, Regulski K (2014) Computer-assisted integration of knowledge in the context of identification of the causes of defects in castings. Arch Metall Mater 59 (2):743–746
Kluska-Nawarecka S, Regulski K, Krzyzak M, Lesniak G, Gurda M (2013) System of semantic integration of non-structuralized documents in natural language in the domain of metallurgy. Arch Metall Mater 58 (3):927–930
Acknowledgements
Financial support of The National Centre for Research and Development LIDER/028/593/L-4/12/NCBR/2013 is gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Cite this article
Wilk-Kolodziejczyk, D., Regulski, K., Gumienny, G. et al. Data mining tools in identifying the components of the microstructure of compacted graphite iron based on the content of alloying elements. Int J Adv Manuf Technol 95, 3127–3139 (2018). https://doi.org/10.1007/s00170-017-1430-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-017-1430-7