Details

Title

The Prediction of Moulding Sand Moisture Content Based on the Knowledge Acquired by Data Mining Techniques

Journal title

Archives of Metallurgy and Materials

Yearbook

2016

Numer

No 3 September

Publication authors

Divisions of PAS

Nauki Techniczne

Publisher

Institute of Metallurgy and Materials Science of Polish Academy of Sciences ; Commitee on Metallurgy of Polish Academy of Sciences

Date

2016

Identifier

ISSN 1733-3490

References

Olejarczyk (2012), Parametric representation of TTT diagrams of ADI cast iron of Metallurgy and, Archives Materials, 57, 981, doi.org/10.2478/v10172-012-0065-9 ; Kass (1980), An exploratory technique for investigating large quantities of categorical data, Applied Statistics, 119, doi.org/10.2307/2986296 ; David (2014), The Computer Support of Diagnostics of Circle Crystallizers, Metalurgija, 53, 193. ; Glowacz (2015), Recognition of thermal images of direct current motor with application of area perimeter vector and bayes classifier, Measurement Science Review, 15, 119, doi.org/10.1515/msr-2015-0018 ; Mahesh Parappagoudar (2008), Forward and reverse mappings in green sand mould system using neural networks, Applied Soft Computing, 8, 239, doi.org/10.1016/j.asoc.2007.01.005 ; Sika (2006), Po wdrożeniu programu KonMas - final - jego wykorzystanie do analizy procesu produkcji odlewów na wydziale Odlewni Żeliwa ŚREM XI International Symposium - Modeling of casting and foundry processes, October, 6, 26. ; Speybroeck (2012), Classification and regression trees of, International Journal Public Health, 57, 243, doi.org/10.1007/s00038-011-0315-z ; Jakubski (2010), The usage of data mining tools for green moulding sands quality control of Metallurgy and, Archives Materials, 55, 843. ; Perzyk (2001), Prediction of ductile cast iron quality by artificial neural networks of Material Processing Technology, Journal, 109. ; Warmuzek (2011), A Procedure for in situ Identification of the Intermetallic AITMSi Phase Precipitates in the Microstructure of the Aluminum Alloys Praktische Metallographie - Practical, Metallography, 48, 660, doi.org/10.3139/147.110045 ; Kluska (2011), Rough sets applied to the RoughCast system for steel castings Intelligent Information and Database Systems Part II Third International Conference Korea Proceedings Part II Series : Springer Lecture Notes in Computer Volume in Artificial, Science Lecture Notes Intelligence, 20, 6592, doi.org/10.1007/978-3-642-20042-7_6,Subseries:NgocThanh;-Gun;Janiak(Eds.),1stEdition.580p ; Malinowski (2013), Technological knowledge management system for foundry industry of Metallurgy and, Archives Materials, 58, 965. ; Jakubski (2013), ANN Modelling For The Analysis Of The Green Moulding Sands Properties of Metallurgy and, Archives Materials, 58, 961. ; Perzyk (2005), Modeling of manufacturing processes by learning systems : The naïve Bayesian classifier versus artificial neural networks of Material Processing Technology, Journal, 164. ; Smyksy (2013), Performance evaluation of rotary mixers through monitoring of power energy parameters of Metallurgy and, Archives Materials, 58, 911. ; Opalinski (2013), Scalable web monitoring system in : Computer Science and Information Systems Federated Conference on, IEEE. ; Jakubski (2010), Selected parameters of moulding sands for designing quality control systems of Foundry, Archives Engineering, 10, 11.

DOI

10.1515/amm-2016-0277

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