About: Modeling Tool Wear in End Milling Using Enhanced GMDH Learning Networks

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	This paper presents an enhanced approach to predictive modeling for determining tool wear in end milling operations based on enhanced group method of data handling (e GMDH). Using milling input parameters (speed, feed, and depth of cut) and response (tool wear), the data for the model is partitioned into training and testing datasets, and the training dataset is used to realize a predictive model that is a function of the input parameters and the coefficients determined. In our approach, we first present a methodology for modeling, and then develop predictive model(s) of the problem being solved in the form of second order equations based on the input data and coefficients realized. This approach leads to some generalization because it becomes possible to predict not only the test data obtained during experimentation, but other test data outside the experimental results can also be used This paper presents an enhanced approach to predictive modeling for determining tool wear in end milling operations based on enhanced group method of data handling (e GMDH). Using milling input parameters (speed, feed, and depth of cut) and response (tool wear), the data for the model is partitioned into training and testing datasets, and the training dataset is used to realize a predictive model that is a function of the input parameters and the coefficients determined. In our approach, we first present a methodology for modeling, and then develop predictive model(s) of the problem being solved in the form of second order equations based on the input data and coefficients realized. This approach leads to some generalization because it becomes possible to predict not only the test data obtained during experimentation, but other test data outside the experimental results can also be used (en) Článek prezentuje přístup využívající algrotmu eGMDH k predikci opotřebení vrtných nástrojů při vrtání. To je odhadováno ze vstupních parametrů - rychlost, příkon a hloubka vrtu. V článku je prezentována jak metoda eGMDH tak i výsledky realizovaných experimentů. (cs)
Title	Modeling Tool Wear in End Milling Using Enhanced GMDH Learning Networks Modelování opotřebení vrtáků pomocí eGMDH algoritmu (cs) Modeling Tool Wear in End Milling Using Enhanced GMDH Learning Networks (en)
skos:prefLabel	Modeling Tool Wear in End Milling Using Enhanced GMDH Learning Networks Modelování opotřebení vrtáků pomocí eGMDH algoritmu (cs) Modeling Tool Wear in End Milling Using Enhanced GMDH Learning Networks (en)
skos:notation	RIV/68407700:21230/08:03139207!RIV09-AV0-21230___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(1ET101210513)
http://linked.open...iv/cisloPeriodika	11-12
http://linked.open...vai/riv/dodaniDat	2009
http://linked.open...aciTvurceVysledku	Buryan, Petr
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	379912
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/08:03139207
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	GMDH; self-organizing networks; system modelling; tool wear (en)
http://linked.open.../riv/klicoveSlovo	tool wear GMDH self-organizing networks system modelling
http://linked.open...odStatuVydavatele	GB - Spojené království Velké Británie a Severního Irska
http://linked.open...ontrolniKodProRIV	[E43E82AD050F]
http://linked.open...i/riv/nazevZdroje	The International Journal of Advanced Manufacturing Technology
http://linked.open...in/vavai/riv/obor	JC
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Relational machine learning for analysis of biomedical data
http://linked.open...UplatneniVysledku	2008
http://linked.open...v/svazekPeriodika	39
http://linked.open...iv/tvurceVysledku	Buryan, Petr Lemke, F. Onwubolu, G. C.
http://linked.open...ain/vavai/riv/wos	000260699300003
issn	0268-3768
number of pages	13 (xsd:int)
http://localhost/t...ganizacniJednotka	21230
is http://linked.open...avai/riv/vysledek of	Modeling Tool Wear in End Milling Using Enhanced GMDH Learning Networks Modeling Tool Wear in End Milling Using Enhanced GMDH Learning Networks Modeling Tool Wear in End Milling Using Enhanced GMDH Learning Networks Modeling Tool Wear in End Milling Using Enhanced GMDH Learning Networks

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