DocumentCode :
2018282
Title :
Review of ANN Technique for Modeling Surface Roughness Performance Measure in Machining Process
Author :
Zain, Azlan Mohd ; Haron, Habibollah ; Sharif, Safian
Author_Institution :
Fac. of Comput. Sci. & Inf. Syst., Univ. Teknol. Malaysia, Skudai
fYear :
2009
fDate :
25-29 May 2009
Firstpage :
35
Lastpage :
39
Abstract :
The former, which is defined as modeling of machining processes, is essential to provide the basic mathematical models for formulation of the certain process objective functions. With conventional approaches such as statistical regression technique, explicit models are developed that required complex physical understanding of the modeling process. With non conventional approaches or artificial intelligence techniques such as artificial neural network, fuzzy logic and genetic algorithm based modeling, implicit model are created within the weight matrices of the net, rules and genes that is easier to be implemented. With the focus on surface roughness performance measure, this paper outlines and discusses the concept, application, abilities and limitations of artificial neural network in the machining process modeling. Subsequently the future trend of artificial neural network in modeling machining process is reported.
Keywords :
fuzzy logic; genetic algorithms; machining; neural nets; production engineering computing; regression analysis; surface roughness; ANN technique; artificial intelligence technique; artificial neural network; fuzzy logic; genetic algorithm; machining process modeling; mathematical model; statistical regression technique; surface roughness performance measure; weight matrix; Analytical models; Artificial intelligence; Artificial neural networks; Fuzzy logic; Machining; Mathematical model; Predictive models; Response surface methodology; Rough surfaces; Surface roughness; ANN; machining; modeling; surface roughness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Modelling & Simulation, 2009. AMS '09. Third Asia International Conference on
Conference_Location :
Bali
Print_ISBN :
978-1-4244-4154-9
Electronic_ISBN :
978-0-7695-3648-4
Type :
conf
DOI :
10.1109/AMS.2009.78
Filename :
5071954
Link To Document :
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