A review of artificial intelligent approaches applied to part accuracy prediction
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comunitat-uji-handle2:10234/7034
comunitat-uji-handle3:10234/8619
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Title
A review of artificial intelligent approaches applied to part accuracy predictionAuthor (s)
Date
2010Publisher
InderscienceISSN
1748-5711; 1748-572XBibliographic citation
International Journal of Machining and Machinability of Materials (2010) vol. 8, no.1/2 p. 6-37Type
info:eu-repo/semantics/articleSubject
Artificial intelligence | AI selection | Part accuracy | Surface roughness | Dimensional derivations | Artificial neural networks | ANNs | Fuzzy logic | Adaptative network based fuzzy inference systems | Bayesian networks | Guidelines | Accuracy prediction | Intelligent manufacturing | Artificial intelligence | Intelligent control systems | Artificial intelligence--Industrial applications | Neural networks (Computer science) | Intel·ligència artificial | Control intel·ligent | Intel·ligència artificial--Aplicacions industrials | Xarxes neuronals (Informàtica)
Abstract
Nowadays, despite the large volume of worldwide academic research on various aspects of metal cutting the control of workpiece precision still relies on machine-tool operator's experience and trial and error runs. In ... [+]
Nowadays, despite the large volume of worldwide academic research on various aspects of metal cutting the control of workpiece precision still relies on machine-tool operator's experience and trial and error runs. In order to increase the efficiency of machining systems, many empirical models based on artificial intelligent (AI) approaches have been proposed in the past, where important process improvements were reported. This paper overviews the AI approaches applied in machining operations to predict part accuracy in terms of dimensional deviations and surface roughness. Successful techniques applied in this field such as artificial neural networks, fuzzy logic, adaptive-network-based fuzzy inference systems and Bayesian networks are briefly reviewed and compared to facilitate its use. For each AI approach, the most relevant research works are described and based on those works some guidelines are proposed for its implementation. In addition, advantages and drawbacks of each approach are summarised and a generic guideline for AI approaches selection is proposed. [-]
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