NDLI: Artificial neural networks for machining processes surface roughness modeling

Content Provider	Springer Nature Link
Author	Pontes, Fabricio J. Ferreira, João R. Silva, Messias B. Paiva, Anderson P. Balestrassi, Pedro Paulo
Copyright Year	2009
Abstract	In recent years, several papers on machining processes have focused on the use of artificial neural networks for modeling surface roughness. Even in such a specific niche of engineering literature, the papers differ considerably in terms of how they define network architectures and validate results, as well as in their training algorithms, error measures, and the like. Furthermore, a perusal of the individual papers leaves a researcher without a clear, sweeping view of what the field’s cutting edge is. Hence, this work reviews a number of these papers, providing a summary and analysis of the findings. Based on recommendations made by scholars of neurocomputing and statistics, the review includes a set of comparison criteria as well as assesses how the research findings were validated. This work also identifies trends in the literature and highlights their main differences. Ultimately, this work points to underexplored issues for future research and shows ways to improve how the results are validated.
Starting Page	879
Ending Page	902
Page Count	24
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	49
Issue Number	9-12
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-12-15
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial neural networks Machining Surface roughness Modeling Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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