NDLI: Application of GONNS to predict constrained optimum surface roughness in face milling of high-silicon austenitic stainless steel

Content Provider	Springer Nature Link
Author	Elhami, S. Razfar, M. R. Farahnakian, M. Rasti, A.
Copyright Year	2012
Abstract	Surface roughness is a technical requirement for machined products and one of the main product quality specifications. In the present research, a genetically optimized neural network system (GONNS) is proposed for prediction of constrained optimal cutting conditions in face milling of a high-silicon austenitic stainless steel (UNS J93900) in order to minimize surface roughness. In order to attain minimum operation numbers and decrease the cost of machining, an experimental scheme was arranged by using Taguchi method. The considered parameters were cutting speed, feed, depth of cut, and engagement. Cutting force components and surface roughness were measured, and then analysis of variance is performed. The results show that the feed is the dominant factor affecting the surface roughness. Backpropagation artificial neural network was utilized to create predictive models of surface roughness and cutting forces exploiting the experimental data, and the genetic algorithm was employed to find the constrained optimum of surface roughness. Finally, in order to validate the method, an experiment with the obtained optimal cutting condition was carried out, and the results were compared with the predicted value of surface roughness. The corresponding results show the capability of GONNS to predict constrained surface roughness.
Starting Page	975
Ending Page	986
Page Count	12
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	66
Issue Number	5-8
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-07-26
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Constrained optimization Neural network modeling Face milling Design of experiments Surface roughness Industrial and Production Engineering Production/Logistics/Supply Chain Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
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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