NDLI: Grain Size Estimation of Superalloy Inconel 718 After Upset Forging by a Fuzzy Inference System

Content Provider	Springer Nature Link
Author	Toro, Luis Cavazos, Alberto Colás, Rafael
Copyright Year	2009
Abstract	A fuzzy logic inference system was designed to predict the grain size of Inconel 718 alloy after upset forging. The system takes as input the original grain size, temperature, and reduction rate at forging and predicts the final grain size at room temperature. It is assumed that the system takes into account the effects that the heterogeneity of deformation and grain growth exerts in this particular material. Experimental trials were conducted in a factory that relies on upset forging to produce preforms for ring rolling. The grain size was reported as ASTM number, as this value is used on site. A first attempt was carried out using a series of 15 empirically based set of rules; the estimation error with these was above two ASTM numbers; which is considered to be very high. The system was modified and expanded to take into account 28 rules; the estimation error of this new system resulted to be close to one ASTM number, which is considered to be adequate for the prediction.
Starting Page	1183
Ending Page	1192
Page Count	10
File Format	PDF
ISSN	10599495
Journal	Journal of Materials Engineering and Performance
Volume Number	18
Issue Number	9
e-ISSN	15441024
Language	English
Publisher	Springer US
Publisher Date	2009-02-27
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	fuzzy logic modeling grain size estimation superalloy upset forging Engineering Design Quality Control, Reliability, Safety and Risk Tribology, Corrosion and Coatings Materials Science Characterization and Evaluation of Materials
Content Type	Text
Resource Type	Article
Subject	Mechanics of Materials Materials Science Mechanical Engineering

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