NDLI: Predicting the Optimum Process Parameters for Seamless Tube Rolling with FEM and BPNN

Content Provider	IEEE Xplore Digital Library
Author	Jian hua Hu Yuan hua Shuang
Copyright Year	2009
Abstract	The objective of the designer in tube rolling is to choose the process parameters that provide for acceptable tube diameter, wall thickness at the end of the rolling process. Nowadays, the empirical know-how of the designer is still decisive for the process parameters. It’s often requires very costly trial-and-error, and it is complicated and time-consuming.The paper presents a unique reverse engineering approach to the design of process parameters. It uses finite element simulation to obtain the data as the training samples of neural network, and artificial neural network for prediction the optimum process parameters. Instead of using the process parameters as input values and rolling outcomes as output values, the BPNN model configures the tube parameters as inputs and the process parameters as outputs. A group data of tube parameters are inputted to the trained neural network, and the optimum parameters are obtained in the output. In addition, an example of continuous tube rolling is included to demonstrate the efficacy of this approach to tube rolling.
Starting Page	661
Ending Page	665
File Size	389002
Page Count	5
File Format	PDF
ISBN	9780769535074
DOI	10.1109/CSIE.2009.307
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Process design Design methodology Reverse engineering Artificial neural networks Predictive models Finite element methods FEM Computer science Design engineering Materials science and technology BPNN Neural networks tube rolling
Content Type	Text
Resource Type	Article

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