JSM 2004 - Toronto

Abstract #302161

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Activity Number: 232
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #302161
Title: Comparative Analysis for Artificial Neural Networks and Multiple Linear Regression for Powder Hardfacing Processes
Author(s): Shu-Yi Tu*+ and Jen-Ting Wang and Ming-Der Jean
Companies: University of Michigan, Flint and SUNY, Oneonta and Yung-Ta Institute of Technology & Commerce
Address: , Flint, MI, 48502,
Keywords: artificial neural networks ; multiple linear regression ; Taguchi methods ; powder hardfacing
Abstract:

Artificial neural networks (ANNs) are an information processing system with the ability to learn, recall, and generalize from training data. Experiments involving numerous variables are usually analyzed using the analysis of variance (ANOVA) or multiple regression methods. We compare ANNs and the various multiple linear regression analysis in a powder hardfacing process that involves eight variables. The experimental results show that the ANN model effectively performs a better accuracy at predicting the response variable than the multiple linear regression models.

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