Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Investment casting defect prediction using neural network and multivariate regression along with principal component analysis

Investment casting defect prediction using neural network and multivariate regression along with... Investment cast parts can contain different types of internal defects like ceramic inclusion, flash, misrun, shrinkage, and slag inclusion. Their occurrence can be prevented by prediction from a given composition and process parameter set. In this work, we explore the application of artificial neural network (ANN) and multivariate regression (MVR) to predict a wider range of investment casting defects based on real-time industrial data. The data of 24 parameters related to process, chemical composition, and defects was collected from about 500 heats in an industrial investment casting foundry. This was reduced to ten principal components using principal component analysis (PCA). Different six ANN training models were used to train a portion of the data and used for prediction. The MVR was also employed on the same data for prediction, and its results were compared with ANN models. The best results were obtained by ANN with Levenberg and Marquardt algorithm. [Received 16 August 2016; Accepted 25 October 2016] Keywords: investment casting; casting defects; principal component analysis; PCA; artificial neural network; ANN; multivariate regression, MVR. Reference to this paper should be made as follows: Sata, A. (2016) ` and multivariate regression along with principal component analysis', Int. J. Manufacturing Research, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Manufacturing Research Inderscience Publishers

Investment casting defect prediction using neural network and multivariate regression along with principal component analysis

Download PDF
18 pages

Loading next page...
 
/lp/inderscience-publishers/investment-casting-defect-prediction-using-neural-network-and-gL2DFmIYD4

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Inderscience Publishers
Copyright
Copyright © 2016 Inderscience Enterprises Ltd.
ISSN
1750-0591
eISSN
1750-0605
DOI
10.1504/IJMR.2016.082819
Publisher site
See Article on Publisher Site

Abstract

Investment cast parts can contain different types of internal defects like ceramic inclusion, flash, misrun, shrinkage, and slag inclusion. Their occurrence can be prevented by prediction from a given composition and process parameter set. In this work, we explore the application of artificial neural network (ANN) and multivariate regression (MVR) to predict a wider range of investment casting defects based on real-time industrial data. The data of 24 parameters related to process, chemical composition, and defects was collected from about 500 heats in an industrial investment casting foundry. This was reduced to ten principal components using principal component analysis (PCA). Different six ANN training models were used to train a portion of the data and used for prediction. The MVR was also employed on the same data for prediction, and its results were compared with ANN models. The best results were obtained by ANN with Levenberg and Marquardt algorithm. [Received 16 August 2016; Accepted 25 October 2016] Keywords: investment casting; casting defects; principal component analysis; PCA; artificial neural network; ANN; multivariate regression, MVR. Reference to this paper should be made as follows: Sata, A. (2016) ` and multivariate regression along with principal component analysis', Int. J. Manufacturing Research,

Journal

International Journal of Manufacturing Research – Inderscience Publishers

Published: Jan 1, 2016

There are no references for this article.