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

Authors: Amit Sata

Addresses: Mechanical Engineering Department, MEFGI, Faculty of Engineering, Rajkot-Morbi Road, Rajkot 360003, India

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 networks; ANNs; multivariate regression; MVR; ceramic inclusion; flash; misrun; shrinkage; slag inclusion.

DOI: 10.1504/IJMR.2016.082819

International Journal of Manufacturing Research, 2016 Vol.11 No.4, pp.356 - 373

Received: 16 Aug 2016
Accepted: 25 Oct 2016
Published online: 12 Mar 2017 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article