ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

A Feature-Based Solution to Forward Problem in Electrical Capacitance Tomography of Conductive Materials

Abdelrahman, Mohamed A and Gupta, Ankush and Deabes, Wael A (2011) A Feature-Based Solution to Forward Problem in Electrical Capacitance Tomography of Conductive Materials. In: Transactions on Instrumentation and Measurement, 60 (2). pp. 430-441.

[img] PDF
Feature.pdf - Published Version
Restricted to Registered users only

Download (1523Kb) | Request a copy
Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumb...

Abstract

A new feature-based technique is introduced to solve the nonlinear forward problem (FP) of the electrical capacitance tomography with the target application of monitoring the metal fill profile in the lost foam casting process. The new technique is based on combining a linear solution to the FP and a correction factor (CF). The CF is estimated using an artificial neural network (ANN) trained using key features extracted from the metal distribution. The CF adjusts the linear solution of the FP to account for the nonlinear effects caused by the shielding effects of the metal. This approach shows promising results and avoids the curse of dimensionality through the use of features and not the actual metal distribution to train the ANN. The ANN is trained using nine features extracted from the metal distributions as input. The expected sensors readings are generated using ANSYS software. The performance of the ANN for the training and testing data was satisfactory, with an average root-mean-square error equal to 2.2%.

Item Type: Journal Article
Additional Information: Copyright 2011 IEEE. Personal use of this material is permitted.However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: Artificial neural network (ANN); electrical capacitance tomography (ECT); forward problem (FP); lost foam casting (LFC)
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering (Formerly, Aeronautical Engineering)
Date Deposited: 01 Mar 2011 11:23
Last Modified: 01 Mar 2011 11:23
URI: http://eprints.iisc.ernet.in/id/eprint/35820

Actions (login required)

View Item View Item