Abstract
This paper presents highly robust, novel approaches to solving the forward and inverse problems of an Electrical Capacitance Tomography (ECT) system for imaging conductive materials. ECT is one of the standard tomography techniques for industrial imaging. An ECT technique is nonintrusive and rapid and requires a low burden cost. However, the ECT system still suffers from a soft-field problem which adversely affects the quality of the reconstructed images. Although many image reconstruction algorithms have been developed, still the generated images are inaccurate and poor. In this work, the Capacitance Artificial Neural Network (CANN) system is presented as a solver for the forward problem to calculate the estimated capacitance measurements. Moreover, the Metal Filled Fuzzy System (MFFS) is proposed as a solver for the inverse problem to construct the metal images. To assess the proposed approaches, we conducted extensive experiments on image metal distributions in the lost foam casting (LFC) process to light the reliability of the system and its efficiency. The experimental results showed that the system is sensible and superior.
Highlights
Electrical Capacitance Tomography (ECT) is a wellestablished technique for imaging material distribution inside closed pipes and flasks
The final Capacitance Artificial Neural Network (CANN) model is composed of one hidden layer with twenty-five neurons for best modeling the forward problem in the ECT system
Every two electrodes comprise a capacitive sensor at a time, with one electrode connected to the source signal working as a transmitter and the other works as a receiver
Summary
Electrical Capacitance Tomography (ECT) is a wellestablished technique for imaging material distribution inside closed pipes and flasks. It is assumed that the fuzzy parameters, rules, and membership functions extract the system behavior embedded in the input-output data The expert, in this case, verifies based on his knowledge whether the fuzzy model gained the required characteristics of the actual system or not. This paper presents novel methods for solving the forward and inverse problems of the ECT system for conductive imaging materials in the lost foam casting (LFC) process. A novel method for solving the inverse problem is presented This method applies a trained fuzzy system to enhance the quality of the reconstructed images describing the metal distribution in the LFC process. Because of the advantages of the ECT system compared with the inferred and X-ray technologies, the LFC process implements the ECT system for imaging the metal during the casting process [25]
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