Abstract
Image reconstruction for industrial applications based on Electrical Capacitance Tomography (ECT) has been broadly applied. The goal of image reconstruction based ECT is to locate the distribution of permittivity for the dielectric substances along the cross-section based on the collected capacitance data. In the ECT-based image reconstruction process: (1) the relationship between capacitance measurements and permittivity distribution is nonlinear, (2) the capacitance measurements collected during image reconstruction are inadequate due to the limited number of electrodes, and (3) the reconstruction process is subject to noise leading to an ill-posed problem. Thence, constructing an accurate algorithm for real images is critical to overcoming such restrictions. This paper presents novel image reconstruction methods using Deep Learning for solving the forward and inverse problems of the ECT system for generating high-quality images of conductive materials in the Lost Foam Casting (LFC) process. Here, Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models were implemented to predict the distribution of metal filling for the LFC process-based ECT. The recurrent connection and the gating mechanism of the LSTM is capable of extracting the contextual information that is repeatedly passing through the neural network while filtering out the noise caused by adverse factors. Experimental results showed that the presented ECT-LSTM-RNN model is highly reliable for industrial applications and can be utilized for other manufacturing processes.
Highlights
Computed tomography is a technology to image materials distributions in closed vessels
Compared with convolutional and fully-connected layers, the recurrent connection and the gating mechanism of the Long Short Term Memory (LSTM) is capable of extracting the contextual information that is repeatedly passing through the neural network while filtering out the noise caused by adverse factors
Electrical Capacitance Tomography (ECT)-LSTM-Recurrent Neural Network (RNN): FORWARD AND INVERSE PROBLEMS SOLVER This work’s contribution is the evolution of an image reconstruction model for the distribution of metal filling for the Lost Foam Casting (LFC) process based on ECT using advanced deep learning methods
Summary
Computed tomography is a technology to image materials distributions in closed vessels. In [20], the analog Hopfield network was used to address the inverse problem based on NN with a multi-criteria optimization image reconstruction approach This image reconstruction approach was evaluated on a set of independent capacitance measurements to reconstruct the permittivity distribution. B. RESEARCH OBJECTIVES This paper presents novel image reconstruction methods using DL for solving the ECT problems for generating highquality images of conductive materials in the Lost Foam Casting (LFC) process. The training data are generated based on different models developed to describe the molten metal’s behavior during the casting process [41] They are stimulated by a finite element method to calculate the capacitance measurements related to all different distributions. The proposed ECT-LSTM-RNN inverse problem solver can create significantly high quality images than those produced by the traditional algorithms, with a lower reconstruction cost. Afterwards, the LSTM-RNN takes the measurements as inputs to produce the final reconstructed image G, which represents the metal’s distribution inside the imaging area
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