Conductivity Distribution Research Articles

The D-Bar Algorithm Fusing Electrical Impedance Tomography with A Priori Radar Data: A Hands-On Analysis

Electrical impedance tomography (EIT) is an imaging modality that can estimate a visualization of the conductivity distribution inside the human body. However, the spatial resolution of EIT is limited because measurements are sensitive to noise. We investigate a technique to incorporate a priori information into the EIT reconstructions of the D-Bar algorithm. Our paper aims to help engineers understand the behavior of the D-Bar algorithm and its implementation. The a priori information is provided by a radar setup and a one-dimensional reconstruction of the radar data. The EIT reconstruction is carried out with a D-Bar algorithm. An intermediate step in the D-Bar algorithm is the scattering transform. The a priori information is added in this exact step to increase the spatial resolution of the reconstruction. As the D-Bar algorithm is widely used in the mathematical community and thus far has limited usage in the engineering domain, we also aim to explain the implementation of the algorithm and give an intuitive understanding where possible. Different parameters of the reconstruction algorithm are analyzed systematically with the help of the GREIT figures of merit. Even a limited one-dimensional a priori information can increase the reconstruction quality considerably. Artifacts from noisy EIT measurements are reduced. However, the selection of the amount of a priori information and the estimation of its value can worsen the reconstruction results again.

Dual regulation strategy to enhance the electrochemical performance of rich sulfur vacancies NiCo2S4 integrate electrode material for supercapacitors

NiCo2S4 is a promising electrode material for supercapacitors due to its high conductivity and rich elemental valence distribution. However, the low specific capacitance and poor cycling stability of NiCo2S4 electrode material restricted its practical application. In this paper, we proposed a dual regulation strategy of nitrogen-doped carbon cloth current collector (NCC) and the introduction of S vacancies in the NiCo2S4 (NCS) to prepare a high electrochemical performance of integrated electrode material. The introduction of N doping into the CC current collector helped to improve the loading capacity of NCS and contributed to capacitance, while the rich S vacancies in NCS could provide more electroactive sites and increase its electrical conductivity. Benefiting from the synergistic effect of NCC and S vacancies, the fabricated NCS4–0.5/NCC integrated electrode material illustrated an ultra-high specific capacitance of 4075.89 mF cm−2 (2079.53 F g−1) at 1 mA cm−2 and superior electrochemical cycling stability with 80.03% of initial capacitance at a high current density of 20 mA cm−2 after 5000 cycles. Therefore, the present dual regulation strategy provides a new route to fabricate supercapacitor electrode materials with excellent electrochemical properties.

Simultaneous Conductivity and Permeability Reconstructions for Electromagnetic Tomography Using Deep Learning

Electromagnetic tomography (EMT) is a research hotspot in electrical tomography, which has wide application prospect for multiphase flow measurement. The existing EMT usually visualizes the distributions of conductivity or permeability separately. In order to realize the simultaneous imaging of different electromagnetic characteristics in the measurement area and improve the quality of the reconstructed images, a deep learning based multi-parameter EMT method is proposed in this paper. Firstly, the information from the mutual inductance and magnetic induction intensity of the imaging area is measured respectively. Then, the Landweber algorithm is used to reconstruct the initial conductivity and permeability images using above measurements. Finally, the initial images are input into the improved DeepLabv3 network for image segmentation and the images of conductivity and permeability distributions with clear boundary and accurate size and position are output. The images reconstructed by the improved DeepLabv3 network are compared with those from traditional methods, UNet++, LinkNet and PAN networks through the simulation and experiment. The experimental results show that our method achieves RMSE of 0.1667, CC of 0.6984 and SSIM of 0.6542 on average for permeability distribution reconstruction, and RMSE of 0.1907, CC of 0.7791 and SSIM of 0.7538 on average for conductivity distribution reconstruction. These results prove that the proposed method can simultaneously obtain the conductivity and permeability distributions with high-quality reconstructed images. Our code is publicly available at https://github.com/Tougerr/Landweber-DLv3.

Imaging of conductivity distribution based on a combined reconstruction method in brain electrical impedance tomography

Electrical impedance tomography (EIT) is a promising technique in medical imaging. With this technique, pathology-related conductivity variation can be visualized. Nevertheless, reconstruction of conductivity distribution is a severely ill-posed inverse problem which poses a great challenge for the EIT technique. Especially in brain EIT, irregular and multi-layered head structure along with low-conductivity skull brings more difficulties for accurate reconstruction. To address such problems, a novel reconstruction method which combines Tikhonov regularization with denoising algorithm is proposed for imaging conductivity distribution in brain EIT. With the proposed method, image reconstruction of intracerebral hemorrhage in different brain lobes of a three-layer head model is conducted. Besides, simultaneous reconstruction of intracerebral hemorrhage and secondary ischemia is performed. Meanwhile, the impact of noise is investigated to evaluate the anti-noise performance. In addition, image reconstructions under head shape deformation are performed. The proposed reconstruction method is also quantitatively estimated by calculating blur radius and structural similarity. Phantom experiments are carried out to further verify the effectiveness of the proposed method. Both qualitative and quantitative results have demonstrated that the proposed combined method is superior to Tikhonov regularization in imaging conductivity distribution. This work would provide an alternative for accurate reconstruction in EIT based medical imaging.

Intracerebral Hemorrhage Imaging Based on Hybrid Deep Learning With Electrical Impedance Tomography

Intracerebral hemorrhage (ICH) is a common disease which has characteristics of high disability rate and high mortality rate. Rapid detection and continuous monitoring is crucial for diagnosis and treatment of ICH. As a potential technique, electrical impedance tomography (EIT) offers an alternative for brain imaging. With this technique, an image representing conductivity distribution variation is reconstructed and physiological change in the intracerebral region can be provided. However, image reconstruction is an inherently nonlinear and severely ill-posed inverse problem. To solve the problem, a convolutional neural network (CNN) and transformer combined hybrid network (CTHN) is proposed to map the nonlinear relationship between boundary measurement and conductivity distribution. With the hybrid network, high-level features of voltage measurement sequence are extracted and long-range dependencies learning ability can be improved with few layers. To evaluate the performance of the proposed method, extensive simulations are carried out on a three-layer head model with the inclusion which simulates ICH. Image reconstructions are separately performed under noiseless condition, different noise level condition, conductivity variation in three layers and deformed head models. The performance of the proposed method is also tested by the experiments. Both reconstructed images and quantitative evaluations show that ICH can be accurately reconstructed with the proposed approach.

An EIT image reconstruction method based on DenseNet with multi-scale convolution.

Electrical impedance tomography (EIT) is an imaging technique that non-invasively acquires the electrical conductivity distribution within a field. The ill-posed and nonlinear nature of the image reconstruction process results in lower quality of the obtained images. To solve this problem, an EIT image reconstruction method based on DenseNet with multi-scale convolution named MS-DenseNet is proposed. In the proposed method, three different multi-scale convolutional dense blocks are incorporated to replace the conventional dense blocks; they are placed in parallel to improve the generalization ability of the network. The connection layer between dense blocks adopts a hybrid pooling structure, which reduces the loss of information in the traditional pooling process. A learning rate setting achieves reduction in two stages and optimizes the fitting ability of the network. The input of the constructed network is the boundary voltage data, and the output is the conductivity distribution of the imaging area. The network was trained and tested on a simulated dataset, and it was further tested using actual measurement data. The images reconstructed via this method were evaluated by employing root mean square error, structural similarity index measure, mean absolute error and image correlation coefficient in comparison with conventional DenseNet and Gauss-Newton. The results show that the method improves the artifact and edge blur problems, achieves higher values on the image metrics and improves the EIT image quality.

High-Dimensional Uncertainty Quantification in Electrical Impedance Tomography Forward Problem Based on Deep Neural Network

In electrical impedance tomography (EIT), the uncertainty of conductivity distribution may cause the uncertainty in the forward calculation and further affect the inverse problem. In this paper, an improved univariate dimension reduction method based on deep neural network (DNN-UDR) is proposed for the high-dimensional uncertainty quantification in EIT forward problem. Firstly, DNN is studied to build a substitute model for EIT forward problem in order to solve the high-dimensional problem. Three normalized circular finite element models are established with random uniform conductivity distribution. Then UDR is used to analyze and quantify the uncertainty in the simulation with the form of probability. Compared with Monte Carlo simulation (MCS), the probability distribution of voltage is fitted, and the quantification indicators such as mean, variance, variation coefficient and covariance, are also consistent. On the other hand, with the increase of parameter dimensions, DNN-UDR accelerates the computations obviously. This indicates that DNN-UDR is effective and has high structural stability, accurate prediction results and high computational efficiency.

Magneto-Acousto-Electrical Tomography With Nonuniform Static Magnetic Field

Magneto-acousto-electrical tomography (MAET) is a powerful tool for numerous applications, including medical imaging and process tomography. In these applications, and to enable precise reconstruction of images of a given process using MAET, high-quality measurements must be obtained from the target of interest. It is thus obvious that the magnetic field excitation used in these measurements plays an important role in this task. However, the existing reports in the literature to date have usually assumed that the static magnetic field is a uniform field, which is challenging to achieve in practice. In this article, we circumvent this challenge and present a comprehensive mathematical model for MAET in the presence of nonuniform magnetic fields. In the proposed model, a relationship is established between the measurement signal and the reciprocal current density. The conductivity distribution is obtained via a simulation model and compared with the second norm error of the images and the various uniformities of the nonuniform magnetic fields obtained from the imaging results. The model is then validated for different test cases, which range from a simple phantom with three layers to a phantom with a cylindrical geometry. It is concluded that the use of a nonuniform magnetic field can lead to significant changes and improvements in the reconstruction of the conductivity, thus making it possible to use this technique in bedside detection applications.

Electrical Impedance Tomography Image Reconstruction With Attention-Based Deep Convolutional Neural Network

Electrical impedance tomography (EIT) is a promising functional and structural imaging method in process tomography. However, due to the ’soft-field’ nature and the high dependence on the prior information, it often suffers serious artifacts in quantitative analysis. Most recently, EIT image quality has improved significantly because of the state-of-the-art deep learning-based models in the aspect of solving the inverse problem, especially fully convolutional networks (FCN) and V-Net variants. Despite their success, these deep convolutional networks (CNNs) have two limitations: (1) The long-range information transition is frequently lost and the reverse gradient often disappears in deep CNNs; (2) Some novel skip connections, such as residual and dense connections, often occupy substantial computational resources. To overcome these two limitations, we propose V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> A-Net, a new neural architecture based on redesigned feature transited connections by the terms of (1) A pre-reconstructor based on the iterative Newton-Raphson method, which maps the nonlinear function between the measurements and the initial images, (2) Dual cascaded V-Net are combined, which play the role of an encoder and a decoder, respectively, (3) A new parallel attention mechanism via channel attention and coordinate attention to learn the conductivity distributions and boundary-shaped feature separately, and (4) the light-weight skip connections reduce the computational resources (or accelerate the inference speed) of EIT imaging. The V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> A-Net is evaluated by using the multi-phase flow industrial applications, and the results demonstrate that (1) V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> A-Net has better performance in shape reconstruction with sharp ’corner’, (2) V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> A-Net could reconstruct the model accurately where it has some low-contrast conductivity distributions, (3) V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> A-Net enhances the quality of interfaces with the stratified flow, and (4) the pruned V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> A-Net achieves significant speedup compared with the VDD-Net or V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DNet. The analyses show that the average relative error is 0.05, the average correlation coefficient is 0.92, the average structural similarity is 0.92 on the testing datasets. In addition, the average relative cover ratio is 0.97 and the average relative contrast ratio is 0.98 on the testing datasets.

ТЕЧЕНИЕ ГАРТМАНА В СЛОЕ ЖИДКОСТИ С ПРОСТРАНСТВЕННО НЕОДНОРОДНЫМИ СВОЙСТВАМИ

In this study we consider the flow of a spatially-inhomogeneous electrically conductive fluid between parallel planes in a transverse magnetic field. The distributions of electrical conductivity and viscosity of the fluid are given by linear functions. The slopes of these distributions characterize the maximum deviation of the fluid properties from their mean values. We show that inhomogeneity of the fluid properties leads to distortion of the velocity profiles. The resulting profiles are asymmetric and have inflection points. We use a quantity equal to the ratio of flow rates in the upper and lower halves of the layer as a quantitative measure of asymmetry. We determine the relationship between this quantity, the average Hartmann number, and the parameters of the distributions of inhomogeneous properties. We show that starting from a relatively small mean Hartmann number, the inflection points in the velocity profiles appear for any values of the distribution parameters. We provide estimates of characteristic temperatures and concentrations of non-conducting impurity for liquid sodium, at which the described effects appear.

Recycled waste glass usage for construction materials

Recycling glass waste obtained from different industries is an issue in today’s economy. One of the ways of recycling is to produce lightweight ceramic by applying glass waste, clay, and gasifier as raw materials. The present research has been devoted to the evaluation of the influences of different clays extracted from two Latvian quarries (Lielauce un Samini) on the properties of lightweight ceramics. The main criteria of applicated clay as raw materials for producing lightweight ceramic are the following: saturation of SiO2 should be a maximum of 70% and Al2O3 minimum of 12%. The aim of the present research was to elaborate a composition for producing glass ceramic from glass waste with minimum energy consumption with a gasifier. The most important properties of the final product are thermal conductivity, compression strength, volume density, size, granulometry, and pore distribution. Two parameters of them, compression strength and volume density have been tested and analysed in the present research framework. Burning time is one more important additional parameter, which has been considered evaluating the properties of the final product. The obtained volume density is in the range of 226.75 kg/m3 to 475.78 kg/m3 depending on the composition.

Bioinspired Stretchable MXene Deformation-Insensitive Hydrogel Temperature Sensors for Plant and Skin Electronics.

Temperature sensing is of high value in the wearable healthcare, robotics/prosthesis, and noncontact physiological monitoring. However, the common mechanic deformation, including pressing, bending, and stretching, usually causes undesirable feature size changes to the inner conductive network distribution of temperature sensors, which seriously influences the accuracy. Here, inspired by the transient receptor potential mechanism of biological thermoreceptors that could work precisely under various skin contortions, we propose an MXene/Clay/poly(N-isopropylacrylamide) (PNIPAM) (MCP) hydrogel with high stretchability, spike response, and deformation insensitivity. The dynamic spike response is triggered by the inner conductive network transformation from the 3-dimensional structure to the 2-dimensional surface after water being discharged at the threshold temperature. The water discharge is solely determined by the thermosensitivity of PNIPAM, which is free from mechanical deformation, so the MCP hydrogels can perform precise threshold temperature (32 °C) sensing under various deformation conditions, i.e., pressing and 15% stretching. As a proof of concept, we demonstrated the applications in plant electronics for the real-time surface temperature monitoring and skin electronics for communicating between human and machines. Our research opens venues for the accurate temperature-threshold sensation on the complicated surface and mechanical conditions.

Reduced graphene oxide reinforced PDA-Gly-PVA composite hydrogel as strain sensors for monitoring human motion

Hydrogels with soft, skin-friendly properties and high biocompatibility are promising alternatives to traditional sensors. However, balancing electrical conductivity and sensitivity remains a significant challenge. The sensitivity-improved strain sensor was designed by reduced graphene oxide (rGO) reinforced polydopamine (PDA)-glycerol (Gly)-polyvinyl alcohol composite hydrogels (PGPHs). The hydrogels exhibited excellent sensing sensitivity with a gauge factor of 2.78, conductivity of 2.2 S/m, tensile deformation of 200%, fast response time of 370 ms, and recovery time of 260 ms, surpassing those of most previously reported hydrogel-based strain sensors. This improvement can be attributed to the high electrical conductivity and uniform distribution of the rGO associated with Gly and PDA. PGPHs also exhibited an attractive monitoring effect for hand movements and precise detection feedback for the slight dynamics of the pharynx. Hydrogel-based strain sensors have been demonstrated as a potentially sustainable solution for dynamic detection and communication.

Bayesian Image Reconstruction Using Weighted Laplace Prior for Lung Respiratory Monitoring With Electrical Impedance Tomography

The poor spatial resolution of electrical impedance tomography (EIT) limits its use in medical devices because of its highly nonlinear and ill-posed nature. A novel hierarchical block sparse Bayesian learning (BSBL) method is designed for lung respiratory monitoring with EIT. It is its excellent modeling capability and noise robustness that allows BSBL to adaptively explore and exploit the internal conductivity distribution, e.g., block sparsity and intra-block correlation. First, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -nearest neighbor (KNN) strategy is employed to automatically group each block based on the clustering property and incorporated to constrain the intra-block correlation matrix based on the spatial distance. Second, a three-layer hierarchical BSBL model using weighted Laplace (WL) prior is considered to enhance the recovery performance. Finally, an efficient bound optimization (BO) method is performed for Bayesian inference, avoiding the tedious parameter adjustment. This leads to improvements in recovery performance, algorithm robustness, and computational efficiency. Moreover, root mean square error (RMSE), image correlation coefficient (ICC), and relative size coverage ratio (RCR) are applied for quantitative comparisons of image quality. Numerical simulations and in vivo lung respiratory experiments showed that the proposed KNN-BSBL-WL method outperforms existing referenced methods in terms of reconstruction accuracy and computational time. On average, KNN-BSBL-WL obtains the RMSE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$= 0.07\,\,\pm \,\,0.02$ </tex-math></inline-formula> , ICC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$= 0.96\,\,\pm \,\,0.01$ </tex-math></inline-formula> , and RCR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$= 0.98\,\,\pm \,\,0.05$ </tex-math></inline-formula> , which are closest to the true values of the recorded snapshots obtained in the experiments. Meanwhile, the average time of KNN-BSBL-WL is 0.16 s, achieving an acceleration ratio close to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 3$ </tex-math></inline-formula> compared with the referenced KNN-BSBL method. Therefore, the proposed method is an efficient and robust means of imaging reconstruction, which further improves the feasibility of EIT in respiratory clinical applications.

Recovery algorithm of electrical conductance distribution in the volume of the object of research in electrical impedance tomography

An efficient, rigorously substantiated algorithm for solving electrical impedance tomography problems is proposed and tested. The features of the algorithm are the use of a methodology based on solving conditionally correct inverse problems and the presence of a search stage for the optimal current configuration that provides the maximum sensitivity of the problem functionals to the desired parameters, which allows to speed up the iterative process and improve the accuracy of the problem solution. The algorithm can be used in medicine and industry to solve diagnostic problems.

A finite volume method for global electromagnetic induction forward modeling on collocated unstructured grids

Global electromagnetic induction provides an efficient way to probe the electrical conductivity in the Earth’s deep interior. Owing to the increasing geomagnetic data especially from high-accuracy geomagnetic satellites, inverting the Earth’s three-dimensional conductivity distribution on a global scale becomes attainable. A key requirement in the global conductivity inversion is to have a forward solver with high-accuracy and efficiency. In this study, a finite volume method for global electromagnetic induction forward modeling is developed based on unstructured grids. Arbitrary polyhedral grids are supported in our algorithms to obtain high geometric adaptability. We employ a cell-centered collocated variable arrangement which allows convenient discretization for complex geometries and straightforward implementation of multigrid technique. To validate the method, we test our code with two synthetic models and compare our finite volume results with an analytical solution and a finite element numerical solution. Good agreements are observed between our solution and other results, indicating acceptable accuracy of the proposed method.

A Solution to TMR-EMT Blind Spots Based on Biaxial TMR

Electromagnetic tomography based on tunneling magnetoresistance (TMR-EMT) is a new type of electrical tomography technology. In contrast to the traditional EMT that uses the magnetic flux obtained from the coil to reconstruct the electrical conductivity distribution in the region of interest (ROI), the TMR-EMT reconstructs the magnetic permeability distribution in the ROI by the boundary magnetic induction intensity in the direction of the sensitive axis of the uniaxial TMR (Uni-TMR). However, the Uni-TMR measurements are influenced by the direction of boundary magnetic induction intensity at the Uni-TMR location, when the direction is orthogonal to the sensitive axis direction, the Uni-TMR cannot measure the boundary magnetic induction intensity, and such locations are named TMR-EMT blind spots in this paper. The blind spots will exacerbate the ill-condition of inverse problem solving of TMR-EMT, and even lead to failure of imaging. Aiming at the problem of TMR-EMT blind spots, this paper proposes a solution to measure the boundary magnetic induction intensity using biaxial TMR (Bi-TMR). The Bi-TMR consists of two Uni-TMRs whose sensitive axis directions are orthogonal to each other. The Bi-TMR measurement is obtained by squaring the sum of the squares of these two Uni-TMR measurements, which is unaffected by the boundary magnetic induction intensity direction. Simulation and experimental results show that this solution does not have the blind spots, and can obtain complete boundary magnetic induction intensity in plane.

Machine Learning Applied to EIT: Image Reconstruction, Displaced Electrodes and Boundary Shape

Electrical Impedance Tomography (EIT) is a noninvasive, nonradioactive technique used to obtain an internal image of a body or object. This work addresses several EIT-related problems using Artificial Neural Networks (ANNs). The EIT method consists of performing measurements of electrical potentials in a set of electrodes while different patterns of electrical current are applied. This work proposes the use of a modified version of LeNet-5 to determine the conductivity distribution within the domain, resulting in an image of the interior of the boundary in which the electrodes are positioned. There are two sources of errors in the usage of EIT in medical images. The first one is the displacement of the electrodes, which are supposed to be equidistant; and the second one is the modification of the boundary shape. The human body is not rigid, and due to breathing the boundary shape changes, leading to the displacement of electrodes. A modified version of LeNet-5 is also used to determine the changes of the boundary shape, and classifiers, such as Support Vector Machine (SVM), Random Forest and K-nearest Neighbors (KNN), are used to determine the displaced electrodes. In this work, all methods are used apart. Then, the reconstruction algorithm does not yet consider these sources of errors.

A finite volume method for global electromagnetic induction forward modeling on collocated unstructured grids

Global electromagnetic induction provides an efficient way to probe the electrical conductivity in the Earth’s deep interior. Owing to the increasing geomagnetic data especially from high-accuracy geomagnetic satellites, inverting the Earth’s three-dimensional conductivity distribution on a global scale becomes attainable. A key requirement in the global conductivity inversion is forward solvers with high-accuracy and efficiency. In this paper, a finite volume method for global electromagnetic induction forward modeling is developed based on unstructured grids. Arbitrary polyhedral grids are supported in our algorithms to obtain high geometric adaptability. We employ a cell-centered collocated variable arrangement which allows convenient discretization for complex geometries and straightforward implementation of multigrid technique. To validate the method, we test our code with two synthetic models and compare our finite volume results with an analytical solution and another numerical solution by a finite element method. Good agreements are observed between our solution and other results, indicating acceptable accuracy of the proposed method.

Multiphysics Coupled Simulation of Electrolytic Machining of Toothed Grooves of Contrate Gear

In the field of aviation, the contrate gear is an important component, the material of the contrate gear is the basis for ensuring the reliability and high performance of the aircraft engine, the commonly used material for contrate gear is 20Cr2Ni4A. In order to fully understand the electrolytic machining of the toothed groove of the contrate gear, a multiphysics coupling model of the electric field, the gas-liquid two-phase flow field and the temperature field was established to study the distribution of bubble rate, temperature, conductivity and current density in the processing gap. Simulation results show that the temperature and bubble rate gradually increase along the process direction in the processing gap, the conductivity and current density of the electrolyte gradually decrease.