Finite element mesh generation

Wördenweber, Burkard

doi:10.1016/0010-4485(84)90087-3

Cited by 49 publications

(6 citation statements)

References 3 publications

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“…The inversion applies a mesh of 1,023 elements and 620 nodes to avoid the so-called "inverse crime" [51]. All mesh models applied in this study are generated automatically according to [52]. A homogeneous conductivity model with σ 0 = 0.33 S/m is employed for both conventional EIT and CEIT as the starting model.…”

Section: A Modelingmentioning

confidence: 99%

CT Image-Guided Electrical Impedance Tomography for Medical Imaging

Zhang

Liu

et al. 2020

IEEE Trans. Med. Imaging

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This study presents a computed tomography (CT) image-guided electrical impedance tomography (EIT) method for medical imaging. CT is a robust imaging modality for accurately reconstructing the density structure of the region being scanned. EIT can detect electrical impedance abnormalities to which CT scans may be insensitive, but the poor spatial resolution of EIT is a major concern for medical applications. A cross-gradient method has been introduced for oil and gas exploration to jointly invert multiple geophysical datasets associated with different medium properties in the same geological structure. In this study, we develop a CT image-guided EIT (CEIT) based on the cross-gradient method. We assume that both CT scanning and EIT imaging are conducted for the same medical target. A CT scan is first acquired to help solve the subsequent EIT imaging problem. During EIT imaging, we apply cross gradients between the CT image and the electrical conductivity distribution to iteratively constrain the conductivity inversion. The crossgradient based method allows the mutual structures of different physical models to be referenced without directly affecting the polarity and amplitude of each model during the inversion. We apply the CEIT method to both numerical simulations and phantom experiments. The effectiveness of CEIT is demonstrated in comparison with conventional EIT. The comparison shows that the CEIT method can significantly improve the quality of conductivity images.

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Section: A Modelingmentioning

confidence: 99%

CT Image-Guided Electrical Impedance Tomography for Medical Imaging

Zhang

Liu

et al. 2020

IEEE Trans. Med. Imaging

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“…FEA preprocessing and mesh gen eration strategies have been in existence for years [1,2] while the meshing infrastructure required for carrying out efficient AM pro cess modeling using adaptive mesh generation (primarily static) were developed more recently [3,4]. FEA preprocessing and mesh gen eration strategies have been in existence for years [1,2] while the meshing infrastructure required for carrying out efficient AM pro cess modeling using adaptive mesh generation (primarily static) were developed more recently [3,4].…”

Section: Threementioning

confidence: 99%

An Integrated Approach to Additive Manufacturing Simulations Using Physics Based, Coupled Multiscale Process Modeling

Pal

Patil²,

Zeng

et al. 2014

Journal of Manufacturing Science and Engineering

100

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The complexity o f local and dynamic thermal transformations in additive manufacturing (AM) processes makes it difficult to track in situ thermomechanical changes at different length scales within a part using experimental process monitoring equipment. In addition, in situ process monitoring is limited to providing information only at the exposed surface o f a layer being built. As a result, an understanding o f the bulk microstructural transformations and the resulting behavior o f a part requires rigorous postprocess microscopy and mechanical testing. In order to circumvent the limited feedback obtained from in situ experiments and to better understand material response, a novel 3D dislocation density based thermomechanical finite element framework has been developed. This framework solves fo r the in situ response much faster than currently used state-of-the-art modeling software since it has been specifically designed fo r AM platforms. This modeling infrastructure can predict the anisotropic performance o f AM-produced components before they are built, can serve as a method to enable in situ closed-loop process control and as a method to predict residual stress and distortion in parts and thus enable support structure optimization. This manuscript provides an overview o f these software modules which together form a robust and reliable AM software suite to address future needs fo r machine development, material development, and geometric optimization. Journal of Manufacturing Science and EngineeringCopyright© involved in AM processing and validated software tools with the capabilities to predict process effects are needed both in academia and industry. These software tools will enable future machines to be more efficient, scientists and researchers to develop new material alloys specifically tweaked for cooling rates experienced during AM, and designers to more fully explore the geometric freedom and functionality desired by the end-user while still maintaining the required strength, fatigue, corrosion, or other attributes. F ig . 4 (a ) O p tic a l m ic r o g r a p h o f a n E B M -m a d e T i 6 /4 m ic r o s t r u c t u r e s h o w in g h e x a g o n a l p r io r p g r a in m o tifs a n d (b ) m ic r o s t r u c t u r a lly in f o r m e d m e s h , a n d ( c ) in -p la n e m e s h c o m p le x ity [1 8 ]

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“…As the problems in real life are complex therefore adequate precision in the area of high variation may be acquired through a high node density (Wördenweber, 1984). In case of many real life problems it may not be apparent to user that how the solution would look like and where a fine mesh is needed.…”

Section: Adaptive Finite Element Analysismentioning

confidence: 99%

Case Based Reasoning Support for Adaptive Finite Element Analysis: Mesh Selection for an Integrated System

Khan¹,

Chaudhry

Sarosh

2014

APR

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An Adaptive Finite Element Analysis Integrated System supported through application of Case Based Reasoning (CBR) methodology is being proposed in this paper. The approach is fruitful for selection of an initial mesh from a library of solutions to initiate analysis process, as already tested optimal mesh will have lesser refinement iterations. The optimal mesh distribution, represented by object-oriented method, can be easily adapted to the topology of new problem in same domain. An integrated and universal structural analysis system models human reasoning by forming solutions through the retrieval and adaptation of successful strategies used in the past. Basic insight of two distinct subjects along with resolution of involved issues and integration strategy for development of an intelligent system is elaborated here. The research explains an algorithm for case retrieval and mesh generation procedures based on the principles of mapping method.

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Finite element mesh generation

Cited by 49 publications

References 3 publications

CT Image-Guided Electrical Impedance Tomography for Medical Imaging

CT Image-Guided Electrical Impedance Tomography for Medical Imaging

An Integrated Approach to Additive Manufacturing Simulations Using Physics Based, Coupled Multiscale Process Modeling

Case Based Reasoning Support for Adaptive Finite Element Analysis: Mesh Selection for an Integrated System

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