ON THE FEATURES OF ELECTROMAGNETIC FINITE ELEMENT ANALYSIS SOFTWARE PACKAGES

Abd. A. Arkadan , in Finite Elements, Electromagnetics and Design, 1995

16.3 Preprocessor

When a differential or integral method is used for the analysis of a device, the preprocessor is used to define the geometry of the device and provide the material property for further processing by the solver. In a preprocessor a computer model is created and the data is stored in computer files to be used by the solver or to remodel and reuse in future. This process is known as geometric modeling.

The features of interest for the preprocessor are listed below and labeled as features PR1 through PR5 — see Fig. 16.3.1.

Figure 16.3.1. Preprocessors

PR1 Solid Modeling: Solid modeling has its advanced features in general applicability with less user interaction.

PR2 Automatic Meshing: First the grid points are placed throughout the region to be meshed and then the Delaunay tesselation algorithm or an octree mesh generator is used for meshing the region.

PR3 Adaptive Meshing: The adaptive mesh generator reduces the solution error by reducing the size and increasing the number of elements in the areas of the model where the error is high.

PR4 Interactive User Interface

PR5 Parametric Modeling: This is a feature-based design method in which parts can be described by parameters (Brauer 1993).

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Modelling the structure and behaviour of 2D and 3D woven composites used in aerospace applications

D.S. Ivanov , S.V. Lomov , in Polymer Composites in the Aerospace Industry, 2015

2.2.6 Paths forward

As the history of research shows, reconstruction of the internal geometry demands complex solid modelling of yarn volumes. Perhaps the most promising concept is direct meso-scale modelling of fabrics. Miao et al. [80] have employed the digital element method where yarns are modelled as chains of truss elements. The output example of such an approach is shown in [81] for the case of 3D woven composite. A similar concept has been employed by Mahadik and Hallett [82] for modelling 3D angle interlock fabrics. Iarve [68] has also applied this approach to get braided geometry and handle the problem of nesting. Pickett et al. [67] have reconstructed a fragment of three-ply triaxial braided composites by (1) modelling braiding process with 1D beam elements, (2) inflating yarns along with solving contact problem. Stig and Hallström [40] employed a similar strategy to reproduce the yarn geometry of a fragment of a 3D woven composite. Robitaille et al. [83] started 3D modelling of yarn volumes considering the 2D geometries of the yarn volume projections on unit cell faces. Then tow surfaces are constructed based on predefined yarn midlines and interaction of yarns with unit cell boundaries as well as with each other. Hivet et al. [84,85] has introduced a so-called consistent model for the nonintersecting yarn volumes with a variable nonsymmetrical cross-sectional shape of the yarn.

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Modeling of 2D and 3D woven composites

Dmitry S. Ivanov , Stepan V. Lomov , in Polymer Composites in the Aerospace Industry (Second Edition), 2020

2.2.3.3 Paths forward

As the history of research shows, reconstruction of the internal geometry demands complex solid modeling of yarn volumes. Perhaps, the most promising concept is direct mesoscale modeling of fabrics. Miao et al. [100] have employed the digital element method, where yarns are modeled as chains of truss elements. The output example of such an approach is shown in Ref.[101] for the case of 3D woven composite. A similar concept has been employed by Mahadik and Hallett [102], Green et al. [103], El Said et al. [98,99], Joglekar and Pankow [104], Tsukrov et al. [48] for modeling 3D woven fabrics and Thompson et al. [105] for 2D textile laminate. Iarve [86] has also applied this approach to get braided geometry and handle the problem of nesting. Pickett et al. [85] have reconstructed a fragment of three-ply triaxial braided composites by (1) modeling braiding process with 1D beam elements, (2) inflating yarns along with solving contact problem. Stig and Hallström [49] employed a similar strategy to reproduce the yarn geometry of a fragment of a 3D woven composite. Solid modeling of yarns volumes under compaction as preprocessor for the mechanical volume has been suggested by Grail et al. [83] and demonstrated for the case of nested laminate. Nilakantan et al. [106] suggested an iterative algorithm of unit cell based on the thermal growth of yarn volumes.

Robitaille et al. [107] started 3D modeling of yarn volumes considering the 2D geometries of the yarn volume projections on unit cell faces. Then tow surfaces are constructed based on predefined yarn midlines and interaction of yarns with unit cell boundaries, as well as with each other. Hivet et al. [108,109] have introduced a so-called model for the non-intersecting yarn volumes with a variable nonsymmetrical cross-sectional shape of the yarn. Wendling et al. [96] developed an algorithm for modeling 3D interlock fabrics based on detailed considerations of various yarn interaction modes. Isart et al. [110,111] suggested an analytical approach to high fidelity reconstruction of orthogonal interlock woven composites based on examination of composite cross-sections. Another modeling philosophy is based on a direct conversion of micro-CT measurements into a voxel model of representative elements. Good examples of such an approach have been presented by Straumit et al. [112–114], Vanaerschot et al. [115–118], Sevenois et al. [87], Rinaldi et al. [119]. This approach, although requiring high-resolution measurements of the architecture and numerical algorithms to track fiber orientation, allows releasing assumptions associated with geometrical or mechanical modeling of textile reinforcement.

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3D printed microfluidic devices and applications

Sui Ching Phung , ... Mei He , in Microfluidic Devices for Biomedical Applications (Second Edition), 2021

17.2.1 Materials and methods

Three-dimensional design is always difficult within conventional microfabrication, due to complicated protocols for accurate alignment and multilayer bonding. Configured with smartphone solid modeling app (AutoCAD 360), a laptop-sized 3D printer (D3 ProJet 1200, 30-μm resolution) was used for fabrication of monolithic microfluidic chips. VisiJetFTX Clear resin consisting of triethylene glycol diacrylate, isobornyl methacrylate, and 2%–3% photoinitiator phenylbis (2,4,6-trimethylbenzoyl)-phosphine oxide was used to produce transparent 3D microfluidic chips per vendor's instruction. Printed 3D devices were cleaned using isopropyl alcohol and blown dry. The uncured resin within microchannel was flushed out by an air compressor. A field emission scanning electron microscope (SEM) characterized printed microstructures using a focus ion beam (FEI Versa) after gold sputtering coating in 20-nm thickness. Ethylene glycol chemistry was used to develop the hydrophilic surface of 3D printed devices: 1.82   M potassium hydroxide (KOH) was mixed into pure ethylene glycol solution (Sigma-Aldrich, anhydrous, 99.8%) and used to soak 3D devices at 55°C for 2   h. When needed, we also can bond the 3D printed objects with glass materials when 3% of photoinitiator 2-(2-bromoisobutyryloxy)ethyl methacrylate (BrMA, Sigma-Aldrich) was introduced into clear resin and mixed. The Silane-Prep glass slides pretreated with aminoalkylsilane (Sigma-Aldrich) were used to bond the 3D printed layer with open channels and microstructures. The clean 3D layer annealed very well with the glass slides. The two annealed layers were then exposed to 365-UV for 8   min. The bonding was irreversible via covalent chemical bond.

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COMPUTER-AIDED DESIGN

Dominick Rosato , Donald Rosato , in Plastics Engineered Product Design, 2003

Engineered personal computer

CAD projects often range from simple 2-D drawings to graphics-intensive engineering applications. Computationally intensive number crunching in 3-D surface and solid modeling, photo-realistic rendering, and finite element analysis applications demand a great deal from a personal computer (PC). Careful selection of a PC for these applications requires an examination of the capabilities of the CPU, RAM capacity, disk space, operating system, network features, and graphics capabilities. The industry advances quickly, especially in microprocessor capabilities.

There are minimum requirements of PC configurations for various CAD applications. This area of development has more capability than what is reviewed here and because of its rapid advances whatever listing exists is continually outdated.

For 2-D drafting applications, a low-end PC is sufficient. This denotes a PC equipped with a 486 processor running on a 16-bit DOS operating system with 16 MB of RAM, or Microsoft Windows with 32 MB of RAM. The operating system needs 4 MB, most drafting applications require at least 8 MB of RAM, and Windows holds as much data as possible in RAM. Eight K of on-board cache memory and 256 K to 512 K of external cache for faster response is recommended as a minimum. A 500-MB, fast SCSI (smaller computer system interface) hard drive is the minimum. SCSI is a type of bus used to support local disk drives and other peripherals. Five hundred-MB is considered minimum because CAD files require approximately 150 MB, and the operating system itself usually requires about 100 MB. A 16 inch high-resolution (1024 × 768), 256-color monitor should also be considered a minimum requirement. CD-ROM drives and fast modems with transfer rates of 28,800 band are essential for non-networked tasks.

For 3-D modeling and FEA applications, a Pentium 100 MHz processor running on a 32-bit Windows NT operating system works significantly better. The minimum memory requirement for Windows NT is 32 MB and the operating system requires 160 MB of hard-drive space. The system should have a 16-K internal cache with 256 K to 512 K of external cache for increased performance. The monitor for these applications should be 21 inch with 0.25 ultrafine dot pitch, high resolution (1600 × 1280), and 65,536 colors Peripheral component interconnect (PCI) local bus (a high-bandwidth, processor-independent bus) and a minimum I-GB SCSI-2 hard drive are also required.

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Advanced Dimensions

Elliot J. Gindis , Robert C. Kaebisch , in Up and Running with AutoCAD 2019, 2018

13.3 Introduction to Constraints

AutoCAD was always a top-notch 2D drafting software application but one with a bit of an inferiority complex. Not in regard to any other 2D competitors but rather toward 3D solid modeling and design software. These sophisticated products were developed in their own world, apart from AutoCAD, via development firms that never competed against or had anything to do with Autodesk or architecture. These programs included CATIA, NX, Pro-Engineer (now called Creo), and SolidWorks among others. Their developers invented quite a few revolutionary concepts and methods that transformed the way engineering design is done. Autodesk eventually jumped on board with Inventor and later Revit, both of which are excellent 3D products (one for engineers and the other for architects).

That left the AutoCAD development team looking at how its software could be enhanced by incorporating some of these sophisticated 3D tools without fundamentally altering AutoCAD's identity or formula for success. If you go on to study AutoCAD 3D, you will notice how many "borrowed" concepts found their way into the software as AutoCAD reached ever further into the high-end 3D world.

Some of these concepts also found their way into 2D, such as the ones we are about to discuss, namely, Parametrics, which is really two topics, geometric constraints and dimensional constraints; the latter is also referred to as dimension-driven design. If you are an engineer or an engineering school student who used the previously mentioned 3D solid modeling software, you quickly recognize these concepts. AutoCAD literally took a page out of their playbook. Let us discuss each concept separately.

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Cross Wedge Rolling for Making Multi-Scaled Metallic Parts and Structures

Baoyu Wang , Junling Li , in Encyclopedia of Materials: Metals and Alloys, 2022

Finite element simulation model

Taking the commercial software DEFORM as an example, the establishment process of a finite element numerical simulation model of cross-wedge rolling is briefly introduced.

(1)

Establishment of a geometric model.

Third-party 3D software can be used for solid modeling due to the limitations of 3D modeling in DEFORM software; thereafter, the solid modeling can be saved into a specific format and imported into DEFORM for finite element analysis. Firstly, the dimensions of the die, guide plate, rolled parts and other geometric bodies are determined and modeled using third-party 3D software, such as PRO/E and SolidWorks, based on the requirements. Then, solid modeling is carried out in 3D software. Secondly, these are then saved into a specific format, such as. stl. Thirdly, the solid models are then imported into DEFORM, and the relative position between each solid model is determined, as shown in Fig. 14.

Fig. 14

Fig. 14. Geometric model of cross-wedge rolling in the assembly position.
(2)

Establishment of a finite element model.

It is difficult to perfectly reproduce the forming process due to the complicated deformation mechanism and multiple influencing factors. However, by simplifying and assuming certain influencing factors, the accuracy and reliability of a finite element model can be guaranteed.

Firstly, except for the billet, the rollers and guide plates undergo little plastic deformation during cross-wedge rolling. Therefore, they can be explicitly set as rigid bodies. The billet is defined as a plastic deformable body composed of a continuous medium with isotropy. Field variables such as stress, strain and temperature are continuously changing.

Secondly, half of the model can be developed by applying the symmetrical boundary to speed up the calculation process and reduce memory consumption when there is a characteristic of symmetrical rolling in cross-wedge rolling. Additionally, the mesh type and size are related to calculation time, memory footprint and solution accuracy. The appropriate type and size can be chosen according to the shape and geometric size of the billet. Local mesh refinement can be performed to improve calculation accuracy without affecting calculation speed.

Thirdly, the definition of contact relationship includes the friction model and thermal boundaries. DEFORM provides two types of friction model: coulomb and shear friction. It can also customize friction expressions according to forming characteristics. The definition of thermal boundaries includes contact heat transfer, convection heat transfer and radiant heat transfer, which needs to be defined individually.

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Geometric analysis and computation using layered depth-normal images for three-dimensional microfabrication

Tsz-Ho Kwok , ... Charlie C.L. Wang , in Three-Dimensional Microfabrication Using Two-Photon Polymerization (Second Edition), 2020

3.2 An LDNI-based geometric computational framework

The framework of the LDNI-based geometric computation method is shown in Fig. 5.7.

Fig. 5.7

Fig. 5.7. The framework of the LDNI-based geometric computation method.

From the exact geometry such as closed two-manifold polygonal meshes defined in a STL file, an LDNI model can be efficiently constructed by a rasterization technique that can be implemented using graphics hardware. Based on such well-structured sampling points, various solid modeling operations can be performed quickly and robustly. For solid modeling operations such as Minkowski sum or sweeping, multiple operations such as union can be repeatedly performed based on the computed LDNI model. Point-based rendering techniques [24] can also be used to directly display the LDNI models. For AM systems that require B-reps as the input, the processed LDNI model can be converted into a polygonal model based on a contouring method. For example, as shown in Fig. 5.7, in order to add an internal truss structure inside a shelled model, both polygonal models are first converted into related LDNI models. An LDNI-based Boolean operator is then used to compute a processed LDNI model based on them. Finally a polygonal model can be reconstructed from the processed LDNI model. Note that the newly constructed polygonal model is now valid without defects such as self-intersections. Such a polygonal model can be built by a microfabrication process such as TPP.

The presented framework as shown in Fig. 5.7 has certain similarity to the well-known digital communication and signal processing processes. That is, continuous geometric information is first converted into discrete digital information; various operations can then be performed based on such digital information; finally, the processed digital data are converted back to continuous geometric information. Similar to the digital signal processing (DSP) technology, an analog-to-digital converter (ADC), DSP methods, and a digital-to-analog converter (DAC) have been developed in this framework. They are discussed in more detail in the following sections: the conversion between polygonal meshes and LDNI models is presented in Section 4, and the LDNI-based geometric operations are discussed in Section 5, followed by some applications in Section 6.

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Importing and Exporting Data

Elliot J. Gindis , Robert C. Kaebisch , in Up and Running with AutoCAD 2019, 2018

16.7 Exporting and the Save As Feature

As previously with the MicroStation example, exporting data can be done by selecting File   →   Export… from the cascading menu. What you see is the dialog box in Fig. 16.12.

Figure 16.12. Export Data dialog box.

Your drawing can be exported to the following formats:

•

3D DWF or DWFx: This is to convert the drawing to web format for use on viewers.

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FBX: FBX stands for Filmbox and is a proprietary file format developed by Kaydara and now owned by Autodesk. It is used to provide interoperability between digital content creation applications.

•

Metafile: This is to convert the drawing to a Windows Meta File, a Microsoft graphics format for use with both vectors and bitmaps.

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ACIS : ACIS is the 3D solid modeling kernel of AutoCAD as well as a format to which a drawing can be exported. Few other software currently use the ACIS kernel (SolidEdge comes to mind), as Parasolid is the industry standard, so this is a rarely used export format.

•

Lithography: Stereo lithography files (∗.stl) are the industry standard for rapid prototyping and can be exported from most 3D CAD applications, including AutoCAD. Basically, it is a file that uses a mesh of triangles to form the shell of your solid object, where each triangle shares common sides and vertices.

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Encapsulated PS: The eps is a standard format for importing and exporting PostScript language files in all environments, allowing a drawing to be embedded as an illustration.

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DXX Extract: DXX stands for Drawing Interchange Attribute. This is a .dxf file with only block and attributes information and is not relevant to most users.

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Bitmap: This converts the drawing from vector form to bitmap form, with the resulting pixilation of the linework; it is not recommended.

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Block: This is another way to create the familiar block covered in Level 1.

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V8 DGN: MicroStation Version 8, as described previously.

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V7 DGN: MicroStation Version 7, as described previously.

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IGES: IGES stands for Initial Graphics Exchange Specification and is a vendor-neutral format for 3D CAD solid modeling files. The files can end in either .iges or .igs.

The Save As dialog box can be accessed anytime you select File   →   Save or File   →   Save As… from the drop-down cascading menus. You can also type in saveas and press Enter. In either case, you get the dialog box shown in Fig. 16.13.

Figure 16.13. Save Drawing As dialog box.

The extensions presented here are described in detail in Appendix C, File Extensions, but in summary, you can save your drawing as an older version of AutoCAD (an important step that is automated via a setting in Options, as covered in chapter: Options, Shortcuts, CUI, Design Center, and Express Tools) or as a .dxf file, as described earlier. Note that the .dxf files can also be created going back in time, all the way to Release 12 (that was a mostly DOS-based AutoCAD). The other extensions, .dws and .dwt, are used less often and are also mentioned in Appendix C.

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Biologically Inspired and Biomolecular Materials

R.A. Hortensius , B.A.C. Harley , in Comprehensive Biomaterials II, 2017

2.16.3.1 Microstructure: Experimental Measurement and Modeling

CG microstructure (pore size and morphology) is typically assessed using a stereological approach. Longitudinally and transversely oriented scaffold samples are embedded in glycolmethacrylate, sectioned, stained, and observed using optical microscopy. Using a linear intercept approach in any standard image analysis software package, a best-fit ellipse representative of the mean pore morphology is created. Dimensions of the ellipse (major and minor axis) are used to calculate an equivalent mean diameter and aspect ratio (O'Brien et al., 2005, 2004; Harley et al., 2007a). Micro-computed tomography (µCT) has also been utilized to analyze pore microstructure (O'Brien et al., 2005, 2004; Harley et al., 2007a), though often requires significant use of contrast agents to allow sufficient visualization of the non-mineralized collagen-GAG content. Most recently, µCT analysis has been used to create a computational model of the CG microstructure for use in analysis of shear stresses on cells in dynamic culture conditions (Jungreuthmayer et al., 2009b).

Cellular solids modeling approaches have proven to be a useful tool in the description and characterization of CG scaffold pore geometry and microstructural properties (pore shape, specific surface area) (Gibson and Ashby, 1997). The complex geometry of foams (and scaffolds) is difficult to model exactly; instead, dimensional arguments can be used to model salient microstructural features without incorporating exact cell geometries using the cellular solids modeling framework. CG scaffolds have been primarily modeled as a low density, open-cell foam using a tetrakaidecahedral (14-sided polyhedron) unit cell. Modeling microstructural features of CG scaffolds in this manner is possible because the mean pore structure of it and a variety of other low-density, open-cell foams has been observed to have a number of consistent features, notably approximately 14 faces per unit cell, 5.1 edges per face, and vertices that are nearly tetrahedral (Gibson and Ashby, 1997; Williams, 1968). The tetrakaidecahedron packs to fill space, nearly satisfies minimum surface energy requirements, and approximates the structural features of many experimentally characterized low density, open-cell foams (Fig. 7; Thompson, 1887; Gibson and Ashby, 1997). Application of modeling approaches using the tetrakaidecahedral unit cell with CG scaffolds has led to estimations of a number of key microstructural features of the CG scaffolds, many of which will be discussed throughout this chapter. The first microstructural element described using cellular solids approaches was the scaffold specific surface area (SA/V), the total surface area divided by the volume of the scaffold. Specific surface area describes the relative amount of surface available for cells to attach, and has been shown to be an integral factor affecting overall scaffold bioactivity (O'Brien et al., 2005; Murphy et al., 2010). For a low density, open-cell foam with an interconnected pore structure and edges of circular cross-section modeled using the discussed approaches, specific surface area is related to the mean pore size (d) and the relative density (ρ⁎/ρ s ) (O'Brien et al., 2005; Gibson and Ashby, 1997):

Fig. 7. Tetrakaidecahedral unit cell. d≡pore diameter; l≡strut length; t≡strut thickness.

 Reprinted with permission Harley, B. A. C.; Kim, H. D.; Zaman, M. H.; Yannas, I. V.; Lauffenburger, D. A.; Gibson, L. J. Microarchitecture of Three-Dimensional Scaffolds Influences Cell Migration Behavior Via Junction Interactions. Biophysical Journal 2008, 95 (8), 4013–4024.

(1) S A V = 10.15 d ( ρ ⁎ ρ s ) 1 / 2

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