Submitted by:

Manpreet Singh Kaler

08107026

4th year Mechanical

GRAPHIC COMPRESSION TECHNOLOGY IN CAD/CAM

2011

CAD/CAM PROJECT

What is Data Compression?

In computer science and information theory, data compression, source coding or bit-rate reduction is the process of encoding information using fewer bits than the original representation would use.

Why is Data Compression needed?

Compression is useful because it helps reduce the consumption of expensive resources, such as hard disk space or transmission bandwidth.

Presentation of graphic files in the compact compressed kind is needed for comfort of storage, provision of reception and information transfer on the ducting of connection. Thus redundancy in presentation of information allows this information to squeeze, i.e. to shorten the resources expended on its presentation.

When diminishing the sizes of memory, occupied by files, or at preparation of files for sending, the specialized programs are used in a compact form, accepted to talk that files are exposed to compression, or the compression. Technologies of compression of files use, as a rule, programs diminishing file sizes graphic arts due to the change of method of data organization, for example, replacing repetitive elements by information that is more effective for storage.

How does Compression works?

For Compression to work, three things are required which are: -

AN ENCODER: It is an embodiment of an encoding process. As shown in Figure 1, an encoder takes as input digital source image data and table specifications, and by means of a specified set of procedures generates as output compressed image data.

A DECODER: It is an embodiment of a decoding process. As shown in Figure 2, a decoder takes as input compressed image data and table specifications, and by means of a specified set of procedures generates as output digital reconstructed image data.

THE INTERCHANGE FORMAT: As shown in Figure 3, is a compressed image data representation which includes all table specifications used in the encoding process. The interchange format is for exchange between application environments.

ENCODING & DECODING PROCESSES

There are two classes of encoding and decoding processes, Lossy and Lossless processes

Lossy Process

"LOSSY" compression is a data encoding method that compresses data by discarding (losing) some of it. The procedure aims to minimize the amount of data that need to be held, handled, and/or transmitted by a computer.

Lossy compression can be thought of as an application of transform coding – in the case of multimedia data, perceptual coding: it transforms the raw data to a domain that more accurately reflects the information content. For example, rather than expressing a sound file as the amplitude levels over time, one may express it as the frequency spectrum over time, which corresponds more accurately to human audio perception.

There are two basic lossy compression schemes:

1. In lossy transform codecs, samples of picture or sound are taken, chopped into small segments, transformed into a new basis space, and quantized. The resulting quantized values are then entropy coded.

2. In lossy predictive codecs, previous and/or subsequent decoded data is used to predict the current sound sample or image frame. The error between the predicted data and the real data, together with any extra information needed to reproduce the prediction, is then quantized and coded.

Uncompressed PNG Image File

New image file size after Lossy Compression

Lossy methods are most often used for compressing sound, images or videos. This is because these types of data are intended for human interpretation where the mind can easily "fill in the blanks" or see past very minor errors or inconsistencies.

Lossless Process

Lossless data compression is a class of data compression algorithms that allows the exact original data to be reconstructed from the compressed data. The term lossless is in contrast to lossy data compression, which only allows an approximation of the original data to be reconstructed, in exchange for better compression rates.

Lossless data compression is used in many applications. For example, it is used in the ZIP/ Rar file format and in the UNIX tool gzip.

Lossless compression is used in cases where it is important that the original and the decompressed data be identical, or where deviations from the original data could be deleterious. Typical examples are executable programs, text documents and source code. Some image file formats, like PNG or GIF, use only lossless compression, while others like TIFF and MNG may use either lossless or lossy methods.

Lossless compression programs do two things in sequence:

1. The first step generates a statistical model for the input data, and 2. The second step uses this model to map input data to bit sequences in such a way that

"probable" (e.g. frequently encountered) data will produce shorter output than "improbable" data.

LOSSLESS COMPRESSION DONE WITH WINRAR

The primary encoding algorithms used to produce bit sequences are Huffman coding (also used by DEFLATE) and arithmetic coding. Arithmetic coding achieves compression rates close to the best possible for a particular statistical model, which is given by the information entropy, whereas Huffman compression is simpler and faster but produces poor results for models that deal with symbol probabilities close to 1.

There are two primary ways of constructing statistical models:

In a Static model, the data is analysed and a model is constructed, then this model is stored with the compressed data. This approach is simple and modular, but has the disadvantage that the model itself can be expensive to store, and also that it forces a single model to be used for all data being compressed, and so performs poorly on files containing heterogeneous data.

Adaptive models dynamically update the model as the data is compressed. Both the encoder and decoder begin with a trivial model, yielding poor compression of initial data, but as they learn more about the data, performance improves.

DIFFERENCE BETWEEN ABOVE COMPRESSION METHODS

The advantage of lossy methods over lossless methods is that in some cases a lossy method can produce a much smaller compressed file than any lossless method, while still meeting the requirements of the application.

Lossy methods are most often used for compressing sound, images or videos. This is because these types of data are intended for human interpretation where the mind can easily "fill in the blanks" or see past very minor errors or inconsistencies – ideally lossy compression is transparent (imperceptible), which can be verified via an ABX test.

Transparency

When a user acquires a loosely compressed file, (for example, to reduce download time) the retrieved file can be quite different from the original at the bit level while being indistinguishable to the human ear or eye for most practical purposes. Many compression methods focus on the idiosyncrasies of human physiology, taking into account, for instance, that the human eye can see only certain wavelengths of light. The psychoacoustic model describes how sound can be highly compressed without degrading perceived quality. Flaws caused by lossy compression that are noticeable to the human eye or ear are known as compression artefacts.

Compression ratio

The compression ratio (that is, the size of the compressed file compared to that of the uncompressed file) of lossy video codecs is nearly always far superior to that of the audio and still-image equivalents.

1. Video can be compressed immensely (e.g. 100:1) with little visible quality loss2. Audio can often be compressed at 10:1 with imperceptible loss of quality3. Still images are often loosely compressed at 10:1, as with audio, but the quality loss is more

noticeable, especially on closer inspection.

The compression rate is 95 to 96 % in lossy compression while in lossless compression it is about 50 to 60 % of the actual file.

Why Graphic Compression in CAD/CAM?

CAD/CAM softwares are being used today extensively for their designing purposes. Today, these softwares are used everywhere, from a small scale commercial companies to large high yielding production companies like Automobile industry, Medical sector , military purposes and even for the designing by NASA.

The designs thus made have high graphic contents that are stored in the files in the formats of CAE, DWG, and VRML etc.

There are many reasons responsible for the huge size of these files but the most important of them is the GRAPHIC CONTENT, like 3-D Models and thus compression technologies are more focused toward reducing the size of the graphic material in CAD/ CAM files without the loss of any information that the files hold.

Reasons for compression technology use in CAD/CAM: -

1) Storage purposes the files need to be small yet should have all the require data in them. Shortage of space also pushes companies to have their CAD/CAM files to be as small as possible. This can only be done by using special applications that can deal with reduction of the size without compromising the information.

2) With the increasing servers and clients most of the time servers are used as Host for CAD/CAM applications by the clients and thus it becomes very important that the data to be sent should be as small in size as possible for faster transmissions and thus this requires the files to be compressed.

3) To reduce the complexity in the data by compressing it in a different format that makes it easy to view, understand and easy to access.

4) Products becoming increasingly complex, increased products data have brought a heavy burden to storage and transmission, resulting in drastic efficiency reduction of cooperated product development under distributed network environment.

5) Saves bandwidth costs and improves the analyst's productivity.

Technology to Compress CAD/CAM Graphic Data

Today there are many software companies that are focused to find out technologies to reduce size of the CAD/CAM data files, especially graphic content, but out of all these there are only few that have made a breakthrough with their compression technologies.

Current methods for 3D compression may be grouped into three categories:

o Mesh-based methods that traverse a polygon mesh representing an object's surface -- Edgebreaker, Java3D compression, Topological Surgery, etc.

o Progressive and hierarchical methods that transmit a base mesh and a series of refinements -- Compressed Progressive Meshes, subdivision-based approaches, Compressed Normal Meshes

o Image-based approaches that encode not an object but a set of pictures -- QuicktimeVR and IPIX

EDGEBREAKER

The Edgebreaker compression visits the triangles in a spiralling (depth-first) TST order and generates the clears string of labels, one label per triangle, which indicate to the decompression how the mesh can be rebuilt by attaching new triangles to previously reconstructed ones (Fig. 2).

The Edgebreaker compression pseudo-code is shown in the insert above. The following explanations contain in parentheses the excerpts of the pseudo-code they reference. Edgebreaker works directly on the Corner Table and does not require any additional data structure, except for one bit per vertex and one bit per triangle to mark the ones that have already been processed. In particular, it does not require maintaining linked lists of border edges.

It traverses the mesh in depth-first order of a TST using iteration (REPEAT) and occasionally recursion (Compress) on corner indices. It marks all visited vertices (set(c.v.m)) and triangles (set(c.t.m)). The current triangle is identified by its tip corner (c). Note that the current triangle has been reached though the gate edge joining c.n.v with c.p.v. By testing the marks of the tip vertex of the current triangle and of neighbouring triangles, it selects the label and appends it to the sequence the clears string.

If the tip vertex (c.v) has not yet been visited (!c.v.m), its location is encoded (encode(c.v.g)) using the parallelogram prediction and geometry compression, as explained earlier. The label C is appended to the clears string (WRITE(clears, C)) and the iteration moves to the right neighbour (c:=c.r). Note that the vertices are encoded in the order in which they are encountered by C-triangles during this traversal. This order does not usually reflect the order in which the vertices were listed in the original mesh. Similarly, the triangles are reordered during transmission. A dictionary mapping the original order on the server to the new order on the client may be kept on the server to reconcile vertex or triangle selections between one location and the other in subsequent processing.

In this example of a typical compression situation, Edgebreaker starts with the darker triangle (left) and spirals out clockwise, filling the beginning of the clers string with CCCCRCCRCRC. It appends the tip of each C triangle to the vertex

list. A typical situation where Edgebreaker finishes compression or closes a hole is shown (center). It spirals counterclockwise, appending the label sequence CRSRLECRRRLE to the clers string and adding the vertices (a) and (b) to the vertex list. The triangles in the rabbit (right) have been shaded according to their Edgebreaker labels. Notice that half of the triangles are C (white) and about a third are R.

JAVA 3D COMRESSION TECHNOLOGY

JAVA 3D allows programmers to specify geometry using a binary compressed geometry format. This compression format is used with APIs other than just Java 3D, and can be used both as a runtime in-memory format for describing geometry, as well as a storage and network format. Eventually the full specification of the compressed geometry format described in this section will be part of its own stand-alone specification, but for completeness it is included as an appendix to the early specification of the Java 3D API.

Java 3D uses a compressed geometry format that allows 3D geometry to be represented in an order of magnitude less space than most traditional 3D representations, with very little loss in object quality. The compression is achieved through several layers of techniques.

For a binary format to be useful as an interchange format, it is essential that the format be thoroughly and unambiguously documented. This appendix attempts to completely specify all the details of the compressed geometry format. To ensure current and future compatibility, it is essential to only use the features explicitly specified in this document. For a binary format to be useful as an interchange format, it is essential that the format be thoroughly and unambiguously documented. This appendix attempts to completely specify all the details of the Compressed Geometry format. To insure current and future compatibility, it is essential to only use the features explicitly specified in this document. Any features, fields, usage, etc. not specified in the document should be considered illegal, and their usage would result in invalid Compressed Geometry data. "Invalid" means that using such a construct will be incompatible with current implementations or will break future implementations.

Compression

First, the geometry to be compressed is converted into a generalized mesh form, which allows a triangle to be, on average, specified by 0.80 vertices.

Next the data for each vertex component of the geometry is converted to the most efficient representation format for its type and then quantized to as few bits as possible.These quantized bits are differentiated between successive vertices, and the results are modified Huffman encoded into self-describing variable-bit-length data elements.

Finally, these variable-length elements are strung together using Java 3D's seven geometry instructions into a final compressed geometry block.

Decompression

For pure software implementations, upon receipt, compressed geometry blocks are decompressed into the local host's preferred geometry format by reversing the above process. This decompression can be performed in a lazy manner, avoiding full expansion into memory until the geometry is needed for rendering.

Convert to Generalized Mesh Format

Once a group of geometry has been identified, it is next converted into generalized mesh format. This is a complex step, and a number of topological analysis-based algorithms have been applied to it. Note that to reduce compression time, when space is a less important issue than time, a compressor might only stripify, not meshify. Alternatively, the triangles have to have come from somewhere, and that in many cases is a tessellator of higher order surfaces. Such a tessellator will implicitly know the mesh connectivity, and may be able to generate the triangle data directly in the generalized mesh format.

The next step is the quantization of the geometry positions, colours, and/or normal. All these quantization can be varied within the geometry, but for simplicity a single fixed quantization of each is assumed here.

Position - Normalize the position data.

The containing bounding box for the object is computed. This is the minimal box such that all geometry vertices are contained within it. The vertices are then all normalized to be contained within this bounding box by first subtracting the XYZ location of the bounding box centre from the vertex XYZ and then dividing all the XYZ vertex values by the half length of the longest side of the bounding box. Thus all normalized positions will be within the ±1 unit cube. A constant matrix transform corresponding to an offset to the centre of the bounding box, and an inverse scale by the half length of the longest side of the bounding box are created as a prologue for the geometry data. Note that in practice a little more care must be taken; Compressed Geometry can always represent -1, but the greatest positive value is actually, when positions are quantized to n bits. Thus when computing the scale factor (and centre) that will normalize the geometry, the actual representation range needs to be taken into account.

HIERARCHICAL STRUCTURE OF CAD MODELS

A complete product model contains geometric data, feature information, assembly information, product attributes, manufacture information, etc. Product information is usually abstracted as attributes layer, geometry layer, topology layer and assembly layer. Figure 1 shows the structure of traditional CAD model.

It’s difficult to analyze the information redundancy and transmit model incrementally, due to the tight coupling of layer data in traditional model structure. Therefore, the model data are reorganized and a B-rep based hierarchical representation structure is set up.

As Figure 2 shows, the bidirectional relationships between layer data are decoupled in hierarchical structure, and translated to a structure of single direction list. The process of reorganization is as follows. First, Hierarchical Data Buffer (HDB) is introduced to store the model data of every layer.

Five HDBs are provided correspond to the number of layers. Second, all nodes of the layer data are tagged. Finally, the directed graph of nodes is traversed and every node is wrote to the HDB according to it’s layer. After finishing the traverse of graph, the model data are decomposed and every layer data are stored in the HDBs.

COMPRESSION OF CAD MODELS

o Feature layer compressiono Geometry layer compression

System implementation

The above algorithms are implemented in the prototype system, which provides such functions as remote collaborative browse, check, comment, assembly, edit of animation and explosion, etc.

Figure 7 shows the modules and principles of the prototype system. The product models designed in different CAD systems are imported into the prototype system through the interface developed with API provided by CAD systems. The compressed accurate models are obtained by analyzing the redundancies, coding the feature data and free-form curves and surfaces and entropy coding.

Then the compressed accurate models can be transmitted incrementally through network and be used in such fields as collaborative development, mass customization and model retrieval.

THESE METHODS USING TECHNOLOGIES

These methods are used by companies to create technologies that can compress the graphic contents in the CAD/CAM files and those technologies are: -

o VCollab-CAX format

o Lattice 3D Technology-XVL

VCOLLAB

With VCollab, individuals and groups throughout product development can share and interact with complex computer-aided-design (CAD), computer-aided-manufacturing (CAM), and computer-aided-engineering (CAE) files without having to access the native applications. VCollab 2006 extends the opportunity for product visualization far across the spectrum of engineering disciplines, from the creation of 3D digital product geometry to finite element analysis (FEA), computational fluid dynamics (CFD), and manufacturing assembly simulation.

VCollab 2006 is a significant step forward for industry because it is a multidisciplinary solution. Manufacturers can use VCollab to share valuable data and results easily from all of their 3D CAD, CAM, and CAE activities.

The software combines powerful graphics tools with a compression technology that reduces file size while preserving engineering content. VCollab files can be embedded into Microsoft® Office applications or opened directly within Internet Explorer. Team members can then view embedded CAD, CAM, and CAE product data with laptop or desktop computers, or on graphics clusters in immersive environments, without needing the native engineering software.

The VCollab 2006 software suite contains:

o VCollab Pro, scalable, high-performance visualization software that converts large 3D CAD, CAM, and CAE data sets to highly compressed .vcb format. A file saved in .vcb format loads up to 100 times faster than it does in its native authoring format, so communicating 3D product data is easy and convenient. VCollab Pro contains an array of tools for visualizing 3D digital models and simulations.

o VCollab Presenter, feature-rich, easy-to-use CAx viewing software that enables nontechnical personnel to view and manipulate 3D CAD, CAM, and CAE information, using Microsoft® Internet Explorer, Word, Excel, and PowerPoint on laptops and desktops.

o VMove CAE, a direct native reader of CAE output results that converts native CAE results files into highly compressed VCollab .vcb files based on user inputs. CAE software supported includes MSC Nastran, LS-DYNA, ANSYS, and ABAQUS.

CAE results files are large. They are getting bigger and bigger every day. Managing these large CAE results files is a pain to the product development companies, IT departments as well as end users. VCollab provides solutions to address these large file issues.

CAD/CAM/CAE DATA REDUCTION

VCollab provides an ultra-compact and a common format called CAX as a solution to store the FEA and CFD models and results from many FEA/CFD software's, in an ultra-compact format. This format makes the FEA and CFD model and results files portable and provide a consistent platform/strategy for global FEA and CFD data deployment.

VCollab provides the following tools to reduce and store the large CAE models and results into CAX format.

1) VMoveCAE as a tool to create the ultra-compact CAX files from many commercial software.

VMoveCAE is a direct reader of the native CAE results files, from many CAE software. VMoveCAE converts the native CAE results files into highly reduced CAX files, for easier sharing and Viewing of Simulation Results.

How does VMove works?

How effective is CAX fie reduction?

VMoveCAE currently supports the following CAE Software:

o MSC NASTRANo MSC MARCo NE/Nastrano NX/Nastrano IDEASo ANSYSo ABAQUSo LS-DYNAo FLUENTo STAR-CCM+o STLo CGNS Format

Key Features

o Universal CAE Data Reduction Engine.o Reduces the large CAE results file sizes up to 99% for easier transfers and visualizationo Converts native CAE Model and results files into an ultra-compact VCollab format that can

be easily shared or viewed. VCollab format allows for easy manipulation and analysis of results.

o Universal Meta Data Extractor - Extracts meta data from all the software as listed above and generates XML file for easier integration with any PLM /SLM /ERP system.

o Generates STL file from the above CAE software. Useful for 3D printing / rapid prototyping of the CAE models.

VMoveCAE can run in 2 modes

o Interactiveo Batch mode

Available as a cross platform solution

o Windows 32 Bito Windows 64 Bito Linux 64 BITo AIX 64 Bit

Works within many Simulation Work Flow Automation, MDO and Simulation Data Management Systems to reduce CAE file sizes and produce compact CAE Visual File

Benefits

1. Large file handling capability: No need to move around and transfer large CAE results files to local systems

2. Faster post processing, leading to improved productivity for CAE analysts3. Helps IT in HPC / Desktop Integration4. CAE Data Deployment is easier with smaller files5. CAE Simulation Knowledge Capture: The CAE knowledge can now be stored and archived as

ultra-compact CAX files6. Integrates easily with PLM/SLM and other CAE work flow automation systems

2) VMoveAPI software so as to create and export the CAE models and results into CAX format.

VMoveAPI provides an easy way for CAD/FEA/CFD Vendors or customers with in house developed CAE software, to export CAE models and CAE results into highly compact, lightweight CAX files. CAX files are extremely compact in size which makes them easier to move, view and share. Free as well as premium Viewers are available to view and process the CAX files. The VMoveAPI provides vendors or customers to easily write out CAE models and results from their solvers or post processors, into the light weight CAX format, for easier sharing and reuse of CAE data and results for decision making.

CAE vendors are often under pressure to provide a free viewer to their customers. By integrating the VMoveAPI, they gain a free viewer from VCollab, which they can provide to their customers. CAX files also increase the utilization of the vendor specific CAE models and results. VCollab also provides premium viewers for advanced users. This can help adding revenues to the CAE vendors.

Customers with in house FEA/CFD solvers can use this API to write CAX files and use the state of the art VCollab Viewers to visualize their models, simulations and results.

Completely cross-platform C++ library developed using ANSI C++.

Supported Platforms

32-bit and 64-bit Windows XP and Vista operating systems running on INTEL and AMD processors (Built using Microsoft Visual C++ 8.0)

32-bit and 64-bit RHEL distributions running on INTEL or AMD processors (built using gnu c++ compilers)

AIX 5.3 and 6.1 running on RISC processors (built using gcc c++ compilers). API can be provided for additional platforms and operating systems on request.

Who can benefit from the VMoveAPI?

1. CAD vendors.2. CAE Solver vendors3. CAE Post Processor vendors4. CAE Pre Processor vendors5. End customers with in house CAE software

Features of CAX

CAX format is a highly reduced 3-D binary and light-weight data format to store CAD geometries, FEA/CFD results and the meshes.

CAX files not only contain the design and simulation data such as mesh, part hierarchies, simulation results etc., but also contain the post-processing information such as deformation plots, color plots, palettes, the visual information such as cameras, viewpoints, animations, and the review information such as annotations, comments and change history.

CAX files also store the Meta data information.

VCollab provides free as well as premium VCollab viewers to visualize the CAX files. ex. non CAE experts can visualize and easily understand the FEA/CFD results using VCollab viewers.

CAX files make CAE data portable.

CAX files can be embedded into PowerPoint slides for 3D presentations or put into Word for archiving 3D files or embedded into WEB pages for easier sharing.

"CAX files along with the "VCollab Light Weight Viewer" provide a complete light weight 3D Visualization and Collaboration solution to the CAD/CAM/CAE/ other 3D authoring software companies and also to the MDO, PLM and SLM software vendors.

Advantages

1) Avoid moving large FEA/CFD results files over the network, for visualization of results. Saves bandwidth costs and improves the analyst's productivity.

2) Simulation knowledge is never lost. 3D CAE results/knowledge can be stored / archived in ultra-compact CAX files.

3) Extend visibility of CAE data to designers, chief engineers, test and service engineers and other stake holders.

4) Maximize the utilization of CAE investments and CAE Data.

LATTICE 3D TECHNOLOGY-XVL

XVL Delivers the Industry's best 3D Compression

Unmatched Accuracy and Precision

Compression with XVL doesn't mean sacrificing accuracy. XVL delivers higher compression than ever before, with XVL v10 files being compressed to an average 0.5% of the original CAD assembly file size, with up to the same measurable accuracy of the original CAD system.

XVL Provides an Unmatched Retention of Critical Data

No need to recreate topology, assembly structures, parts lists, annotation: It's all delivered into your XVL file:

3D geometry and topology: shell, face, edge, vertex Attribute data, colours etc. Assembly structures from 3D CAD data Parts lists and other metadata data in 3D CAD data or from ERP systems

Where is XVL used?

XVL is used by thousands of manufacturers worldwide as a means of communicating design, production and manufacturing information which is leveraged by 3D data. With its unequalled compression, XVL allows previously large 3D CAD assemblies to be easily shared, used and manipulated. The XVL Solutions enable the reuse of the 3D data for needs extended across the enterprise including simulation, animation, design review, process design, assembly and process planning, 3D parts lists and creation illustrations direct from 3D.

XVL also supports 3D data in Microsoft Excel, Microsoft Office applications and Adobe Acrobat's PDF.

How does XVL work?

XVL is based on free-form surface representation, also known as a Gregory patch. A Gregory patch is excellent at representing continuity between surfaces. It easily ensures tangent-plane continuity (G~1 continuity) between surfaces. If you have experience in operating spline-series modelers, which support NURBS (Non-Uniform Rational B-Spline) surface, you may understand how difficult it is to modify model shape while keeping surface continuity.

Advancements in their new technology update

P-XVL (the original XVL format which went to v3) delivered relatively accurate free-form NURBs surface and curve definitions with a very small footprint, suitable for fast network delivery of 3D CAD data for viewing. It could also save out to IGES and STEP formats.

V-XVL (v5, 7 and 9) delivered very high accuracy with tessellated curves and surfaces, with a larger footprint in the data size, but appropriate for highly accurate Design Review and simulation. It was unable to export to IGES and STEP.

XVL v10 (codenamed U-XVL) delivers the best of both prior formats by delivering very high accuracy freeform NURBs, tessellated curves and surface definitions at a much higher compression rate than ever before. This makes it suitable for very fast, highly accurate, hardware-intensive processes such as simulation, and animation of 3D processes but delivered with the small footprint needed for faster testing, sharing and usage of the 3d data. XVL v10 is also able to save out to surface formats such as IGES and STEP.

Compression, performance and memory consumption comparisons between XVL v9 and XVL v10

The architecture of XVL v10

Standalone: XVL v10, like its predecessors, allows for single or multiple XVL files to be saved with appropriate data. 3D geometric data, assembly structures, parts lists and other metadata can be imported directly from the 3D and PLM file, or via .CSV, .XML and other external sources if preferred.

Data can be imported manually or automatically into the stand-alone XVL applications from various sources

As an Integrated Platform: XVL integrates directly with existing IT infrastructure including PLM, PDM, ERP, MES and more. This allows the free flow of data to-and-from XVL, while the XVL applications automatically deliver end-user documentation that is immediately up-to-date and relevant to each recipient. Using the XVL System toolkit, all XVL applications can be integrated with these systems to enable rapid reuse of 3D data with the correct, relevant manufacturing documentation, process and production information.

XVL data and applications can be directly integrated using the XVL System Toolkit into an existing workflow and IT infrastructure.

The applications of XVL in process/facility planning are:

1) Check assembly feasibility for each component in the assembly sequences

2) Confirm the installation of large parts such as the engine

3) Check the work space for tools and technician posture

4) Confirm the completeness of the assembly line

5) Capture the check results in reports.

XVL is also used in the quality planning process. Design data contains quality information so that the quality of products should be ensured when production work is carried out. However, because various problems occur in the real world, we check and confirm the issues from the quality control perspective at an early stage.

This includes:

1) Reliability check - such as abnormal noise and source of potential fire problems

2) Confirm the manufacturability

3) Confirm the visibility of vehicle frame number

4) Confirm the dynamic movement of tools/operators to ensure quality

5) Confirmation that the process will capture the design intent at each step of the process

REFERENCESThis project was accomplished with the help of a number of sources and the useful material they provided me with. Some of the references are listed below: -

1) www.google.com

2) www.wikipedia.org

3) www.3dcompression.com

4) www.lattice3d.com

5) download.oracle.com/docs

6) www.vcollab.com

7) citeseerx.ist.psu.edu

8) msc.usc.edu

9) www.cadforum.cz

10) 3D mesh compression by Jarek Rossignac

11) 3D Model Compression For Collaborative Design by Jun Liu