CAD, 3-D GIS and the Global Digital City

The issue of improving quality, costs and delays indicators in design and manufacturing is more relevant than ever in the industry. After lean manufacturing, well known in production process, the lean engineering approach is being implemented today in the field of design, taking the name of lean product development. The management of knowledge and know-how (existing, new or to be acquired) is the heart of lean engineering. In our suggested methodology this is implemented through a new generation of tools called Knowledge Configuration Management (KCM) and Knowledge Extraction Assistant (KEA). KCM tools are lean engineering components that provide analytical approach to knowledge management and knowledge-based engineering. These tools require a highly integrated approach that involves, for example, predefined geometrical parametric 3D models, such as CAD templates. But this approach cannot be deployed in all engineering sites. We propose to complete this KCM approach introducing a semantic network approach, coupling with Feature Identity Card (FIC). FIC contains a set of metadata and information existing in the Product Data Management (PDM), connected with information extracted from 3D CAD (Computer Aided Design) models. It allows contextualizing information and ensures semantic connections, in order to manipulate the right parameters with mathematical algorithms. Those algorithms will search candidate relationships between design parameters extracted from CAD models. Our suggested approach aims at extracting knowledge in cases where design never came out of Knowledge Based Engineering (KBE) applications. In those situations, it seems important to complete classical knowledge management approach, and to find out the implicit knowledge embedded in 3D CAD models. This is achieved through a global approach, focusing on the product’s 3D definitions. We suggest introducing the latter approach by a suite of digital KEA tools (interfaced with KCM tools). Extracting knowledge from projects information stored in the Product Data Management does this. More precisely, the methodology is based on a commercial 3D similarity search tools for CAD models and on mathematical algorithms that search relationships between extracted design parameters. The goal is to submit new rules to the process and design experts. Implementing this methodology, a deeper knowledge of the product and its associated process can be acquired. This ensures a more productive and efficient design process.

To accurately quantify polyethylene wear in retrieved arthroplasty components, the original geometry of the component must be estimated accurately using a reference geometry such as a computer-aided design (CAD) model or a never-implanted insert. However, differences may exist between the CAD model and manufactured inserts resulting from manufacturing tolerances. We quantified the deviations between CAD models and newly manufactured inserts and determined how these deviations compared with using a never-implanted insert as a reference geometry. We obtained five cruciate-retaining (CR) and five posterior-stabilizing (PS) tibial inserts and their CAD models. The inserts were scanned and reconstructed using microcomputed tomography (micro-CT). Differences in volume and surface geometry were measured among (1) the individual inserts; (2) between the inserts and a CAD model; and (3) between the inserts and a reference geometry constructed from multiple scanned inserts averaged together. The micro-CT volumes were, on average, 0.4% smaller (34-178 mm(3)) than the CAD model volumes. The mean deviation between the CAD model and insert surface geometry was 25.7 μm smaller for CR and 36.8 μm smaller for PS. The mean deviation between the inserts and an averaged reference geometry was 1.4 μm larger for CR and 0.4 μm smaller for PS. Deviations exist between manufactured tibial inserts and CAD models that could cause errors in wear measurements. Scanned inserts may better represent the preimplantation geometry of worn inserts than CAD models, depending on the manufacturing variability between lots. The magnitude of the error in estimation of the preimplantation geometry of a retrieved component could add or subtract the equivalent of 1 year of wear.

Applying geometric and dimensional tolerances (GD&T) to part features in computer-aided design (CAD) software is essential so that the part will function properly and to guide downstream manufacturing and inspection processes. However, it is not well characterized how CAD software implements capabilities for a designer to apply GD&T to a part. Of course, CAD software vendors do their own internal testing of those capabilities and users evaluate CAD software so that it satisfies their CAD modeling requirements. However, there has never been any rigorous public-domain testing of CAD software GD&T implementations. To improve that situation, the National Institute of Standards and Technology (NIST) has developed a system to test implementations of GD&T in CAD software. Representative part geometry with GD&T applied to features was modeled in four of the major CAD systems. Errors with semantic representation and graphical presentation of the GD&T were collected and analyzed. The testing methodology, test results, and data analysis demonstrate how well the CAD system GD&T implementations perform. The testing project results can be used as a basis for future testing, methods, and standards to evaluate defects in GD&T applied to part features.

Data acquired from site surveys provide an essential resource to various exploration and production activities, such as field development planning, well positioning, and subsea equipment positioning. For many years, site survey data has been delivered as a Computer-aided Design (CAD) product. As operators move into a Geographic Information Systems (GIS) environment, shortfalls from CAD-based workflows have become obvious. This paper presents a method of managing site survey data based on geo-information management principles and practices. The methodology presented in this paper defines a GIS data model for seabed survey data interpretation that satisfies GIS query, presentation, and data exchange purposes. This paper also defines a standard for GIS seabed survey data deliverables from contractors to operators, including methods and processes to move the model to/from CAD-based applications. The implementation of the data model has proven to be a relative success and allows for a better integration of subsurface applications internally. The results have helped define an efficient contractor-operator data exchange process by eliminating data ambiguity and enabling seamless GIS data transfer. Results have also produced a more intelligent seabed survey GIS dataset with rules and behaviors capability offered by an ArcGIS Geodatabase embedded into the data model. Use of a standard GIS data model and a standard list of GIS data deliverables required from contractors to operators will allow project teams to fully embrace the power of GIS data integration and allow nearly seamless and lossless movement of CAD/GIS representations of the model. Introduction In order to reach maximum potential benefit of data provided from seabed site surveys throughout the E & P lifecylcle (Figure 1), there is a need to ensure that data delivered from survey contractors to operators is done so in a structured, efficient, and consistent manner.

In this chapter, we address various issues related to data processing for rapid prototyping. In contrast to traditional material removal processes, rapid prototyping techniques build a part by incrementally adding materials layer by layer (Kruth 1991, Barequet and Kaplan 1998), so they are also called layer-based manufacturing or layered manufacturing. The processes are fully automatic and offer a number of competitive advantages over traditional manufacturing processes. Hence, they are particularly useful for rapid product development. The entire process for model prototyping is illustrated in Figure 5-1 and can be summarized as follows: CAD modeling: The first step in rapid prototyping is to prepare a computer-aided design (CAD) model of the object to be fabricated using layer based manufacturing processes. For most of the available RP technologies, a solid model with complete topological and geometric information is required. STL interfacing: STL is the de-facto standard for the rapid prototyping industry. It is a file format for approximately defining an object using triangular facets. When a CAD model is available, the entire part geometry is converted into STL format based on a tolerance for accuracy control. Part orientation: Before processing for prototyping, a RP engineer needs to figure out the specific orientation in which the prototype model will be produced. Part accuracy, the amount of supporting material required and ease of post-processing are important factors influencing part orientation determination. Support generation: Depending on the specific RP process to be used, one may need to further define support structures for supporting down-facing areas during part build-up. Support generation can be done on the basis of a STL model or the original CAD model. Model slicing and tool path generation: In contrast to material removal manufacturing technologies, rapid prototyping technologies refer to a class of layer-based material increase manufacturing processes. The digital model of the object and related support structures need to be sliced layer by layer. For each layer, a set of surface contours is obtained and a tool path is defined within the material area for model production in a layer-wise fashion. Model production on a RP machine: The produced tool path is sent to a RP machine for building up the prototype model, including support, layer by layer. Post-processing: Depending on the RP process involved, a post-processing step might be needed for post-curing in the case of stereolithography, for infiltration and furnace sintering in the case of SLS, and/or for removing the support structures and surface polishing in the cases of most other RP processes.

4D CAD model updating using image processing-based construction progress monitoring

Among machining conditions, tool paths exert a significant influence on the final machining outcome. In conventional practice, computer-aided design (CAD) models created from basic dimensions are typically used to generate tool paths within computer-aided manufacturing software. In recent years, 3D annotated models—CAD models enriched with product manufacturing information (PMI)—have become increasingly widespread. However, CAD models are often modified manually to achieve the desired machining results while accounting for machining errors. Consequently, operators consume time and effort in generating tool paths. Moreover, knowledge and experience in machining are required to modify CAD models according to PMI, such as unilateral tolerances. In this study, a method is proposed that leverages operator expertise to automatically modify CAD models and generate tool paths capable of satisfying specified size limits. The method employs a chain expression of dimensions, where the dimensional chain originates from the datum used as a reference for machining. Based on the dimensional chain, the objects requiring shifts are systematically identified. Subsequently, basic dimensions with unilateral tolerances are converted into target dimensions with bilateral tolerances to achieve the desired machining results. Case studies confirm that CAD models can be automatically modified when the target dimension is set to the median value of the size limits. Furthermore, machining experiments demonstrate that the proposed method effectively ensures compliance with the specified size limits.

Since the emergence of Computer Aided Design (CAD) software in 1957, educators have been committed to equipping design engineering students with proficiency in the most widely employed CAD tools in the industry. In the early days, training focused on step by step instructions and understanding CAD theory. Overtime, lecturers adopted more hands-on practice, project-based learning and collaborative learning techniques [1]. The abundance of online resources and tutorials has allowed lecturers, particularly in Higher Education (HE), to focus less on training and more on theory and use of various software and techniques. Over the last decade, the number, pace and emergence of unique sub-sector software development has grown exponentially, making it difficult for educators to adopt a fixed CAD curriculum. In the early days, the dominant players in the CAD software arena until the early 2010s were Solidworks, Rhino 3D, and Autodesk Maya, each equipped with its own proprietary rendering engine. However, over the past decade, there has been a proliferation of CAD software alternatives, complemented by independent rendering engines like Keyshot, notably embraced by software applications such as Blender, Gravity sketch, and Sketchup. In particular, Blender and Rhino's Grasshopper add-on have experienced a surge in user-generated custom add-ons, broadening the array of available features and functionalities. The rapid expansion of Artificial Intelligence (AI) tools over the last two years has introduced a new layer of complexity, as tools such as Midjourney, Stable Diffusion, and Control Net have gained prominence in the industry. The exponential growth of computational tools available has presented higher education institutions with a perplexing choice regarding which CAD software to teach and the most effective pedagogical methods [2]. This complexity is further compounded by the dynamic nature of software development and the diverse career paths of students, each demanding a distinct skill set for success. This intricate situation prompts the inquiry into an approach to future-proof and tailor CAD software education to the unique requirements of each student cohort . Perhaps the same tool that advances learning can be used for future-proofing? Whilst there are examples of successful uses of AI in education in other disciplines, the inquiry using baseline data from undergraduate and postgraduate students at a world leading institution raises additional questions: • Can we design an AI tool that develops bespoke CAD learning for students? • Can this learning go beyond simple command instructions and extract higher universal principles of CAD theory? • Specifically in HE, can AI be used to create individual learning plans based on student’s interests, industry needs and ongoing advances in CAD? References : [1] Brink, Kilbrink & Gericke, 2023. Teach to Use CAD or through Using CAD: An Interview Study with Technology Teachers. International journal of technology and design education 33.3: 957–979. [2] Xie, 2018. Learning and Teaching Engineering Design through Modeling and Simulation on a CAD Platform. Computer applications in engineering education 26.4: 824–840. [3] Ye, Peng, Chen & Cai, 2004. Today's students, tomorrow's engineers: an industrial perspective on CAD education, Computer-Aided Design, Volume 36, Issue 14, 2004, 1451-1460.

Most object-based approaches to Geographical Information Systems (GIS) have concentrated on the representation of geometric properties of objects in terms of fixed geometry. In our road traffic marking application domain we have a requirement to represent the static locations of the road markings but also enforce the associated regulations, which are typically geometric in nature. For example a give way line of a pedestrian crossing in the UK must be within 1100–3000 mm of the edge of the crossing pattern. In previous studies of the application of spatial rules (often called ‘business logic’) in GIS emphasis has been placed on the representation of topological constraints and data integrity checks. There is very little GIS literature that describes models for geometric rules, although there are some examples in the Computer Aided Design (CAD) literature. This paper introduces some of the ideas from so called variational CAD models to the GIS application domain, and extends these using a Geography Markup Language (GML) based representation. In our application we have an additional requirement; the geometric rules are often changed and vary from country to country so should be represented in a flexible manner. In this paper we describe an elegant solution to the representation of geometric rules, such as requiring lines to be offset from other objects. The method uses a feature-property model embraced in GML 3.1 and extends the possible relationships in feature collections to permit the application of parameterized geometric constraints to sub features. We show the parametric rule model we have developed and discuss the advantage of using simple parametric expressions in the rule base. We discuss the possibilities and limitations of our approach and relate our data model to GML 3.1.

This report documents a data collection where we recorded redundant range image data from multiple views of a simple scene, and recorded accurate survey measurements of the same scene. Collecting these data was a focus of the research project Automated Geometric Model Builder Using Range Image Sensor Data (96-0384), supported by Sandia's Laboratory-Directed Research and Development (LDRD) Program during fiscal years 1996, 1997, and 1998. The data described here are available from the authors on CDROM, or electronically over the Internet. Included in this data distribution are Computer-Aided Design (CAD) models we constructed from the survey measurements. The CAD models are compatible with the SolidWorks 98 Plus system, the modern Computer-Aided Design software system that is central to Sandia's DeskTop Engineering Project (DTEP). Integration of our measurements (as built) with the constructive geometry process of the CAD system (as designed) delivers on a vision of the research project. This report on our final data collection will also serve as a final report on the project.

A case is made that introducing CAD-based geometry prematurely in the aircraft design process actually hinders multidisciplinary design. Consequently, a design process which allows a designer to quickly build and analyze aerospace configurations without CAD or computational meshing specialists is proposed. This allows higher fidelity analyses to be applied at conceptual design and decreases the turnaround time for trade studies and optimizations. The basic premise of conceptual geometry modeling is presented and discussed. Key features and capabilities which are essential for a successful geometry modeler are identified. Desktop Aeronautics' conceptual geometry modeler, RAGE, is described, with emphasis on recent progress and advances in the tool. A preliminary graphical-user-interface is presented along with an application programming interface. Extensibility of the RAGE modeler is discussed along with the importance of this feature. The applicability of RAGE to simple trade studies is demonstrated in an example problem. The precision of the RAGE modeler is also demonstrated on a complex aircraft design. Future plans for the geometry modeler are also discussed. I. Background In the conceptual phase of aircraft design, there is often a wide range of geometric configurations that the designer wishes to consider, analyze, and assess. A designer’s intuition and creativity are crucial to a successful conceptual design, yet are easily inhibited by the modeling and analysis process itself, particularly if high-fidelity analysis is warranted. If the time between inception and assessment of a candidate design is relatively long, a designer may be forced to limit the number of concepts considered. New data revealed in a design iteration may suggest or require significant changes to the geometry configuration. Since the majority of considered design configurations will eventually be discarded, an ideal conceptual design process should involve a highly efficient and easy to use geometry modeler. Significant changes should be allowed without tedious re-work of the geometry model. Exporting the model for analysis should require little effort and have the capability to be automated. Therefore, any modeling application used in this process should be flexible and not restrict the designer’s choice of user interface, analysis method, and design optimization environment. Most current design processes in the aerospace industry rely heavily on computer-aided-design (CAD) applications. As a general design tool, CAD is a powerful geometry modeler capable of precisely representing practically any shape. Unfortunately, this generality is realized at the cost of requiring specialized training to efficiently and effectively use the software. Also, a CAD model is often not easily modified, especially if significant re-work of the geometry is necessary. This can lead to the premature freeze of an aircraft design due to employing excessively high-fidelity geometry modeling too early in the design process. Parametric CAD modeling could address this issue, but creating a well-designed parametric CAD model is difficult. Not only does it require an extremely skilled operator, but also the designer must have some insight into what parameterization may ultimately be desired. Modifying a parametric CAD model is often more difficult than modifying a conventional CAD model. Significant changes, such as adding or removing cross-sections, may invalidate the parameterization in some cases. In most organizations, CAD modeling (whether it be parametric or conventional) is often assigned to a dedicated CAD specialist. Since the conceptual designer is usually not a CAD specialist, the latter must communicate his design intent to the former, often using sketches, dimensions, and word of mouth. When the CAD specialist interprets the sketches, any part or detail not precisely defined usually requires judgment and artistic license. This interpretation may result in loss of design intent and indeterminate iteration between the CAD specialist and designer to obtain a satisfactory result. Because this process is iterative, CAD operators often must recreate similar geometry

Boundaries of land have been documented and recorded throughout history dating back over 2000 years ago. Land has been an important commodity throughout time, and a move toward building a coordinate-based land cadastre has been implemented in other countries across the world. It is used in parts of Europe, British Columbia, and the Middle East to manage land records. The County of Riverside needed a process for checking and reporting surveyed land divisions that were submitted in the form of computer aided design (CAD) files, and loading them into a geographic information system (GIS). The current CAD data submitted had errors with the geometry, and in most cases did not have a projected coordinate system. Most of the CAD files were not used for their intended purpose: which was to be shared with the Riverside County Assessor’s staff to aid them in creating and updating their Assessor Parcel Layer. Without the digital data the Assessor’s staff followed its business practice of re-entering the parcels one at a time. This created redundancy of work since the same data supplied by the Land Surveyor or Civil Engineer was identical. Some of the problems listed above uncovered the need to come up with a digital submission standard, and an automation tool to help prepare the data for loading into the Parcel Fabric. The goal of the project was to create a tool that will automatically check the surveyed land division for compliance prior to approval; this tool is named County Automated Terrestrial CAD Helper (CATCH). CATCH uses GIS technology to automatically identify mapping errors and report these errors back to the Land Surveyor or Civil Engineer who created the data. CATCH has been well received by the professional engineering community, from a series of stakeholder meetings held at county offices to introduce the project to private industry professionals and county staff. These meetings were critical in the success of CATCH, and helped bring up issues that the industry have had in submitting their digital files in the past. Also, CATCH could be well suited as the platform for tracking and maintaining other county assets in a GIS such as: grading plans, storm drains, gas lines, and other critical utilities that come in the form of digital CAD files. The problem that was solved was having an accurate foundation: and that being the boundary survey tied to a CAD file that utilized global navigation satellite system (GNSS) and global positioning system (GPS) technology.

This paper describes new algorithms for automatically constructing a computer aided design (CAD) model of a structured scene as imaged by a single camera on a mobile robot. The scene to be modeled is assumed to be composed mostly of linear edges with particular orientations in 3-D. This is the case for most indoor scenes as well as some outdoor urban scenes. The orientation data is used by a motion stereo algorithm to estimate the 3-D structure using a sequence of images. The algorithm assumes that the linear edges are the boundaries of opaque planar patches, such as the floor, the ceiling and the walls. The resulting 3-D description is a CAD model of the scene. Applications of this technique include CAD modeling for architecture and computer graphics, and robot navigation. This paper completes earlier publications and therefore concentrates on the latter parts of processing, including automatically tracking segments, and using the resulting models in different applications. >

This in vitro study evaluated the dimensional accuracy of three 3D printers and one milling machine with their respective polymeric materials using a simplified geometrical model. A simplified computer-aided design (CAD) model was created. The test samples were fabricated with three 3D printers: a dental desktop stereolithography (SLA) printer, an industrial SLA printer, and an industrial fused deposition modeling (FDM) printer, as well as a 5-axis milling machine. One polymer material was used per industrial printer and milling machine while two materials were used with the dental printer for a total of five study groups. Test specimens were then digitized using a laboratory scanner. The virtual outer caliper method was used to measure the linear dimensions of the digitized 3D printed and milled specimens in x-, y-, and z-axes, and compare them to the known values of the CAD model. Data were analyzed with Kruskal-Wallis one-way ANOVA on Ranks followed by the Tukey's test. Milled specimens were not significantly different from the CAD model in any dimension (p > 0.05). All 3D printed specimens were significantly different from the CAD model in all dimensions (p = 0.01), except the dental SLA 3D printer with one of the polymers tested (Bis-GMA) which was not significantly different in two (x and z) dimensions (p = 0.4 and p = 0.12). The milling technology tested provided greater dimensional accuracy than the selected 3D printing. Printer, printing technology, and material selection affected the accuracy of the printed model.

Digital manufacturing (DM) is actively adopted to the production lifecycles of a variety of critical industries, and this rapid growth has resulted in exponential increase of 3D computer-aided design (CAD) models. Unfortunately, counterfeiting of intellectual property becomes a prominent threat as many 3D designs are accessible online, combined with the proliferation of cheap consumer 3D printers that enable malicious actors to produce non-authentic parts. State-of-the-art techniques to secure manufacturing processes mostly rely on watermarking, which embeds hidden information inside CAD models to prove ownership and authenticity. Nevertheless, such techniques tamper with the model itself, while existing attacks allow removing such watermarks altogether. To address these shortcomings, we integrate signal processing and cryptographic techniques and describe a tailored solution for CAD model ownership and supply chain management. Our approach generates unique identifiers for 3D designs using frequency-domain transforms and employs non-fungible tokens (NFTs) that persist on public distributed ledgers. Our NFTs are implemented on the Ethereum blockchain using smart contracts and their functionality is twofold: (a) authenticate the owner of a CAD model, and (b) enable ownership transfer. To validate our technique, we deployed our smart contract on Ethereum's proof-of-work Ropsten network and demonstrated the applicability of our methodology.