Fuzzy engineering design semantics elaboration and application

Integration of CAD (Computer Aided Design), CAPP (Computer Aided Process Planning) and Process Modeling activities plays a vital role in enabling concurrent product and process design. Typically each of these functions is performed in its own dedicated software environment. The integration will require interfacing several disconnected processes and software components built in different languages, and platforms. This paper presents an integration methodology, validated using a case study, in which a steering housing was analyzed and its process planning and design tasks were integrated using several software tools. The first integration task was to generate a feature based CAD model (in Unigraphics) and map these design features to a set of manufacturing features. Feature based design was performed using the Horizontal Modeling™ approach developed at Delphi. Features developed using this approach were then mapped to manufacturing features using APPS, a software tool developed at Delphi Dynamics and Propulsion Innovation Center. This task involved interrogation of the geometric CAD model to generate geometric and tolerance information and represent them in a format suitable for feature-based process planning. The second task of integration is generation of feasible “production-intent” process plans. This task is performed using APPS and IMPlanner process planner, a knowledge based software tool developed at Ohio University. The third and final task of integration is automated generation of in-process CAD models. This task involved the integration of Delphi process design techniques to generate CAD models (in Unigraphics) to represent the component at each stage of the manufacturing process. Evaluation of these steps through the case study has identified the strengths and weaknesses of the proposed integration methodology, which is reported in this paper.

An Examination of the Effects of Contextual Computer-Aided Design Exercises on Student Modeling Performance

Investigations into the accuracy of computer-aided design (CAD) models for an injection mould have been carried out in this work. Taguchi design of experiments has been employed to find appropriate values for scanning parameters which result in shortest scanning time and lowest deviation between actual mould and its CAD model. CAD models obtained from point clouds captured using different scanning techniques, such as laser line scanning probe, active scanning probe and passive scanning probe attached to a bridge type CMM, have been analysed. For analysis, deviations between CAD model and actual part have been measured at a number of pre-defined points. Effect of surface roughness on the accuracy of reverse-engineered CAD model and scanning time while capturing surface coordinates using scanning touch probe has also been investigated. This investigation clearly exhibits that in case of polished surface higher accuracy for CAD model can be obtained in shorter scanning time.

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.

Error in maximum total point motion of a tibial baseplate is lower with a reverse-engineered model versus a CAD model using model-based radiostereometric analysis

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.

We propose a computer-aided design (CAD) model for synthesis of the unit cell of a metamaterial with the aim of improving the performance of a microstrip antenna designed for use in a traffic collision avoidance system (TCAS). Synthesis of electromagnetic (EM) devices generally requires great computational resources because of their inherent properties, e.g., being complex, nonconvex, and continuous. However, techniques based on soft computing are much more convenient for problems related to such EM devices. In this work, CAD models based on a genetic algorithm and differential evolution are first developed to synthesize a rectangular patch antenna with a microstrip feed line. Transmission line method (TLM) analysis is then used to develop the proposed CAD model for a microstrip patch antenna. In the proposed CAD models, the input parameters are the design/operating frequency, the thickness of the substrate used, and the permeability ($$\mu$$) of the substrate material, whereas the outputs are the dimensions of the structural design of the radiating patch and the microstrip feed line. The CST MWS EM tool is also used to design the antenna and validate the parameters of the synthesized antenna. A comparison between the two CAD models is presented. After the successful design of the CAD model for the microstrip antenna, the metamaterial synthesis technique is applied. Then, a metamaterial lens is designed with the help of the proposed CAD model and used to improve the performance of an antenna for use in TCAS applications. A comparison of the performance improvement is given in tabular form towards the end of the paper, including both the simulated and measured results.

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.

PurposeTo compare the surface of computer-aided design (CAD) models of the maxilla produced using ultra-low MDCT doses combined with filtered backprojection (FBP), adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR) reconstruction techniques with that produced from a standard dose/FBP protocol.MethodsA cadaveric completely edentulous maxilla was imaged using a standard dose protocol (CTDIvol: 29.4 mGy) and FBP, in addition to 5 low dose test protocols (LD1-5) (CTDIvol: 4.19, 2.64, 0.99, 0.53, and 0.29 mGy) reconstructed with FBP, ASIR 50, ASIR 100, and MBIR. A CAD model from each test protocol was superimposed onto the reference model using the ‘Best Fit Alignment’ function. Differences between the test and reference models were analyzed as maximum and mean deviations, and root-mean-square of the deviations, and color-coded models were obtained which demonstrated the location, magnitude and direction of the deviations.ResultsBased upon the magnitude, size, and distribution of areas of deviations, CAD models from the following protocols were comparable to the reference model: FBP/LD1; ASIR 50/LD1 and LD2; ASIR 100/LD1, LD2, and LD3; MBIR/LD1. The following protocols demonstrated deviations mostly between 1–2 mm or under 1 mm but over large areas, and so their effect on surgical guide accuracy is questionable: FBP/LD2; MBIR/LD2, LD3, LD4, and LD5. The following protocols demonstrated large deviations over large areas and therefore were not comparable to the reference model: FBP/LD3, LD4, and LD5; ASIR 50/LD3, LD4, and LD5; ASIR 100/LD4, and LD5.ConclusionsWhen MDCT is used for CAD models of the jaws, dose reductions of 86% may be possible with FBP, 91% with ASIR 50, and 97% with ASIR 100. Analysis of the stability and accuracy of CAD/CAM surgical guides as directly related to the jaws is needed to confirm the results.

The evolution of multimodal large language models (LLMs) capable of processing diverse input modalities (e.g., text and images) holds new prospects for their application in engineering design, such as the generation of 3D computer-aided design (CAD) models. However, little is known about the ability of multimodal LLMs to generate 3D design objects, and there is a lack of quantitative assessment. In this study, we develop an approach to enable two LLMs, GPT-4 and GPT-4V, to generate 3D CAD models (i.e., LLM4CAD) and perform experiments to evaluate their efficacy. To address the challenge of data scarcity for multimodal LLM studies, we created a data synthesis pipeline to generate CAD models, sketches, and image data of typical mechanical components (e.g., gears and springs) and collect their natural-language descriptions with dimensional information using Amazon Mechanical Turk. We positioned the CAD program (programming script for CAD design) as a bridge, facilitating the conversion of LLMs’ textual output into tangible CAD design objects. We focus on two critical capabilities: the generation of syntactically correct CAD programs (Cap1) and the accuracy of the parsed 3D shapes (Cap2) quantified by intersection over union. The results show that both GPT-4 and GPT-4V demonstrate potential in 3D CAD generation. Specifically, on average, GPT-4V outperforms when processing only text-based input, exceeding the results obtained using multimodal inputs, such as text with image, for Cap 1 and Cap 2. However, when examining category-specific results of mechanical components, while the same trend still holds for Cap 2, the prominence of multimodal inputs is increasingly evident for more complex geometries (e.g., springs and gears) in Cap 1. The potential of multimodal LLMs in enhancing 3D CAD generation is clear, but their application must be carefully calibrated to the complexity of the target CAD models to be generated.

The evolution of multimodal large language models (LLMs) capable of processing diverse input modalities (e.g., text and images) holds new prospects for their application in engineering design, such as the generation of 3D computer-aided design (CAD) models. However, little is known about the ability of multimodal LLMs to generate 3D design objects, and there is a lack of quantitative assessment. In this study, we develop an approach to enable LLMs to generate 3D CAD models (i.e., LLM4CAD) and perform experiments to evaluate their efficacy where GPT-4 and GPT-4V were employed as examples. To address the challenge of data scarcity for multimodal LLM studies, we created a data synthesis pipeline to generate CAD models, sketches, and image data of typical mechanical components (e.g., gears and springs) and collect their natural language descriptions with dimensional information using Amazon Mechanical Turk. We positioned the CAD program (programming script for CAD design) as a bridge, facilitating the conversion of LLMs’ textual output into tangible CAD design objects. We focus on two critical capabilities: the generation of syntactically correct CAD programs (Cap1) and the accuracy of the parsed 3D shapes (Cap2) quantified by intersection over union. The results show that both GPT-4 and GPT-4V demonstrate great potential in 3D CAD generation by just leveraging their zero-shot learning ability. Specifically, on average, GPT-4V outperforms when processing only text-based input, exceeding the results obtained using multimodal inputs, such as text with image, for Cap 1 and Cap 2. However, when examining category-specific results of mechanical components, the prominence of multimodal inputs is increasingly evident for more complex geometries (e.g., springs and gears) in both Cap 1 and Cap 2. The potential of multimodal LLMs to improve 3D CAD generation is clear, but their application must be carefully calibrated to the complexity of the target CAD models to be generated.

Model-based definitions (MBDs) aim to capture both geometric and non-geometric data in digital product definitions using 3D computer-aided design (CAD) models, as a form of product definition baseline, to disseminate product information across different stages of the lifecycle. MBDs can potentially eliminate error-prone information exchange associated with traditional paper-based drawings and improve the fidelity of component details, captured using 3D CAD models. A component’s behavior during its lifecycle stages influences its downstream performance, and if included within the MBD of a part, could be used to forecast performance upfront during the design and explore newer designs to enhance performance. However, current CAD capabilities limit associating behavioral information with the component’s shape definition. This paper presents a CAD-based tool to store and retrieve metadata using point objects within a CAD model, creating linkages to spatial locations within the component. The tool is illustrated for storage and retrieval of bulk residual stresses developed during the manufacturing of a turbine disk acquired from process modeling and characterization. Further, variations in residual stress distribution owing to process model uncertainties have been captured as separate instances of the disk’s CAD models to represent part-to-part variability as an analogy to track individual serialized components for digital twins. The propagation of varying residual stresses from these CAD models within the damage tolerance analysis performed at critical locations in the disk has been demonstrated. The combination of geometric and non-geometric data inside the MBD, via storage of spatial and feature varying information, presents opportunities to create digital twin(s) of actual component(s).

Databases of 3D CAD (computer aided design) models are often large and lacking in meaningful organisation. Effective tools for automatically searching for, categorising and comparing CAD models, therefore, have many potential applications in improving efficiency within design processes. This paper presents a novel asymmetric autoencoder model, consisting of a recursive encoder network and fully-connected decoder network, for the reproduction of CAD models through prediction of the parameters necessary to generate a 3D part design. Inputs to the autoencoder are STEP (standard for the exchange of product data) files, an ISO standard CAD model format, compatible with all major CAD software. A complete 3D model can be accurately reproduced using a STEP file, meaning that all geometric information can be used to contribute to the final encoded vector, with no loss of small detail.In a CAD model of overall size 10 × 10 × 10 units, for 90% of models, the class of an added feature is estimated with maximum error of 0.6 units, feature size with maximum error of 0.4 units and coordinate values representing position with maximum error of 0.3 units. These results demonstrate the successful encoding of complex geometric information, beyond merely the shape of the 3D object, with potential application in the design of search engine functionality.

In an offshore plant construction project, engineering, procurement, and construction (EPC) companies need to deliver plant design results to the owner in the form of a plant 3D computer-aided design (CAD) model as specified in the contract. However, owing to the limited data interface of plant 3D CAD systems, EPC companies frequently perform manual remodeling to fulfill the terms and conditions of such contracts. A comparison system that automatically measures the similarity between remodeled and original plant 3D CAD models to validate the remodeled plant 3D CAD model is, therefore, needed. In this paper, we propose a new method that automatically calculates the similarity between original and remodeled plant 3D piping CAD models for offshore plants. We also define similarity evaluation metrics that enable a quantitative value representing the overall similarity between original and remodeled plant 3D piping CAD models to be calculated. Subsequently, we design a similarity comparison system and implement a corresponding prototype based on the design. Similarity evaluation experiments are performed with test plant 3D piping CAD models provided by a large shipbuilding company in Korea to verify the feasibility of the proposed method.

<div class="section abstract"><div class="htmlview paragraph">Shell-and-tube heat exchangers, commonly referred to as radiators, are the most prevalent type of heat exchanger within the automotive industry. A pivotal goal for automotive designers is to increase their thermal effectiveness while mitigating pressure drop effects and minimizing the associated costs of design and operation. Their design is a lengthy and intricate process involving the manual creation and refinement of computer-aided design (CAD) models coupled with iterative multi-physics simulations. Consequently, there is a pressing demand for an integrated tool that can automate these discrete steps, yielding a significant enhancement in overall design efficiency. This work aims to introduce an innovative automation tool to streamline the design process, spanning from CAD model generation to identifying optimal design configurations. The proposed methodology is applied explicitly to the context of shell-and-tube heat exchangers, showcasing the tool's efficacy. The automation of CAD tasks is facilitated through custom Python code, leveraging the CadQuery library to parameterize CAD models and expedite the CAD process. Meshing and Computational Fluid Dynamics (CFD) simulations are seamlessly integrated within a Python environment, utilizing Ansys Fluent. Concurrently, a multi-objective Bayesian optimization is executed using a Gaussian process regression model facilitated by the GPflow library. By significantly reducing the time required for design tasks, this automation tool addresses a critical challenge that has long persisted in the industry. The tool automates the design processes and identifies an optimal design for the Shell and Tube Heat Exchanger. The tool explores the design space for new non-dominant designs. Three new designs are added to the space, with two dominant and one non-dominant design, further improving the pareto front. Similarly, this tool can be applied to multidisciplinary fields to identify the optimal design quickly with less human intervention in the design process.</div></div>