Computer-aided Design Object Research Articles

Spatial Visual Imagery (SVI)-Based Electroencephalograph Discrimination for Natural CAD Manipulation.

With the increasing demand for natural interactions, people have realized that an intuitive Computer-Aided Design (CAD) interaction mode can reduce the complexity of CAD operation and improve the design experience. Although interaction modes like gaze and gesture are compatible with some complex CAD manipulations, they still require people to express their design intentions physically. The brain contains design intentions implicitly and controls the corresponding body parts that execute the task. Therefore, building an end-to-end channel between the brain and computer as an auxiliary mode for CAD manipulation will allow people to send design intentions mentally and make their interaction more intuitive. This work focuses on the 1-D translation scene and studies a spatial visual imagery (SVI) paradigm to provide theoretical support for building an electroencephalograph (EEG)-based brain-computer interface (BCI) for CAD manipulation. Based on the analysis of three spatial EEG features related to SVI (e.g., common spatial patterns, cross-correlation, and coherence), a multi-feature fusion-based discrimination model was built for SVI. The average accuracy of the intent discrimination of 10 subjects was 86%, and the highest accuracy was 93%. The method proposed was verified to be feasible for discriminating the intentions of CAD object translation with good classification performance. This work further proves the potential of BCI in natural CAD manipulation.

LiteF2DNet: a lightweight learning framework for 3D reconstruction using fringe projection profilometry.

Fringe projection profilometry (FPP) is the most commonly used structured light approach for 3D object profiling. Traditional FPP algorithms have multistage procedures that can lead to error propagation. Deep-learning-based end-to-end models currently have been developed to mitigate this error propagation and provide faithful reconstruction. In this paper, we propose LiteF2DNet, a lightweight deep-learning framework to estimate the depth profile of objects, given reference and deformed fringes. The proposed framework has dense connections in the feature extraction module to aid better information flow. The parameters in the framework are 40% less than those in the base model, which also means less inference time and limited memory requirements, making it suitable for real-time 3D reconstruction. To circumvent the tedious process of collecting real samples, synthetic sample training was adopted in this work using Gaussian mixture models and computer-aided design objects. The qualitative and quantitative results presented in this work demonstrate that the proposed network performs well compared to other standard methods in the literature. Various analysis plots also illustrate the model's superior performance at high dynamic ranges, even with low-frequency fringes and high noise. Moreover, the reconstruction results on real samples show that the proposed model can predict 3D profiles of real objects with synthetic sample training.

G-code generation in a NURBS workflow for precise additive manufacturing

Purpose Non-uniform rational B-splines (NURBSs) are the de facto standard for representing objects in computer-aided design (CAD). The purpose of this paper is to discuss how to stick to this standard in all phases of the additive manufacturing (AM) workflow, from the CAD object to the final G-code, bypassing unnecessary polygonal approximations. Design/methodology/approach The authors use a commercial CAD system (Rhino3D along with its programming environment Grasshopper) for direct slicing of the model, offset generation and trimming. Circular arcs are represented as quadratic NURBSs and free-form geometry as quadratic or cubic polynomial B-splines. Therefore, circular arcs are directly expressible as G2/G3 G-code commands, whereas free-form paths are rewritten as a succession of cubic Bézier curves, thereby admitting exact translation into G5 commands, available in firmware for AM controllers, such as Marlin. Findings Experimental results of this paper confirm a considerable improvement in quality over the standard AM workflow, consisting of an initial polygonization of the object (e.g. via standard tessellation language), slicing this polygonal approximation, offsetting the polygonal sections and, finally, generating G-code made up of polyline trajectories (G1 commands). Originality/value A streamlined AM workflow is obtained, with a seamless transfer from the initial CAD description to the final G-code. By adhering to the NURBS standard at all steps, the authors avoid multiple representations and associated errors resulting from approximations.

Building EEG-based CAD object selection intention discrimination model using convolutional neural network (CNN)

Currently, building natural interaction systems based on physiological signals has become an crucial requirement for the development of Computer Aided Design (CAD). As the first step of model operation in CAD, object selection is essential and the efficiency of selecting has a great impact on the experience of users. In the research community, gaze-based interaction for object selection has been well-established. However, this interactive mode is still imperfect due to Midas touch problem. In this work, a selection intention discrimination (SID) model is implemented to decode electroencephalogram (EEG) signals generated during object selection process. Common Spatial Pattern (CSP) is applied to extract spatial features from EEG in four frequency bands. Then these features are learned by a Convolutional Neural Network (CNN) equipped with an adaptive weights training module to realize the SID. To verify the decoding feasibility of this model, a cognitive experiment related to object selection is conducted. The empirical result shows that the performance of this model is good. It turns out that EEG-based object selection is feasible, which can be a intuitive and natural interaction mode for CAD.

ShapeGuide: Shape-Based 3D Interaction for Parameter Modification of Native CAD Data.

While Virtual Reality (VR) technologies are commonly used in industrial companies, loading and modifying CAD parts of commercial CAD systems in virtual environments are still challenging. A few VR applications for Computer Aided Design (CAD) enable users to modify native CAD data in an immersive environment. Even if such VR-CAD applications use 3D interaction space, interaction with parameter values of CAD parts could be enhanced. This paper presents ShapeGuide, a technique allowing users to modify native CAD parts using a shape-based 3D interaction technique. With ShapeGuide, users can achieve modification of parameter values by directly pushing or pulling the surface of a CAD object. In addition, force feedback can be integrated into the technique to enhance the precision of the users' hand motions in the 3D space. In a controlled experiment, we compared ShapeGuide to a standard one-dimensional scroll technique to measure its added value for parametric CAD data modification on a simple industrial product example with an adjusted modification capability. We also evaluated the effect of force feedback assistance on both techniques. Results of this experiment demonstrate that ShapeGuide is significantly faster and more efficient than the scroll technique. Furthermore, they show that the force feedback assistance enhances the precision of both techniques, especially of ShapeGuide.

Recognition of Distorted CAD Objects using Neural Networks

uses of features have been considered to be the technology which bridges the gap between Computer aided design (CAD) and Computer aided manufacturing (CAM) in the CIMS (Computer Integrated manufacturing systems).Active research in the last two decades has resulted in a number of recognition techniques like rule based, graph matching, volume decomposition, hint based, neural network, etc. This paper presents the development of distorted CAD objects recognition system. A well-known multi- layer Perceptron (MLP) neural network with backpropagation learning algorithm is chosen for its fast processing time and its good performance for feature recognition problems. Learning systems that learn from previous experiences and/or provided examples of appropriate behaviors, allow the people to specify what the systems should do for each case, not how systems should act for each step. The recognition system is programmed in C++.

Automated Recognition of 3D CAD Objects in Site Laser Scans for Project 3D Status Visualization and Performance Control

This paper presents a new approach that allows automated recognition of three-dimensional (3D) computer-aided design (CAD) objects from 3D site laser scans. This approach provides a robust and efficient means to recognize objects in a scene by integrating planning technologies, such as multidimensional CAD modeling, and field technologies, such as 3D laser scanning. Using such an approach, it would be possible to visualize the 3D status of a project and automate some tasks related to project control. These tasks include 3D progress tracking, productivity tracking, and construction dimensional quality assessment and quality control. This paper provides an overview of the developed approach and demonstrates its performance in object recognition and project 3D status visualization, with data collected from a construction job site.

VR–CAD integration: Multimodal immersive interaction and advanced haptic paradigms for implicit edition of CAD models

This paper presents an approach for the integration of Virtual Reality (VR) and Computer-Aided Design (CAD). Our general goal is to develop a VR–CAD framework making possible intuitive and direct 3D edition on CAD objects within Virtual Environments (VE). Such a framework can be applied to collaborative part design activities and to immersive project reviews. The cornerstone of our approach is a model that manages implicit editing of CAD objects. This model uses a naming technique of B-Rep components and a set of logical rules to provide straight access to the operators of Construction History Graphs (CHG). Another set of logical rules and the replay capacities of CHG make it possible to modify in real-time the parameters of these operators according to the user’s 3D interactions. A demonstrator of our model has been developed on the OpenCASCADE geometric kernel, but we explain how it can be applied to more standard CAD systems such as CATIA. We combined our VR–CAD framework with multimodal immersive interaction (using 6 DoF tracking, speech and gesture recognition systems) to gain direct and intuitive deformation of the objects’ shapes within a VE, thus avoiding explicit interactions with the CHG within a classical WIMP interface. In addition, we present several haptic paradigms specially conceptualized and evaluated to provide an accurate perception of B-Rep components and to help the user during his/her 3D interactions. Finally, we conclude on some issues for future researches in the field of VR–CAD integration.

Automated retrieval of 3D CAD model objects in construction range images

Automated and robust retrieval of three-dimensional (3D) Computer-Aided Design (CAD) objects from laser scanned data would have many potentially valuable applications in construction engineering and management. For example, it would enable automated progress assessment for effortless productivity tracking, automated 3D image database searching for forensic and legal analysis, and real-time local modeling for automated equipment control and safety. After reviewing and analyzing previous research in the field of automated object recognition, this paper presents a new approach for robust automated recognition/retrieval of 3D CAD objects in range point clouds in the Architectural/Engineering/Construction & Facility Management (AEC-FM) context. This approach is validated in laboratory experiments. A first experiment demonstrates that this new approach can efficiently and robustly automatically retrieve 3D CAD model objects in construction laser scanned data. A second experiment demonstrates how this approach can be used for efficiently assessing construction progress. The results presented here are preliminary but conclusive for proof of concept. More extensive field experiments in this and other application areas will follow to characterize performance trade-offs in practice.

Georeferenced Registration of Construction Graphics in Mobile Outdoor Augmented Reality

This paper describes research that investigated the application of the global positioning system and 3 degree-of-freedom (3-DOF) angular tracking to address the registration problem during interactive visualization of construction graphics in outdoor augmented reality (AR) environments. The global position and the three-dimensional (3D) orientation of a user's viewpoint are tracked, and this information is reconciled with the known global position and orientation of superimposed computer-aided design (CAD) objects. Based on this computation, the relative translation and axial rotations between the user's viewpoint and the CAD objects are continually calculated. The relative geometric transformations are then applied to the CAD objects inside a virtual viewing frustum that is coincided with the real world space that is in the user's view. The result is an augmented outdoor environment where superimposed graphical objects stay fixed to their real world locations as the user navigates. The algorithms are implemented in a software tool called UM-AR-GPS-ROVER that is capable of interactively placing static and dynamic 3D models at any location in outdoor augmented space. The concept and prototype are demonstrated with an example in which scheduled construction activities for the erection of a structural steel frame are graphically simulated in outdoor AR.