As collaborative design and configuration design are of increasing importance in product development, it becomes essential to exchange the feature and parametric CAD models among participants. A history-based parametric method has been proposed. But the feature and parametric exchange methods have problems such as the identification of the selected geometries, mapping between features which have different attributes. Furthermore, because the hislory-based parametric method uses the procedural model as a neutral format, or the standard macro file, the history-based parametric translators need a geometric modeling kernel to generate an internal explicit geometric model. To ease the problem, we implemented an integration platform, the TransCAD. The TransCAD separates translators from the neutral niacin files. The translators for commercial CAD systems can communicate with only the TransCAD. To support the communication with the TransCAD, we exposed the function; of the TransCAD by using the Automation APis. which is developed by Microsoft. The Automation APIs of the TransCAD consist of the part modeling functions, the data extraction junctions, and the utility functions. Each translator uses the functions to translate the model from the sending CAD system, into the neutral formal, or from the neutral firmat into the model of the receiving CAD system. This paper introduces what the TransCAD is and how it works for the exchange of the feature and parametric models.

Integration of history-based parametric translators using the automation API

Abstract In the manufacturing industry, all things related to a product manufactured are generated and managed with a three-dimensional (3D) computer-aided design (CAD) system. CAD models created in a 3D CAD system are represented as geometric and topological information for exchange between different CAD systems. Although 3D CAD models are easy to use for product design, it is not suitable for direct use in manufacturing since information on machining features is absent. This study proposes a novel deep learning model to recognize machining features from a 3D CAD model and detect feature areas using gradient-weighted class activation mapping (Grad-CAM). To train the deep learning networks, we construct a dataset consisting of single and multi-feature. Our networks comprised of 12 layers classified the machining features with high accuracy of 98.81% on generated datasets. In addition, we estimated the area of the machining feature by applying Grad-CAM to the trained model. The deep learning model for machining feature recognition can be utilized in various fields such as 3D model simplification, computer-aided engineering, mechanical part retrieval, and assembly component identification. 

/pdf/3d-convolutional-neural-network-for-machining-feature-1uko4ode.pdf

3D convolutional neural network for machining feature recognition with gradient-based visual explanations from 3D CAD models

Agricultural machinery intelligence is the inevitable direction of agricultural machinery design, and the systems in these designs are important tools. In this paper, to address the problem of low processing power of traditional agricultural machinery design systems in analyzing data, such as fit, tolerance, interchangeability, and the assembly process, as well as to overcome the disadvantages of the high cost of intelligent design modules, lack of data compatibility, and inconsistency between modules, a novel agricultural machinery intelligent design system integrating image processing and knowledge reasoning is constructed. An image-processing algorithm and trigger are used to detect the feature parameters of key parts of agricultural machinery and build a virtual prototype. At the same time, a special knowledge base of agricultural machinery is constructed to analyze the test data of the virtual prototype. The results of practical application and software evaluation of third-party institutions show that the system improves the efficiency of intelligent design in key parts of agricultural machinery by approximately 20%, reduces the operation error rate of personnel by approximately 40% and the consumption of computer resources by approximately 30%, and greatly reduces the purchase cost of intelligent design systems to provide a reference for intelligent design to guide actual production.

/pdf/a-novel-agricultural-machinery-intelligent-design-system-1bjxp21g.pdf

A Novel Agricultural Machinery Intelligent Design System Based on Integrating Image Processing and Knowledge Reasoning

Neutral model-based interfacing of 3D design to support collaborative project management in the process plant industry


 3D reconstruction is a significant research topic in the field of Computer-Aided Design (CAD), which is used to recover editable CAD models from original shapes, including point clouds, voxels, meshes, and boundary representations (B-rep). Recently, there has been considerable research interest in deep model generation due to the increasing potential of deep learning methods. To address the challenges of 3D reconstruction and generation, we propose Brep2Seq, a novel deep neural network designed to transform the B-rep model into a sequence of editable parametrized feature-based modeling operations comprising principal primitives and detailed features. Brep2Seq employs an encoder-decoder architecture based on the Transformer, leveraging geometry and topological information within B-rep models to extract the feature representation of the original 3D shape. Due to its hierarchical network architecture and training strategy, Brep2Seq achieved improved model reconstruction and controllable model generation by distinguishing between the primary shape and detailed features of CAD models. To train Brep2Seq, a large-scale dataset comprising one million CAD designs is established through an automatic geometry synthesis method. Extensive experiments on both DeepCAD and Fusion 360 datasets demonstrate the effectiveness of Brep2Seq, and show its applicability to simple mechanical components in real-world scenarios. We further apply Brep2Seq to various downstream applications, including point cloud reconstruction, model interpolation, shape constraint generation and CAD feature recognition.

Brep2Seq: A dataset and hierarchical deep learning network for reconstruction and generation of computer-aided design models

Collaborative engineering design is a time-consuming process because the collaborative partners are spatially distant and familiar with divergent software solutions due to their local circumstances. The data compatibility problem rises between collaborating groups because each group uses a particular software solution that is different from other groups. A platform independent web-based solution for collaborative CAD system, that supports files of multiple CAD systems as well, has been implemented in this paper. The implemented solution saves time by providing web-based editing and visualization and preserves the design intent that makes it reusable.

A web-based solution for collaborative design supporting multiple CAD systems

It is difficult to exchange parametric assembly models using conventional neutral formats such as the standard for the exchange of product model data or the initial graphics exchange specification....

Exchange of parametric assembly models based on neutral assembly constraints

Feature-based translation of CAD models with macro-parametric approach: issues of feature mapping, persistent naming, and constraint translation

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A web-based solution for collaborative design supporting multiple CAD systems

Exchange of parametric assembly models based on neutral assembly constraints

Feature-based translation of CAD models with macro-parametric approach: issues of feature mapping, persistent naming, and constraint translation