• Korean Journal of Computational Design and Engineering
• /
• v.5 no.1
• /
• pp.23-32
• /
• 2000

In this paper, we present a Web-enabled product data sharing system for the support of RPD (Rapid Product Development) process by incorporating STEP (STandard for the Exchange of Product model data) with Web technology such as VRML (Virtual Reality Markup Language), SGML (Structured Generalized Markup Language) and Java. Extreme competition makes product life cycle short by incessantly deprecating current products with a brand-new one, and thus urges enterprises to devise a new product faster than ever. In this environment, an RPD process with effective product data sharing system is essential to outstrip competitors by speeding up the development process. However, the diversity of product data schema and heterogeneous systems make it difficult to exchange the product data. We chose STEP as a neutral product data schema and Web as an independent exchange environment to overcome these problems. While implementing our system, we focused on the support of STEP AP 203 UoF (Units of Functionality) views to efficiently employ STEP data models that are maximally normalized, and therefore very cumbersome to handle. Our functionality-oriented UoF view approach can increase users'appreciation since it facilitates the modular usage of STEP data models. This can also enhance the accuracy of product data. We demonstrate that our view approach is applicable to the configuration control of mechanical assemblies.

• Korean Journal of Computational Design and Engineering
• /
• v.6 no.4
• /
• pp.254-262
• /
• 2001

It is not possible to exchange parametric information of CAD (Computer Aided Design) models based on the current version of STEP (Standard leer the Exchange of Product model data). The design intent can be lost during the STEP transfer of CAD models. The ISO Parametrics Group has proposed the SMCH (Solid Model Construction History) schema in June 2000 that includes structures fur exchange of parametric information. This paper proposes the macro parametric approach that is intended to provide capabilities to transfer parametric information. In this approach, CAD models are exchanged in the form of macro files. The macro file contains user commands which are used in the modeling phase. To exchange CAD models using the macro parametric approach, modeling commands of commercial CAD systems are analyzed. Those commands are classified by the grouping method suggested by Bill Anderson. As a neutral file format, a standard modeling commands set has been defined. Mapping relations between the standard modeling commands set and the native modeling commands set of commercial CAD systems are defined.

• Journal of Korean Institute of Industrial Engineers
• /
• v.28 no.4
• /
• pp.435-442
• /
• 2002

It is difficult to access and share design data among heterogeneous CAD systems. Usually, different CAD systems exchange the design data using a neutral format such as IGES or STEP. A prototype CAD system which uses a geometric kernel and a commercial database management system has been implemented. The prototype system used the Open Cascade geometric kernel and the commercial object-oriented database ObjectStore. STEP provides the database schema. The database can be accessed from commercial CAD systems such as SolidWorks or Unigraphics. The data access module from a commercial CAD system is developed with the CAD system's native API, ObjectStore API functions, and ActiveX.

• Journal of KIBIM
• /
• v.9 no.4
• /
• pp.31-40
• /
• 2019

With Building Information Modeling(BIM) becoming the de facto standard for data sharing in the AEC industry, additional needs have increased to ensure the data integrity of BIM models themselves. Although the Industry Foundation Classes provide an open and neutral data format, its generalized schema leaves it open to data loss and misclassifications This research applied deep learning to automatically classify BIM elements and thus check the integrity of BIM-to-IFC mappings. Multi-view CNN(MVCC) and PointNet, which are two deep learning models customized to learn and classify in 3 dimensional non-euclidean spaces, were used. The analysis was restricted to classifying subtypes of architectural walls. MVCNN resulted in the highest performance, with ACC and F1 score of 0.95 and 0.94. MVCNN unitizes images from multiple perspectives of an element, and was thus able to learn the nuanced differences of wall subtypes. PointNet, on the other hand, lost many of the detailed features as it uses a sample of the point clouds and perceived only the 'skeleton' of the given walls.