Measuring 3D process plant model similarity based on topological relationship distribution. Issue 4 (7th June 2017)

• Record Type: Journal Article
• Authors: Wen, Rui; Tang, Weiqing; Su, Zhiyong
• Is Part Of: Computer-aided design and applications. Volume 14:Issue 4(2017)
• Page Start: 422
• Page End: 435
• Publication Date: 2017-06-07
• Subjects: Process plant -- 3D model -- similarity measure -- feature extraction -- graph similarity -- topological relationship distribution; Computer-aided design -- Congresses; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Congresses; Engineering design -- Periodicals; 620.00420285
• DOI: 10.1080/16864360.2016.1257185
• Languages: English
• ISSNs: 1686-4360

Abstract

ABSTRACT: Current research on 3D model similarity mainly concentrates on shape feature extraction, as well as the measurement with respect to individual objects. However, a quintessential Process Plant Model (PPM) consists of thousands of geometric solids, such as reaction vessels, pipelines and supports. These solids are interconnected following specific engineering rules. When measuring PPM similarity, geometric features as well as engineering attributes should both be taken into account. Therefore, existing shape based methods are inapplicable. As topological relationships are the core of PPM, this paper applies graph similarity into PPM and presents a new similarity measurement based on Topological Relationship Distribution (TRD) feature. First, a Relation Tree (RT) model for extracting TRD is proposed. The RT model attains and stores a PPM's relationship statistics by traversing all attributes and topological relationships of components. Second, as to achieve the comparable feature vector, standardization is performed via mapping relationship statistics into vector space. Last, a hybrid similarity function combining both directional and numerical differences in feature vectors is proposed to evaluate PPM similarity. Due to the exploitation of topological features and engineering attributes rather than raw directions and positions, the TRD based method embraces the properties of translation, rotation and similarity transformation consistency. Experimental results demonstrate the feasibility and accuracy of the proposed framework. GRAPHICAL ABSTRACT: