A quantitative analysis of parametric CAD model complexity and its relationship to perceived modeling complexity

Digital product data quality and reusability has been proven a critical aspect of the Model-Based Enterprise to enable the efficient design and redesign of products. The extent to which a history-based parametric CAD model can be edited or reused depends on the geometric complexity of the part and the procedure employed to build it. As a prerequisite for defining metrics that can quantify the quality of the modeling process, it is necessary to have CAD datasets that are sorted and ranked according to the complexity of the modeling process. In this paper, we examine the concept of perceived CAD modeling complexity, defined as the degree to which a parametric CAD model is perceived as difficult to create, use, and/or modify by expert CAD designers. We present a novel method to integrate pair-wise comparisons of CAD modeling complexity made by experts into a single metric that can be used as ground truth. Next, we discuss a comprehensive study of quantitative metrics which are derived primarily from the geometric characteristics of the models and the graph structure that represents the parent/child relationships between features. Our results show that the perceived CAD modeling complexity metric derived from experts’ assessment correlates particularly strongly with graph-based metrics. The Spearman coefficients for five of these metrics suggest that they can be effectively used to study the parameters that influence the reusability of models and as a basis to implement effective personalized learning strategies in online CAD training scenarios

TOWARDS GENERATING SEMANTICALLY-RICH INDOORGML DATA FROM ARCHITECTURAL PLANS

Recent years has seen an increase in the work done on indoor data mapping and modeling. The standard data models provide different ways to store and access the indoor data but the way it is done is specific to the domain in which they are used. Although models like IFC, CityGML and IndoorGML provides rich functionality, the widespread availability of indoor data is not in these formats. This paper presents a step by step methodology to convert indoor building data of existing buildings, represented in architectural drawings into a topologically consistent and semantically rich indoor spatial model. The workflow presented consists of extracting relevant geometric entities from CAD drawings, assessing their topological relationships, using it to derive semantic information of spaces and making the data available in the form of IndoorGML. Since the current IndoorGML features lack the capability to store relevant semantic information, a semantic extension to IndoorGML is also proposed. The extraction of primitive spatial elements in rectilinear buildings like walls and doors are considered for the work presented in this paper. Development of a toolkit which implements this methodology in a seamless manner is work in progress and would incorporate extraction of complex spatial elements like staircases, ramps, curvilinear walls and windows, which is out of scope of the current work presented in this paper