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.
A 3D CAD model of workpiece with internal defect can effectively evaluate the effect of internal defect on the use performance of the workpiece by digital simulation, and the analysis result can ...provide technical support for optimizing manufacturing process, but how to obtain the model has always been a difficult problem. A reconstruction approach is proposed for 3D CAD model of workpiece with internal defect based on industrial CT image in this paper. Firstly, the CT image data is achieved by use industry CT scan the workpiece with internal defect, then the internal defect is segmented from the volume data composed of industrial CT images. The STereoLithography (STL) model of the workpiece (excluding defect) and the STL model of the internal defect are obtained via the 3D surface reconstruction of volume data. Then, in order to obtain the correct position of internal defect in the nominal CAD model of the workpiece, the STL model of the workpiece (excluding defect) is registered with the nominal CAD model. Finally, the CAD model of internal defect is reconstructed, and then the 3D CAD model of workpiece with internal defect is achieved by Boolean operation with the entity model of internal defect and the nominal CAD model of workpiece. The reconstructed CAD model of workpiece with internal defect is imported into the finite element simulation software, and the simulation analysis can be carried out according to the needs, which proves the effectiveness of the method in this paper. The proposed method make full uses the nominal CAD model of workpiece, and only need to reconstruct the CAD model of internal defect, which is simple and efficient.
•A light frequency-domain feature extraction method called lifting wavelet transform.•An ELM is used to overcome the issues of traditional learning algorithms.•A moth flame optimization technique ...trains the ELM which ensures a better performance.
Early detection of breast cancer based on a digital mammogram is an important research domain in the field of medical image analysis. An improved CAD model is proposed in this paper for the classification of breast masses into the normal or abnormal and benign or malignant category. The proposed model utilizes lifting wavelet transform (LWT) to extract the features from the region of interest mammogram images. The dimension of the feature vectors is then reduced by using a fusion of PCA and LDA methods. Finally, the classification is performed using a combination of an extreme learning machine and moth flame optimization technique (MFO-ELM). In the MFO-ELM algorithm, MFO is used to optimize the hidden node parameters of ELM. Further, 5-fold stratified cross-validation is used to improve the generalization performance of the model. The proposed model is evaluated on two standard datasets, namely MIAS and DDSM. From the experiment, it is observed that the proposed CAD model obtains ideal results for the MIAS dataset and achieves an accuracy of 99.76% (normal vs. abnormal) and 98.80% (benign vs. malignant) for the DDSM dataset. Our proposed model also demands minimum computational time as compared to other existing models. The experimental results show that the proposed model is superior to other state-of-the-art models in terms of classification accuracy with a significantly reduced number of features.
Design intent is generally understood simply as a CAD model's anticipated behavior when altered. However, this representation provides a simplified view of the model's construction and purpose, which ...may hinder its general understanding and future reusability. Our vision is that design intent communication may be improved by recognizing the multifaceted nature of design intent, and by instructing users to convey each facet of design intent through the better-fitted CAD resource. This paper reviews the current understanding of design intent and its relationship to design rationale and builds on the idea that communication of design intent conveyed via CAD models can be satisfied at three levels provided that specialized instruction is used to instruct users in selection of the most suitable level for each intent.
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