A sketch-based semantic retrieval approach for 3D CAD models

During the new product development process, reusing the existing CAD models could avoid designing from scratch and decrease human cost. With the advent of big data,how to rapidly and efficiently find out suitable 3D CAD models for design reuse is taken more attention. Currently the sketch-based retrieval approach makes search more convenient, but its accuracy is not high enough; on the other hand, the semantic-based retrieval approach fully utilizes high level semantic information, and makes search much closer to engineers＇ intent.However, effectively extracting and representing semantic information from data sets is difficult.Aiming at these problems, we proposed a sketch-based semantic retrieval approach for reusing3 D CAD models. Firstly a fine granularity semantic descriptor is designed for representing 3D CAD models; Secondly, several heuristic rules are adopted to recognize 3D features from 2D sketch, and the correspondences between 3D feature and 2D loops are built; Finally, semantic and shape similarity measurements are combined together to match the input sketch to 3D CAD models. Hence the retrieval accuracy is improved. A sketch-based prototype system is developed.Experimental results validate the feasibility and effectiveness of our proposed approach.

Generating Quantitative Product Profile Using Char-Word CNNs

The online customer reviews provide important information for product improvement and redesign.However,many reviews are redundant with only several short sentences,which may even conflict with each other on the same aspect of a product.Thus it is usually a very challenging task to extract useful design information from the reviews and provide a clear description on the product’s various aspects amongst its competitors.In order to resolve this issue,we propose an approach to build hierarchical product profiles to describe a product’s kernel design aspects quantitatively.It is achieved via three main strategies:a double propagation strategy to achieve the associated features and customers’descriptions;a deep text processing network to build the aspect hierarchy;an aspect ranking approach to quantify each kernel design aspect.Experimental results validate the effectiveness of the proposed approach on online reviews.