Summary of Automatic Feature Recognition and Dimensional Attributes Extraction From Cad Models For Hybrid Additive-subtractive Manufacturing, by Muhammad Tayyab Khan et al.
Automatic Feature Recognition and Dimensional Attributes Extraction From CAD Models for Hybrid Additive-Subtractive Manufacturing
by Muhammad Tayyab Khan, Wenhe Feng, Lequn Chen, Ye Han Ng, Nicholas Yew Jin Tan, Seung Ki Moon
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is presented in this paper to facilitate seamless transitions from digital designs to physical products in modern manufacturing. The integration of Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP), and Computer-Aided Manufacturing (CAM) relies on accurate Automatic Feature Recognition (AFR) of CAD models, particularly in hybrid manufacturing that combines subtractive and additive processes. Traditional AFR methods fall short in recognizing features relevant to additive manufacturing and accurately extracting geometric dimensions and orientations, which are crucial for effective process planning. The proposed methodology uses Python Open Cascade to create a synthetic CAD dataset encompassing features pertinent to both additive and subtractive machining. A Hierarchical Graph Convolutional Neural Network (HGCNN) model is implemented to identify composite additive-subtractive features within the synthetic dataset. The key contribution of this work lies in its ability to recognize a wide range of manufacturing features, precisely extracting their dimensions, orientations, and stock sizes. The proposed model achieves remarkable feature recognition accuracy exceeding 97% and dimension extraction accuracy of 100%. This enhances the integration of CAD, CAPP, and CAM within hybrid manufacturing, enabling more informed decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a new way to recognize features in computer-aided design (CAD) models. This is important because it helps manufacturers make products by combining different techniques like subtractive and additive manufacturing. Currently, computers struggle to identify features that are relevant for additive manufacturing, which makes the process less accurate. The proposed method uses Python and Open Cascade to create a special dataset of CAD files that includes both types of features. Then, a special kind of neural network called Hierarchical Graph Convolutional Neural Network (HGCNN) is used to recognize these features. This approach is very good at identifying features and extracting important information like dimensions and orientations. The results show that this method can accurately identify features over 97% of the time. |
Keywords
* Artificial intelligence * Neural network
