Intelligent Dimension Annotation in Engineering Drawings: A Case-Based Reasoning and MKD-ICP Algorithm Approach
To address the demands for accuracy and completeness in engineering drawing dimension annotation, this paper presents an intelligent dimensioning method that integrates Case-Based Reasoning (CBR), K-Dimensional Tree (KD-Tree), and an enhanced Iterative Closest Point (ICP) algorithm. The proposed app...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/11/5992 |
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| author | Zhengqing Bai Xifeng Fang Bingyu Feng Qinghua Liu |
| author_facet | Zhengqing Bai Xifeng Fang Bingyu Feng Qinghua Liu |
| author_sort | Zhengqing Bai |
| collection | DOAJ |
| description | To address the demands for accuracy and completeness in engineering drawing dimension annotation, this paper presents an intelligent dimensioning method that integrates Case-Based Reasoning (CBR), K-Dimensional Tree (KD-Tree), and an enhanced Iterative Closest Point (ICP) algorithm. The proposed approach leverages a historical case database to extract key features from similar cases, providing high-quality initial references for the ICP algorithm. By combining KD-Tree’s efficient spatial search capabilities with ICP’s precise point cloud alignment, the method achieves both efficient mapping and accurate alignment of dimension information. Applied to creating engineering drawings of refrigerated van design as a case study, the results demonstrate that this method significantly enhances the efficiency and precision of dimension annotation, minimizes manual intervention and error rates, and showcases broad application potential in complex engineering design scenarios. The contributions include an innovative intelligent dimensioning method, the MKD-ICP algorithm for dimension mapping and alignment, and empirical validation of the approach’s effectiveness. |
| format | Article |
| id | doaj-art-de62846fed7a45e0bed16bc963db4e7d |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-de62846fed7a45e0bed16bc963db4e7d2025-08-20T02:33:06ZengMDPI AGApplied Sciences2076-34172025-05-011511599210.3390/app15115992Intelligent Dimension Annotation in Engineering Drawings: A Case-Based Reasoning and MKD-ICP Algorithm ApproachZhengqing Bai0Xifeng Fang1Bingyu Feng2Qinghua Liu3School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaTo address the demands for accuracy and completeness in engineering drawing dimension annotation, this paper presents an intelligent dimensioning method that integrates Case-Based Reasoning (CBR), K-Dimensional Tree (KD-Tree), and an enhanced Iterative Closest Point (ICP) algorithm. The proposed approach leverages a historical case database to extract key features from similar cases, providing high-quality initial references for the ICP algorithm. By combining KD-Tree’s efficient spatial search capabilities with ICP’s precise point cloud alignment, the method achieves both efficient mapping and accurate alignment of dimension information. Applied to creating engineering drawings of refrigerated van design as a case study, the results demonstrate that this method significantly enhances the efficiency and precision of dimension annotation, minimizes manual intervention and error rates, and showcases broad application potential in complex engineering design scenarios. The contributions include an innovative intelligent dimensioning method, the MKD-ICP algorithm for dimension mapping and alignment, and empirical validation of the approach’s effectiveness.https://www.mdpi.com/2076-3417/15/11/5992engineering drawingdimension annotationcase-based reasoningK-dimensional treeiterative closest point |
| spellingShingle | Zhengqing Bai Xifeng Fang Bingyu Feng Qinghua Liu Intelligent Dimension Annotation in Engineering Drawings: A Case-Based Reasoning and MKD-ICP Algorithm Approach Applied Sciences engineering drawing dimension annotation case-based reasoning K-dimensional tree iterative closest point |
| title | Intelligent Dimension Annotation in Engineering Drawings: A Case-Based Reasoning and MKD-ICP Algorithm Approach |
| title_full | Intelligent Dimension Annotation in Engineering Drawings: A Case-Based Reasoning and MKD-ICP Algorithm Approach |
| title_fullStr | Intelligent Dimension Annotation in Engineering Drawings: A Case-Based Reasoning and MKD-ICP Algorithm Approach |
| title_full_unstemmed | Intelligent Dimension Annotation in Engineering Drawings: A Case-Based Reasoning and MKD-ICP Algorithm Approach |
| title_short | Intelligent Dimension Annotation in Engineering Drawings: A Case-Based Reasoning and MKD-ICP Algorithm Approach |
| title_sort | intelligent dimension annotation in engineering drawings a case based reasoning and mkd icp algorithm approach |
| topic | engineering drawing dimension annotation case-based reasoning K-dimensional tree iterative closest point |
| url | https://www.mdpi.com/2076-3417/15/11/5992 |
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