An automatic approach for generating parametric models from topology-optimization results for three-axis CNC machining
•Automatic converting topology-optimization results into parametric models.•Determining machining directions and model differences aided by voxelization.•Generating sketch contours by projecting and heuristic rules.•Maintaining stiffness and shape in converting by employing layer-by-layer manner. Co...
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Published in | Computer aided design Vol. 182; p. 103863 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.05.2025
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Subjects | |
Online Access | Get full text |
ISSN | 0010-4485 |
DOI | 10.1016/j.cad.2025.103863 |
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Abstract | •Automatic converting topology-optimization results into parametric models.•Determining machining directions and model differences aided by voxelization.•Generating sketch contours by projecting and heuristic rules.•Maintaining stiffness and shape in converting by employing layer-by-layer manner.
Converting topology-optimization results into parametric models is crucial for manufacturing lightweight, high-stiffness products. However, currently available technologies cannot effectively automate and perform this conversion, especially for three-axis CNC machining. To bridge this gap, this study proposes an automatic approach for generating parametric models from topology-optimization results. First, integrating the machining characteristics of three-axis CNC machining, surface voxel accessibility analysis and different voxel clustering are carried out to determine the optimal machining directions and removable geometry of the (common) raw material model. Then, a parametric sketch contours generation method is presented for the removable geometry. This also provides the essential preparation for generating a parametric model by adding subtractive features to the raw material model. Particularly, a classified layered projection method is developed to ensure the final parametric model preserves the shape of the topology-optimization result as much as possible. This method can project and fit the removable geometry into quadratic curve sketch contours in a layer-by-layer scheme. Based on the sketch contour of each layer, the corresponding subtractive feature can be generated and added to the raw material model to remove the corresponding removable geometry. Performing this subtractive process layer by layer can generate the final parametric model of the topology-optimization result. Herein, certain constraints are also implemented during the subtractive process to ensure that the final model can preserve the stiffness of the topology-optimization result. Finally, the automatic conversion experiments on two complex and representative topology-optimization results show an average reduction of 19.3 % in maximum displacement (i.e., compliance) and an average increase of 45.2 % in mass as well as 28.4 % increase in volume when averaging the changes across both generated parametric models compared with their original topology-optimization results. The methodological comparisons also show that the presented approach has special benefits, including the ability to convert topology-optimization results automatically and effectively into parametric models, while maintaining stiffness and being processable by three-axis CNC machining. |
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AbstractList | •Automatic converting topology-optimization results into parametric models.•Determining machining directions and model differences aided by voxelization.•Generating sketch contours by projecting and heuristic rules.•Maintaining stiffness and shape in converting by employing layer-by-layer manner.
Converting topology-optimization results into parametric models is crucial for manufacturing lightweight, high-stiffness products. However, currently available technologies cannot effectively automate and perform this conversion, especially for three-axis CNC machining. To bridge this gap, this study proposes an automatic approach for generating parametric models from topology-optimization results. First, integrating the machining characteristics of three-axis CNC machining, surface voxel accessibility analysis and different voxel clustering are carried out to determine the optimal machining directions and removable geometry of the (common) raw material model. Then, a parametric sketch contours generation method is presented for the removable geometry. This also provides the essential preparation for generating a parametric model by adding subtractive features to the raw material model. Particularly, a classified layered projection method is developed to ensure the final parametric model preserves the shape of the topology-optimization result as much as possible. This method can project and fit the removable geometry into quadratic curve sketch contours in a layer-by-layer scheme. Based on the sketch contour of each layer, the corresponding subtractive feature can be generated and added to the raw material model to remove the corresponding removable geometry. Performing this subtractive process layer by layer can generate the final parametric model of the topology-optimization result. Herein, certain constraints are also implemented during the subtractive process to ensure that the final model can preserve the stiffness of the topology-optimization result. Finally, the automatic conversion experiments on two complex and representative topology-optimization results show an average reduction of 19.3 % in maximum displacement (i.e., compliance) and an average increase of 45.2 % in mass as well as 28.4 % increase in volume when averaging the changes across both generated parametric models compared with their original topology-optimization results. The methodological comparisons also show that the presented approach has special benefits, including the ability to convert topology-optimization results automatically and effectively into parametric models, while maintaining stiffness and being processable by three-axis CNC machining. |
ArticleNumber | 103863 |
Author | Gao, Shuming Li, Ming Liu, Wei Qiao, Lixian Kuang, Haiying Pan, Wanbin Wang, Yigang Xu, Gang |
Author_xml | – sequence: 1 givenname: Wanbin orcidid: 0000-0002-7557-3060 surname: Pan fullname: Pan, Wanbin email: panwanbin@hdu.edu.cn organization: School of Media and Design, Hangzhou Dianzi University, Hangzhou 310001, PR China – sequence: 2 givenname: Haiying orcidid: 0009-0007-4830-9743 surname: Kuang fullname: Kuang, Haiying organization: School of Media and Design, Hangzhou Dianzi University, Hangzhou 310001, PR China – sequence: 3 givenname: Shuming surname: Gao fullname: Gao, Shuming email: smgao@cad.zju.edu.cn organization: The State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou 310058, PR China – sequence: 4 givenname: Yigang surname: Wang fullname: Wang, Yigang organization: School of Media and Design, Hangzhou Dianzi University, Hangzhou 310001, PR China – sequence: 5 givenname: Gang surname: Xu fullname: Xu, Gang organization: School of Media and Design, Hangzhou Dianzi University, Hangzhou 310001, PR China – sequence: 6 givenname: Ming surname: Li fullname: Li, Ming organization: The State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou 310058, PR China – sequence: 7 givenname: Wei surname: Liu fullname: Liu, Wei organization: China Academy of Railway Sciences Corporation Limited, Beijing 100081, PR China – sequence: 8 givenname: Lixian surname: Qiao fullname: Qiao, Lixian organization: China Academy of Railway Sciences Corporation Limited, Beijing 100081, PR China |
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