JOURNAL ARTICLE
Path planning and real-time optimization of robotic arm 3D printing.
Published In: International Journal of Modeling, Simulation & Scientific Computing, 2025, v. 16, n. 3. P. 1 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Wang, Yaxin; Zhao, Chun; Liu, Wenzheng; Li, Shun 3 of 3
Abstract
In the era of Industry 4.0, 3D printing has shown significant outcomes. To address the challenges of large-format complex material printing and forming, such as spatial constraints and excessive support structures in traditional 3D printing, the integration of industrial robots with 3D printing technology is proposed. However, robotic 3D printing introduces challenges in path planning and real-time optimization. This paper presents a methodology for path planning and real-time optimization of robotic arms on a 3D printing platform. The approach involves adjusting the printing path by modifying the nozzle printing posture and implementing obstacle avoidance algorithms. The study uses geometric and algebraic methods to optimize the robotic arm trajectory to improve the precision of reaching print points, reduce the printing cycle, and minimize material wastage. To verify the feasibility of this method, a case study in 3D printing is conducted to examine the practical application of motion planning for robotic arms based on digital twin technology. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Modeling, Simulation & Scientific Computing. 2025/06, Vol. 16, Issue 3, p1
- Document Type:Article
- Subject Area:Engineering
- Publication Date:2025
- ISSN:17939623
- DOI:10.1142/S1793962325410132
- Accession Number:186535084
- Copyright Statement:Copyright of International Journal of Modeling, Simulation & Scientific Computing is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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