Digital Twin-Driven Sorting System for 3D Printing Farm
Modern agricultural intelligent manufacturing faces critical challenges including low automation levels, safety hazards in high-temperature processing, and insufficient production data integration. Digital twin technology and 3D printing offer promising solutions through real-time virtual–physical synchronization and customized equipment manufacturing, respectively. However, existing research exhibits significant limitations: inadequate real-time synchronization mechanisms causing delayed response, poor environmental adaptability in unstructured agricultural settings, and limited human–machine collaboration capabilities. To address these deficiencies, this study develops a digital twin-driven intelligent sorting system for 3D-printed agricultural tools, integrating an Articulated Robot Arm, 16 industrial-grade 3D printers, and the Unity3D 2024.x platform to establish a complete “printing–sorting–warehousing” digitalized production loop. Unlike existing approaches, our system achieves millisecond-level bidirectional physical–virtual synchronization, implements an adaptive grasping algorithm combining force control and thermal sensing for safe high-temperature handling, employs improved RRT-Connect path planning with ellipsoidal constraint sampling, and features AR/VR/MR-based multimodal interaction. Validation testing in real agricultural production environments demonstrates a 98.7% grasping success rate, a 99% reduction in burn accidents, and a 191% sorting efficiency improvement compared to traditional methods, providing breakthrough solutions for sustainable agricultural development and smart farming ecosystem construction.
Authors
- Zeyan Wang
- Jun Chen (ORCID: https://orcid.org/0000-0002-4494-4850)
- Qi Mao (ORCID: https://orcid.org/0000-0001-8513-5577)
- Fei Xie (ORCID: https://orcid.org/0000-0002-5426-8390)
- Yijian Liu (ORCID: https://orcid.org/0009-0001-5887-2392)
Institutions
- Nanjing Normal University (CN)
Publication Details
- Journal
- Applied Sciences
- Published
- 2025-09-19
- DOI
- https://doi.org/10.3390/app151810222
- Citations
- 1
- Primary Topic
- Digital Transformation in Industry
- Type
- article
- Field-Weighted Citation Impact
- 1.24
Funders
- Government of Jiangsu Province