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Design of T-beam Formwork Polishing Robot Based on Deep Learning
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Ying-quan CHEN1, Jun-feng SHI1, *, Lei ZHAI2, Chen LIU3, Hui-dong LI2
Science Technology and Engineering | 2025, 25(16) : 6733 - 6741
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Science Technology and Engineering | 2025, 25(16): 6733-6741
Papers·Mechanical and Instrumental Industry
Design of T-beam Formwork Polishing Robot Based on Deep Learning
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Ying-quan CHEN1, Jun-feng SHI1, *, Lei ZHAI2, Chen LIU3, Hui-dong LI2
Affiliations
  • 1 School of Civil, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China
  • 2 National Key Laboratory of Intelligent and Green Bridge Construction, Wuhan 430050, China
  • 3 China Railway Bridge Science Research Institute Ltd. , Wuhan 430034, China
Published: 2025-06-08 doi: 10.12404/j.issn.1671-1815.2406880
Outline
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A deep learning based T-beam formwork polishing robot was designed for the problems of difficult and time consuming polishing of T-beam formwork for variable cross-section. Firstly, an adaptive polishing structure was proposed to solve the technical problem that the existing polishing device cannot fit the inner variable cross-section of the T-beam formwork, and the polishing roller was easy to get stuck in the T-beam formwork partition. Secondly, in order to realize the quantitative monitoring of the polishing quality, a YOLOv8n-DSE algorithm was proposed to identify concrete dirt and stains on the formwork, the DySample dynamic up-sampling module was introduced to enhance the anti-interference ability of the model and accelerate the calculation speed, to improve the accuracy of small target detection, the SOEP (small object enhance pyramid) module was designed to improve the detection performance of small target detection through the SPDConv(space to depth convolution) to obtain the information features of the small target and give them to the CSP(cross stage partial)-Omni-Kernel for the integration of the features. Finally, the EMA(exponential moving average)-SlideLoss was replaced to make the model more concerned with the quantitative monitoring of the concrete, allowed the model to focus more on difficult targets, which can improve the effect on difficult case detection. The accuracy, recall, and mAP(mean average precision) values are improved by 3.1%, 9.7%, and 3.2%, respectively, compared with those before the improvement. The improved model was deployed to the robot and tested in the field. The results show that the equipment meets the plant's needs for polishing variable-section T-beam formwork.

T-beam formwork for variable cross-section  /  deep learning  /  polishing robot  /  adaptive sanding  /  YOLOv8n
Ying-quan CHEN, Jun-feng SHI, Lei ZHAI, Chen LIU, Hui-dong LI. Design of T-beam Formwork Polishing Robot Based on Deep Learning[J]. Science Technology and Engineering, 2025 , 25 (16) : 6733 -6741 . DOI: 10.12404/j.issn.1671-1815.2406880
Year 2025 volume 25 Issue 16
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Article Info
doi: 10.12404/j.issn.1671-1815.2406880
  • Receive Date:2024-09-13
  • Online Date:2025-07-09
  • Published:2025-06-08
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History
  • Received:2024-09-13
  • Revised:2025-03-20
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Affiliations
    1 School of Civil, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China
    2 National Key Laboratory of Intelligent and Green Bridge Construction, Wuhan 430050, China
    3 China Railway Bridge Science Research Institute Ltd. , Wuhan 430034, China
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表12种不同金属材料的力学参数

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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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