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<p>Self-sealing tires are manufactured with a sealant layer applied to the inside of the tire during production. When a sharp puncturing object punctures the tire, this protective layer immediately flows into the hole and surrounds the puncturing object, sealing the puncture to prevent air leaks. This work focuses on developing a robotic cell for automated sealant dispensing and inspection. The robot programming uses ROBOGUIDE for parametric trajectory generation, synchronized with tire rotation, and integrated with a PLC through four operational modes (Home, Abort, Apply Sealant, and Validation). A vision system developed in C + + analyzes process quality by ensuring sealant width uniformity and enabling the detection of surface defects during tire rotation. Two inspection approaches were tested: laser line projection, which faced limitations under rotation, and LED illumination, which enabled accurate segmentation of the sealant layer. The robot’s trajectory generation was validated in simulation to ensure complete coverage, and the integrated vision system was experimentally tested on two tire models under rotation to measure sealant width and detect surface defects. The system achieved reliable width measurements and 72% effectiveness in identifying defective tires. These results confirm the feasibility of combining robotic sealant application with real-time vision inspection, representing the first fully automated “apply and inspect” solution for rotating tires.</p>

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Computer vision-based quality inspection for tire sealant application with simulated robotic dispensing

  • 期刊论文

  • 发表日期：

    2025年10月23日

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  • Leandro Moreira,
  • António Ramos Silva,
  • Filipe Pereira,
  • Rui Fazenda,
  • Adriano Santos,
  • António Silva,
  • António M. Lopes

摘要

Self-sealing tires are manufactured with a sealant layer applied to the inside of the tire during production. When a sharp puncturing object punctures the tire, this protective layer immediately flows into the hole and surrounds the puncturing object, sealing the puncture to prevent air leaks. This work focuses on developing a robotic cell for automated sealant dispensing and inspection. The robot programming uses ROBOGUIDE for parametric trajectory generation, synchronized with tire rotation, and integrated with a PLC through four operational modes (Home, Abort, Apply Sealant, and Validation). A vision system developed in C + + analyzes process quality by ensuring sealant width uniformity and enabling the detection of surface defects during tire rotation. Two inspection approaches were tested: laser line projection, which faced limitations under rotation, and LED illumination, which enabled accurate segmentation of the sealant layer. The robot’s trajectory generation was validated in simulation to ensure complete coverage, and the integrated vision system was experimentally tested on two tire models under rotation to measure sealant width and detect surface defects. The system achieved reliable width measurements and 72% effectiveness in identifying defective tires. These results confirm the feasibility of combining robotic sealant application with real-time vision inspection, representing the first fully automated “apply and inspect” solution for rotating tires.