Tianyi Quan'an partners with Shanghai Jiao Tong University to explore AI-powered visual recognition for high-temperature coating inspection

As aerospace and high-end equipment manufacturing sectors demand higher quality in thermal protection materials, traditional manual visual inspection can no longer meet the need for high precision and efficiency. To overcome this challenge, Tianyi Quanan is actively collaborating with top research institutions to drive innovation in AI-powered visual recognition for high-temperature coating inspection.

This technical discussion focuses on three core areas: (1) developing deep learning-based algorithms for automatic defect detection on coated surfaces, targeting precise classification of micro-cracks, bubbles, and delamination; (2) optimizing image acquisition and preprocessing for high-temperature environments to ensure high-quality inspection images under complex operating conditions; and (3) implementing lightweight deployment strategies for the detection model, enabling real-time AI integration into production line quality control with millisecond-level response times.

Following in-depth discussions, both parties reached multiple consensus points. The Shanghai Jiao Tong University technical team will provide cutting-edge algorithm support in computer vision and deep learning, while Tianyi Quan'an will open access to coating sample data and inspection requirements from actual production scenarios, establishing a tight "industry-academia-research" collaboration model. Both sides agree that introducing AI-powered visual recognition technology is expected to increase the coating defect detection rate to over 99% and reduce inspection time per unit by 60%, significantly lowering quality costs.

Moving forward, both parties will establish a joint project team to define a detailed technical roadmap and milestone plan. The prototype system is expected to be developed and validated within the next six months, followed by pilot deployment on Tianyi Quan'an's production line. This collaboration marks a critical step for Tianyi Quan'an in intelligent quality inspection and sets a benchmark for industry-academia-research collaborative innovation.