Design and Numerical Calculations of Pre-Detection for Defects in T-Shaped Aluminum Alloys

In modern manufacturing, accurate detection of internal defects in aluminum alloys is crucial for ensuring product quality and safety. Traditional methods like X-ray inspection and ultrasonic testing are precise but costly, complex, and slow. This study proposes an innovative composite detection scheme integrating mechanical pre-detection devices, post-precision detection unit and AI algorithms to address this challenge for T-shaped aluminum alloys. This study establishes finite element models to explore the impact of defects on bending roll reduction, providing valuable data for AI model development. The numerical calculation results indicate a pronounced “V” pattern in the roller constraint reaction force with defect position, exhibiting a maximum difference of 40 N. The reaction force of roller-3 showed higher sensitivity to defect locations, with a peak force of 211.5 N. These findings offer significant data support for subsequent AI model training and algorithm optimization. The proposed approach overcomes limitations of traditional methods, enabling efficient online quality monitoring, enhancing product reliability, and reducing production costs, thereby promoting the development of related industries.