Springback in aerospace tube bending is a closed-loop control problem; the elastic rebound of the workpiece after tool release represents a systematic output error caused by material nonlinearities and batch-to-batch parametric uncertainties through a conventional fragmented workflow that lacks any feedback path for sensing, compensating, or reporting such errors together. This paper introduces a four-layer closed-loop control structure composed of unified geometry parameterization, automatic Finite Element (FE) solver combination, physics-informed springback correction, and structured quality-reporting system to achieve standardized cross-connections between components through uniform interface specifications without involving any human intervention. The primary algorithms are a physics-informed uncertain compensation module that models the change in elasticity as a bounded disturbance and analytically obtains a first-order feedforward correction of Euler-Bernoulli beam theory to reduce systematic springback underprediction without re-running simulations. The structure of this system uses an independent upgradeable design method at all levels to expose a plain text interface specification for any subsequent layer, which includes the FE solvers, such as learning-based surrogates and reinforcement-learning agents. An additional automatic diagnosis-based decision-making layer reports a closed outer control loop through mapping violation of quality index values to process parameter adjustments. The framework has been tested using Ti-3Al-2.5V thin-walled aerospace tubing (diameter D=20 mm, wall thickness t=1.0 mm, R/D=1.5), which was measured by a Coordinate Measuring Machine (CMM) and compared with the results of twenty experiments. Prediction errors of the springback angle, wall-thinning rate, and cross-sectional deformation are 0.6°, 1.6%, and 0.6%, respectively. All meet aerospace acceptance standards. Pipelines are prepared in 1/24th of the Fragmented Manual's preparation time, achieving a sufficient speed for iterations aligned with Agile Aerospace qualification programs.
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A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.