Enhancing weld performance of AA2024-T351 using drilled-to-bossed geometry and deep learning prediction in direct drive friction welding

orthogonal array to evaluate both tensile and torsional strength. The experiments were conducted on a customized engine lathe, with friction pressure, forging pressure, spindle speed, faying surface geometry, and friction time selected as the controllable process parameters. In parallel, advanced deep learning models, including an ensemble residual network, a compact attention network, and an adaptive multiscale network, were implemented to predict ultimate tensile strength based on the welding conditions. The results revealed that the optimal parameter combination of 30 MPa friction pressure, 70 MPa forging pressure, 2200 rpm spindle speed, drilled-to-bossed geometry, and 4 min of friction time yielded exceptional mechanical performance, achieving a tensile strength of 538 MPa and torsional strength of 325 MPa. The drilled-to-bossed configuration demonstrated significantly higher joint strength compared to conventional flat-to-flat joints. Moreover, among the deep learning models, the ensemble residual network achieved the highest predictive accuracy with an R² value of approximately 0.81, effectively capturing the complex relationship between process parameters and weld strength.