Yaojun Miao

Based on the rotary bending loading principle, an experimental investigation into the fatigue properties of 6201 aluminum alloy individual wires is presented. Considering the long design service life, high-cycle fatigue (HCF) behavior, and slender geometry (high length-to-diameter ratio) of these wires, a high-speed rotary bending fatigue test platform is custom-designed and constructed. The design mechanically maximizes the probability of fracture at the midpoint of the constant-cross-section specimen, even when considering clamping damage at the ends. Through mechanical derivation for the test platform, based on the beam bending theory and the finite element method, a quantitative relationship is established between the bending stress amplitude and the deflection angle at the clamped end. Using this platform, the fatigue lives of 6201 aluminum alloy individual wires under various bending stress amplitudes are tested, and the stress–life (S-N) curve is obtained. The developed experimental method and the reported fatigue data in this study provide essential experimental and material data for fatigue life prediction and engineering design of 6201 aluminum alloy individual wires.

Producing high-conductivity aluminum conductors for power transmission involves 23 trace elements and multiple interconnected thermo-mechanical stages. The ultra-low alloying levels required to preserve high electrical conductivity create a narrow compositional window and highly imbalanced distributions, which hinder traditional data-driven learning. Here, we developed a physics-guided machine-learning framework based on 4458 valid industrial production records to predict tensile strength and electrical resistivity. In addition to raw composition and process parameters, we introduce ratio descriptors (e.g., Fe/Si and Al/Si) and propose a physics-informed metric termed the Equivalent Solute–Heat Index (ESHI) to couple key solute chemistry (Si, Fe, B) with normalized thermal-history intensity. Fe and Si primarily influence resistivity through impurity/solute scattering, while B mainly affects microstructural uniformity via grain refinement. Incorporating ESHI as an augmented signal into the best-performing XGB surrogate markedly improves generalizability, increasing the tensile strength R² from 0.75 to ~0.92. SHAP analysis reveals that ESHI dominates the decision logic by modulating both targets with metallurgically interpretable mechanisms: solute-controlled scattering and thermal history-traced second-phase evolution that stabilizes the microstructure. NSGA-III was further employed to map the Pareto front and identify composition–process combinations that optimize the strength–conductivity trade-off, enabling improved mechanical reliability while minimizing resistive losses in practical power-transmission applications. Experimental validation on industrial wires confirms this reliability.