• CIFLE enables energy-based low-cycle fatigue life prediction for wrought Mg alloys. • Cycle-based identification reveals governing fatigue parameters. • Probabilistic network clarifies variable effects via posterior analysis. • With 75% fewer tests, CIFLE successfully restored energy-life relations. Magnesium (Mg) alloys are prime candidates for lightweight structures owing to their low density and high specific strength, but the pronounced basal texture that develops during wrought processing produces marked tension–compression asymmetry. Under low‑cycle fatigue (LCF) regime, this asymmetry is driven chiefly by repeated twinning–detwinning, making reliable life assessment exceptionally difficult. Therefore, cycle‑informed fatigue‑life estimation (CIFLE) is presented as a unified, physics‑aware machine learning framework for predicting LCF lives of wrought Mg alloys that display pronounced asymmetry. CIFLE links three data‑driven modules: a neural network that synthesizes representative hysteresis loops from basic loading inputs, an automated routine that interprets each loop into a concise set of mechanical and microstructural damage parameters, and a Bayesian network that maps those parameters to the life fraction while quantifying predictive uncertainty. The framework is trained and validated with cyclic deformation data from extruded AZ91 and SEN9 alloys, covering multiple strain amplitudes and extrusion conditions. Compared with conventional ε – N and energy‑based models, CIFLE achieves higher accuracy and well-calibrated uncertainty, delivering reliable life estimates at untested strain amplitudes by bracketing and refining energy bounds from neighboring tests. It also augments sparse datasets through loop synthesis and preserves accuracy even when most tests are withheld. In a case at an untested strain amplitude, the framework narrows the initial empirical life window and yields estimates that closely follow measured lives, whereas traditional models require additional experiments or extensive parameter tuning. By combining physics-based energy concepts with data-driven cycle synthesis, the framework provides an accurate, interpretable and data-efficient route for fatigue design of wrought Mg alloys.
Yu et al. (Sun,) studied this question.