The development and validation of constitutive models for high-temperature deformation are critical for bridging microstructure evolution with macroscopic mechanical behavior in materials. In this study, we systematically analyzed the hot deformation behavior of Fe-Mn-Cr-based alloys, compared the modeling processes of physical, phenomenological, and data-driven approaches in detail, and optimized their structural and predictive properties. First, the advantages, disadvantages, and applicability of three traditional models, namely the physical Arrhenius model, the phenomenological Johnson–Cook model, and the artificial neural network (ANN) model, are compared for flow stress prediction. Subsequently, traditional mathematical derivations and numerical optimization methods are evaluated. The parameters and architecture of the ANN model are then systematically optimized using optimization algorithms to enhance training efficiency and prediction accuracy. Finally, sensitivity analysis integrated with Bayesian posterior probability density functions enables the calibration of physical model parameters and uncertainty quantification. The results demonstrate that the ANN with optimized parameters and architecture achieves superior prediction accuracy (R² = 0.9985, AARE = 3.01%) compared to traditional methods. Bayesian inference-based quantification of parameter uncertainty significantly enhances the reliability and interpretability of constitutive model parameters. This study not only reveals the strain–temperature coupling effects in the hot deformation behavior of Fe-Mn-Cr-based alloys but also provides systematic methodological support for constitutive modeling of high-performance alloys and a theoretical foundation for material processing technology design.

Hydraulic rock drill exhibits outstanding attributes of high power and high frequency, but there are some issues including unclear mechanisms governing impact dynamic behaviors and inaccurate evaluation of impact performance. In this study, a dynamic test platform for the hydraulic rock drill was established by employing the terminal velocity method, utilizing a high-frequency non-contact laser displacement sensor to precisely capture the transient kinematics of the impact piston. The quantitative results indicate that as the input pressure rises from 10 MPa to 23 MPa, the impact frequency increases from 50 Hz to 76.9 Hz, and the impact energy increases from 89.9 J to 275 J. A hydraulic rock drill AMESim simulation model incorporating the impact system, collision medium and buffer system was developed and validated. This reveals the operating mechanism of impact piston driven by the equivalent pressure difference between the front and rear chambers. And the stroke reversal interval governs the duration between the deceleration onset and collision of the impact piston. As a result, both excessively large and small stroke reversal intervals will lower the impact power. The 12 mm stroke reversal interval has been identified as the optimal setting for maximizing impact power, at which the impact power reaches 17,561.3 W, which presents an increase of 4.70% and 3.12% compared to the intervals of 7 mm and 17 mm, respectively. This study contributes a reliable theoretical basis and direct data support to the performance evaluation and optimized design of hydraulic shock systems.