Gradient grain structure materials with superior mechanical properties of high strength and high toughness have attracted widespread attention. Gradient materials can effectively improve toughness by constructing a microstructure from fine to coarse crystals inside the material, which has gradually become a hotspot of attention in the academic and engineering communities. In this paper, based on the crystal plasticity intrinsic theory, dislocation density is introduced as a characterization quantity, and cohesive units are added at grain boundaries to simulate damage fractures. The results of this study reveal the fracture damage mechanism of gradient crystal structure materials, providing new ideas and methods for the design of gradient materials.

Under extreme thermomechanical conditions, the equiatomic CrMnFeCoNi (Cantor) alloy, a prototypical FCC high-entropy alloy, exhibits diverse deformation mechanisms and superior mechanical properties, driven by lattice distortion effect and multi-slip behavior. However, the atomic scale evolution of manufacturing-induced voids across wide temperature and strain rate ranges, and especially their orientation-dependent interaction with slip systems, remains poorly understood, limiting reliable application. Here, using molecular dynamics (MD) simulations, we systematically investigate how initial void orientation, together with wide ranges of temperature (300-900 K) and strain rate (5×10⁸-1×10¹⁰ s⁻¹), affects the mechanical response and phase transformation of pre-existing voids Cantor alloy. Our results reveal that the failure mode of the Cantor alloy is governed by the coupling between void orientation and slip system activity, a coupling that is strongly modulated by strain rate and temperature. Increasing strain rate shifts the dominant mechanism from FCC-to-HCP transformation driven by stacking fault accumulation (TRIP effect) at lower rates to amorphization-dominated deformation at higher rates. Increasing the temperature amplifies lattice distortion, increasing the stacking fault energy (SFE) and thereby promoting multi-slip and amorphous cluster formation, leading to thermal softening. Furthermore, under coupled high-temperature and high-strain-rate conditions, the influence of void orientation weakens significantly; amorphization dominates failure, suppressing dislocation activity and multi-layer stacking fault (MSF) formation. This study provides a mechanism-based framework for understanding void-induced damage in HEAs under extreme thermomechanical loading, offering atomic-scale guidelines for defect-tolerant alloy design.

In this study, a novel framework was presented for accelerating the prediction of the mechanical response of honeycomb structures under dynamic crushing, using 2D cells to surrogate 3D honeycomb structures by machine learning (ML). The sizes of different honeycomb structures were designed and the necessary training data obtained through finite element (FE) simulations, but without using any explicit features of the honeycomb structures in the ML model. A pixelation method of the FE model was proposed for realizing the separation of a complete cell structure from the honeycomb FE grid data, such as nodes and elements, and convert it into a matching pixel map. FE data was structuralized while reducing the large amount of computational power consumed in identifying complex array structures. Unsupervised automatic extraction of low-dimensional features of pixel maps was performed using a convolutional denoising autoencoder (CDAE). Crushing velocity was used as an explicit external parameter, together with the extracted latent features as input to the long-short term memory (LSTM) network, to complete the prediction of the crushing deformation and stress-strain curves of a honeycomb structure under dynamic loading. Results showed that the constructed ML model could describe the dynamic crushing response behavior. Compared with the traditional FE method, the prediction model was 4.45×103 and 1.05×103 times faster in predicting the stress-strain and structural deformation response, respectively. For larger array models, this order of magnitude was more improved. The mechanical response prediction model provided a method for fast analysis and evaluation of the dynamic mechanical response behavior of similar honeycomb structures, which could be helpful for the design development of bionic structure-based designs.