Motion Deformation Research Articles

Evaluation of Inherent Damping Feature of Beam Structure Based on Complex Modulus

Structural damping, which has been widely used to quantify the energy dissipation of engineering structures, plays an important role in dynamic analysis during the design phase and the assessment of existing structures. However, the development of an accurate damping model remains an open question due to the unclear mechanism. In this study, structural damping was identified based on loss factors for beam structures, which were used for presenting the damping behavior during vibrations. The equation of motion for the bending deformation of a Timoshenko beam was derived, accounting for the material damping. To identify the loss factor of the beam from the response signals at a limited number of measurement points, an inverse algorithm based on wave coefficient estimation was developed, considering the numerical stability for data interpretation. Based on the proposed loss factor identification method, several beam models with different shapes of cross-sections were investigated, focusing on the feasibility of the method of varying cross-section cases. Comparing the spectral element model with the loss factor and the finite element model with Rayleigh damping, it was confirmed that the loss factor can better describe the actual energy dissipation behavior of the structure. It provides a more accurate theoretical model for estimating structural damping in practical engineering.

Attracting dynamical modes of highly elastic fibres settling under gravity in a viscous fluid

The dynamics of a single highly elastic fibre settling under gravity in a very viscous fluid is studied numerically. We employ the bead model and multipole expansion of the Stokes equations, corrected for lubrication that is implemented in the precise Hydromultipole numerical codes. Four attracting regular dynamical modes of highly elastic fibres are found: two stationary shapes (one translating and the other rotating and translating), and two periodic oscillations around such shapes. The phase diagram of these modes is presented. It illustrates that the existence of each mode depends not only on the elasto-gravitation number but also on the fibre aspect ratio. Characteristic time scales, fibre deformation patterns and motion in the different modes are determined.

Nonequilibrium Lattice Dynamics of Individual and Attached PbSe Quantum Dots under Photoexcitation.

Quantum dot (QD) solids are emerging materials for many optoelectronic applications. To enhance interdot coupling and charge transport, surface ligands can be removed, allowing individual QDs to be attached along specific crystal orientations (termed "oriented attachment"). Optimizing the electronic and optical properties of QD solids demands a comprehensive understanding of the nanoscale energy flow in individual and attached QDs under photoexcitation. In this work, we employed ultrafast electron diffraction to directly measure how oriented attachment along ⟨100⟩ directions affects the nonequilibrium lattice dynamics of lead selenide QDs. The oriented attachment anisotropically alters the ultrafast energy relaxation along specific crystal axes. Along the ⟨100⟩ directions, both the lattice deformation and atomistic random motions are suppressed in comparison with those of individual QDs. Conversely, the effects are enhanced along the unattached ⟨111⟩ directions due to ligand removal. The oriented attachment switches the major lattice thermalization pathways from ⟨100⟩ to ⟨111⟩ directions.

Droplet boiling on two-tier hierarchical micro-pillar array surface – Nucleate boiling regime

Although hierarchical structured surfaces have shown great potential in improving heat transfer performance of boiling, the mechanisms associated with structure configuration and structure size remain elusive. In this work, the nucleate boiling regime of droplet on single-tier micro-pillar array (SM) surfaces and three types of two-tier hierarchical micro-pillar array (THM) surfaces is investigated comprehensively using the lattice Boltzmann model. Effects brought by the primary pillar size and the secondary pillar configuration on the development of vapor confined in pillar gaps are emphatically discussed from bubble nucleation through vapor coalescence to vapor expansion. Boiling dynamic characteristics concerning droplet morphological evolution, liquid-vapor interfacial deformation and triple-phase contact line (TPCL) motion are elucidated by the distributions of vapor pressure, fluid temperature and substrate heat flux. It is most efficient for the secondary pillar to enhance boiling heat transfer performance when configured on the side of primary pillar and least efficient when configured on the top of primary pillar.

Vessel-targeted compensation of deformable motion in interventional cone-beam CT

The present standard of care for unresectable liver cancer is transarterial chemoembolization (TACE), which involves using chemotherapeutic particles to selectively embolize the arteries supplying hepatic tumors. Accurate volumetric identification of intricate fine vascularity is crucial for selective embolization. Three-dimensional imaging, particularly cone-beam CT (CBCT), aids in visualization and targeting of small vessels in such highly variable anatomy, but long image acquisition time results in intra-scan patient motion, which distorts vascular structures and tissue boundaries. To improve clarity of vascular anatomy and intra-procedural utility, this work proposes a targeted motion estimation and compensation framework that removes the need for any prior information or external tracking and for user interaction. Motion estimation is performed in two stages: (i) a target identification stage that segments arteries and catheters in the projection domain using a multi-view convolutional neural network to construct a coarse 3D vascular mask; and (ii) a targeted motion estimation stage that iteratively solves for the time-varying motion field via optimization of a vessel-enhancing objective function computed over the target vascular mask. The vessel-enhancing objective is derived through eigenvalues of the local image Hessian to emphasize bright tubular structures. Motion compensation is achieved via spatial transformer operators that apply time-dependent deformations to partial angle reconstructions, allowing efficient minimization via gradient backpropagation. The framework was trained and evaluated in anatomically realistic simulated motion-corrupted CBCTs mimicking TACE of hepatic tumors, at intermediate (3.0 mm) and large (6.0 mm) motion magnitudes. Motion compensation substantially improved median vascular DICE score (from 0.30 to 0.59 for large motion), image SSIM (from 0.77 to 0.93 for large motion), and vessel sharpness (0.189 mm−1 to 0.233 mm−1 for large motion) in simulated cases. Motion compensation also demonstrated increased vessel sharpness (0.188 mm−1 before to 0.205 mm−1 after) and reconstructed vessel length (median increased from 37.37 to 41.00 mm) on a clinical interventional CBCT. The proposed anatomy-aware motion compensation framework presented a promising approach for improving the utility of CBCT for intra-procedural vascular imaging, facilitating selective embolization procedures.

AGAR - Attention Graph-RNN for Adaptative Motion Prediction of Point Clouds of Deformable Objects

This article focuses on motion prediction for point cloud sequences in the challenging case of deformable 3D objects, such as human body motion. First, we investigate the challenges caused by deformable shapes and complex motions present in this type of representation, with the ultimate goal of understanding the technical limitations of state-of-the-art models. From this understanding, we propose an improved architecture for point cloud prediction of deformable 3D objects. Specifically, to handle deformable shapes, we propose a graph-based approach that learns and exploits the spatial structure of point clouds to extract more representative features. Then, we propose a module able to combine the learned features in a adaptative manner according to the point cloud movements. The proposed adaptative module controls the composition of local and global motions for each point, enabling the network to model complex motions in deformable 3D objects more effectively. We tested the proposed method on the following datasets: MNIST moving digits, the Mixamo human bodies motions [ 15 ], JPEG [ 5 ] and CWIPC-SXR [ 32 ] real-world dynamic bodies. Simulation results demonstrate that our method outperforms the current baseline methods given its improved ability to model complex movements as well as preserve point cloud shape. Furthermore, we demonstrate the generalizability of the proposed framework for dynamic feature learning by testing the framework for action recognition on the MSRAction3D dataset [ 19 ] and achieving results on par with state-of-the-art methods.

An enhanced mid-surface method for large deformation and large overall motion of hyperelastic thin plate without locking

An enhanced mid-surface method for large deformation and large overall motion of hyperelastic thin plate without locking

Dynamic CBCT imaging using prior model-free spatiotemporal implicit neural representation (PMF-STINR)

Objective. Dynamic cone-beam computed tomography (CBCT) can capture high-spatial-resolution, time-varying images for motion monitoring, patient setup, and adaptive planning of radiotherapy. However, dynamic CBCT reconstruction is an extremely ill-posed spatiotemporal inverse problem, as each CBCT volume in the dynamic sequence is only captured by one or a few x-ray projections, due to the slow gantry rotation speed and the fast anatomical motion (e.g. breathing). Approach. We developed a machine learning-based technique, prior-model-free spatiotemporal implicit neural representation (PMF-STINR), to reconstruct dynamic CBCTs from sequentially acquired x-ray projections. PMF-STINR employs a joint image reconstruction and registration approach to address the under-sampling challenge, enabling dynamic CBCT reconstruction from singular x-ray projections. Specifically, PMF-STINR uses spatial implicit neural representations to reconstruct a reference CBCT volume, and it applies temporal INR to represent the intra-scan dynamic motion of the reference CBCT to yield dynamic CBCTs. PMF-STINR couples the temporal INR with a learning-based B-spline motion model to capture time-varying deformable motion during the reconstruction. Compared with the previous methods, the spatial INR, the temporal INR, and the B-spline model of PMF-STINR are all learned on the fly during reconstruction in a one-shot fashion, without using any patient-specific prior knowledge or motion sorting/binning. Main results. PMF-STINR was evaluated via digital phantom simulations, physical phantom measurements, and a multi-institutional patient dataset featuring various imaging protocols (half-fan/full-fan, full sampling/sparse sampling, different energy and mAs settings, etc). The results showed that the one-shot learning-based PMF-STINR can accurately and robustly reconstruct dynamic CBCTs and capture highly irregular motion with high temporal (∼ 0.1 s) resolution and sub-millimeter accuracy. Significance. PMF-STINR can reconstruct dynamic CBCTs and solve the intra-scan motion from conventional 3D CBCT scans without using any prior anatomical/motion model or motion sorting/binning. It can be a promising tool for motion management by offering richer motion information than traditional 4D-CBCTs.

Advanced Patch-Based Affine Motion Estimation for Dynamic Point Cloud Geometry Compression.

The substantial data volume within dynamic point clouds representing three-dimensional moving entities necessitates advancements in compression techniques. Motion estimation (ME) is crucial for reducing point cloud temporal redundancy. Standard block-based ME schemes, which typically utilize the previously decoded point clouds as inter-reference frames, often yield inaccurate and translation-only estimates for dynamic point clouds. To overcome this limitation, we propose an advanced patch-based affine ME scheme for dynamic point cloud geometry compression. Our approach employs a forward-backward jointing ME strategy, generating affine motion-compensated frames for improved inter-geometry references. Before the forward ME process, point cloud motion analysis is conducted on previous frames to perceive motion characteristics. Then, a point cloud is segmented into deformable patches based on geometry correlation and motion coherence. During the forward ME process, affine motion models are introduced to depict the deformable patch motions from the reference to the current frame. Later, affine motion-compensated frames are exploited in the backward ME process to obtain refined motions for better coding performance. Experimental results demonstrate the superiority of our proposed scheme, achieving an average 6.28% geometry bitrate gain over the inter codec anchor. Additional results also validate the effectiveness of key modules within the proposed ME scheme.

Deformable motion compensation in interventional cone-beam CT with a context-aware learned autofocus metric.

Interventional Cone-Beam CT (CBCT) offers 3D visualization of soft-tissue and vascular anatomy, enabling 3D guidance of abdominal interventions. However, its long acquisition time makes CBCT susceptible to patient motion. Image-based autofocus offers a suitable platform for compensation of deformable motion in CBCT, but it relies on handcrafted motion metrics based on first-order image properties and that lack awareness of the underlying anatomy. This work proposes a data-driven approach to motion quantification via a learned, context-aware, deformable metric, , that quantifies the amount of motion degradation as well as the realism of the structural anatomical content in the image. The proposed was modeled as a deep convolutional neural network (CNN) trained to recreate a reference-based structural similarity metric-visual information fidelity (VIF). The deep CNN acted on motion-corrupted images, providing an estimation of the spatial VIF map that would be obtained against a motion-free reference, capturing motion distortion, and anatomic plausibility. The deep CNN featured a multi-branch architecture with a high-resolution branch for estimation of voxel-wise VIF on a small volume of interest. A second contextual, low-resolution branch provided features associated to anatomical context for disentanglement of motion effects and anatomical appearance. The deep CNN was trained on paired motion-free and motion-corrupted data obtained with a high-fidelity forward projection model for a protocol involving 120kV and 9.90mGy. The performance of was evaluated via metrics of correlation with ground truth and with the underlying deformable motion field in simulated data with deformable motion fields with amplitude ranging from 5 to 20mm and frequency from 2.4 up to 4 cycles/scan. Robustness to variation in tissue contrast and noise levels was assessed in simulation studies with varying beam energy (90-120kV) and dose (1.19-39.59mGy). Further validation was obtained on experimental studies with a deformable phantom. Final validation was obtained via integration of on an autofocus compensation framework, applied to motion compensation on experimental datasets and evaluated via metric of spatial resolution on soft-tissue boundaries and sharpness of contrast-enhanced vascularity. The magnitude and spatial map of showed consistent and high correlation levels with the ground truth in both simulation and real data, yielding average normalized cross correlation (NCC) values of 0.95 and 0.88, respectively. Similarly, achieved good correlation values with the underlying motion field, with average NCC of 0.90. In experimental phantom studies, properly reflects the change in motion amplitudes and frequencies: voxel-wise averaging of the local across the full reconstructed volume yielded an average value of 0.69 for the case with mild motion (2mm, 12 cycles/scan) and 0.29 for the case with severe motion (12mm, 6 cycles/scan). Autofocus motion compensation using resulted in noticeable mitigation of motion artifacts and improved spatial resolution of soft tissue and high-contrast structures, resulting in reduction of edge spread function width of 8.78% and 9.20%, respectively. Motion compensation also increased the conspicuity of contrast-enhanced vascularity, reflected in an increase of 9.64% in vessel sharpness. The proposed , featuring a novel context-aware architecture, demonstrated its capacity as a reference-free surrogate of structural similarity to quantify motion-induced degradation of image quality and anatomical plausibility of image content. The validation studies showed robust performance across motion patterns, x-ray techniques, and anatomical instances. The proposed anatomy- and context-aware metric poses a powerful alternative to conventional motion estimation metrics, and a step forward for application of deep autofocus motion compensation for guidance in clinical interventional procedures.

TKO‐SLAM: Visual SLAM algorithm based on time‐delay feature regression and keyframe pose optimization

AbstractThis paper addresses the challenge of generating clear image frames and minimizing the loss of keyframes by a robot engaging in rapid large viewing angle motion. These issues often lead to detrimental consequences such as trajectory drifting and loss during the construction of curved motion trajectories. To tackle this, we proposed a novel visual simultaneous localization and mapping (SLAM) algorithm, TKO‐SLAM, which is based on time‐delay feature regression and keyframe position optimization. TKO‐SLAM uses a multiscale recurrent neural network to rectify object deformation and image motion smear. This network effectively repairs the time‐delay image features caused by the rapid movement of the robot, thereby enhancing visual clarity. Simultaneously, inspired by the keyframe selection strategy of the ORB‐SLAM3 algorithm, we introduced a grayscale motion‐based image processing method to supplement keyframes that may be omitted due to the robot's rapid large viewing angle motion. To further refine the algorithm, the time‐delay feature regression image keyframes and adjacent secondary keyframes were used as dual measurement constraints to optimize camera poses and restore robot trajectories. The results of experiments on the benchmark RGB‐D data set TUM and real‐world scenarios show that TKO‐SLAM algorithm achieves more than 10% better localization accuracy than the PKS‐SLAM algorithm in the rapid large viewing angle motion scenario, and has advantages over the SOTA algorithms.

Modeling and analysis for landing airbag–lunar soil interaction using a CPU–GPU-based FMBD-DEM computational framework

To investigate the landing performance of airbag cushion systems on the discrete lunar soil, this study proposes a novel modeling approach that couples multi-physics fields, including the flexible multibody systems (FMBS), the granular systems, and the gas effects. In this model, the GPU parallelism technique is utilized to address challenges arising from the large number of particles and complex contact detection involving large deformation and overall motions, and an efficient contact implementation between finite elements and particles is introduced. Additionally, contact modeling between airbags is realized using master–slave techniques. Then, a modified conventional serial staggered procedure is employed to couple flexible multibody dynamics (FMBD) and discrete element method (DEM) modules. Finally, a series of numerical examples is presented to validate the proposed dynamic model. The validated model successfully simulates the interaction between landing airbags and lunar soil. Subsequent parametric analysis contributes to the design optimization of the landing system. Moreover, discussions regarding computational efficiency illustrate that the proposed CPU–GPU-based FMBD-DEM is advantageous in engineering scenarios involving flexible components and large-scale granular systems.

The Distribution Pattern of Ground Movement and Co‐Seismic Landslides: A Case Study of the 5 September 2022 Luding Earthquake, China

AbstractMajor earthquakes can cause extensive landsliding that poses a major threat to both property and human lives. In addition to co‐seismically triggered ground failure, the earthquake‐affected region remains vulnerable to landslides due to loosened and unstable materials and structures. Many researchers have studied landslide distributions and their controlling factors after earthquakes, but the function of ground motion is unclear. To investigate the connection in a strike‐slip earthquake, we analyzed the 5 September 2022 Luding earthquake (Mw 6.6) in Sichuan Province, China. We interpreted remote‐sensing images to obtain the landslide distribution before and after the earthquake, calculated surface deformation from D‐InSAR data (pre‐ and post‐earthquake), utilized a point‐source model for the focal mechanism inversion, and then constructed a finite fault model for the rupture slip. There are clear differences in the landslide distributions on the two sides of the fault before and after the earthquake. The density of co‐seismic landslides on the west side of the fault exceeded that on the east side. The patterns of surface deformation and ground motion indicated that the areas with larger deformation and motion were associated with more landslides. Furthermore, the landslide size decreased with distance from the fault. A new finding is that co‐seismic landslides induced by strike‐slip earthquakes result in high landslide concentration on both sides of the fault, while previous studies find that co‐seismic landslides triggered by thrust earthquakes present a hanging wall concentrated distribution pattern. These findings contribute to a more comprehensive understanding of the connection between ground movement patterns and landslide distributions.

In silico analysis of embolism in cerebral arteries using fluid-structure interaction method

Ischemic stroke, particularly embolic stroke, stands as a significant global contributor to mortality and long-term disabilities. This paper presents a comprehensive simulation of emboli motion through the middle cerebral artery (MCA), a prevalent site for embolic stroke. Our patient-specific computational model integrates major branches of the middle cerebral artery reconstructed from magnetic resonance angiography images, pulsatile flow dynamics, and emboli of varying geometries, sizes, and material properties. The fluid-structure interactions method is employed to simulate deformable emboli motion through the middle cerebral artery, allowing observation of hemodynamic changes in artery branches upon embolus entry. We investigated the impact of embolus presence on shear stress magnitude on artery walls, analyzed the effects of embolus material properties and geometries on embolus trajectory and motion dynamics within the middle cerebral artery. Additionally, we evaluated the non-Newtonian behavior of blood, comparing it with Newtonian blood behavior. Our findings highlight that embolus geometry significantly influences trajectory, motion patterns, and hemodynamics within middle cerebral artery branches. Emboli with visco-hyperelastic material properties experienced higher stresses upon collision with artery walls compared to those with hyperelastic properties. Furthermore, considering blood as a non-Newtonian fluid had notable effects on emboli stresses and trajectories within the artery, particularly during collisions. Notably, the maximum von Mises stress experienced in our study was 21.83 kPa, suggesting a very low probability of emboli breaking during movement, impact, and after coming to a stop. However, in certain situations, the magnitude of shear stress on them exceeded 1 kPa, increasing the likelihood of cracking and disintegration. These results serve as an initial step in anticipating critical clinical conditions arising from arterial embolism in the middle cerebral artery. They provide insights into the biomechanical parameters influencing embolism, contributing to improved clinical decision-making for stroke management.

Design and research of deformable wheel-legged robot based on origami mechanisms

This study presents a novel deformable wheeled robot based on an origami mechanism, designed to address the limited environmental adaptability of traditional wheeled robots. The research begins by rigorously establishing the fundamental parameters of the origami unit through an in-depth analysis of the Miura origami motion principle and a comprehensive study of the correlation between geometric parameters. Leveraging the theory of origami thickening, the origami unit is thickened and integrated into the design of the deformable Wheel-Legged mechanism. Controlled by a single motor, four Wheel-Legged structures enable seamless transformation between wheeled and legged forms. The paper provides a comprehensive analysis of the robot’s deformation, obstacle crossing, and other motion processes. Furthermore, it thoroughly investigates the effects of various gaits on the stability of the robot’s movement, followed by rigorous simulation and experimental verification. The experimental results unequivocally demonstrate the robot’s capability in deformation, steering, and obstacle avoidance.

Moisture actuated cobalt alginate discoloration artificial muscle

Artificial muscles have the ability to sense changes in the external environment and generate rapid and significant contraction deformation or rotational motion. However, most artificial muscles are limited to a single form of deformation, and there are still great difficulties in achieving complex dual response modes. Herein, a novel artificial muscle made of cobalt alginate fiber with both rotational/contraction deformation and discoloration functions was successfully prepared by a simple wet spinning and directional twisting methods. The wet-sensitive color change performance is achieved by the formation of different hydrates of cobalt ions in varying humidity conditions, while rotational motion is induced by radial untwisting resulting from fiber swelling upon moisture absorption. In addition, the artificial muscle fiber exhibited a transition from blue to purple to red as the environmental humidity changes from 35 % to 85 %. When the fibers were exposed to water, the twisted fiber with a twist count of 2500 truns provided a rotational speed of over 2000 rpm, and the actuating and discoloration performance did not significantly decrease in 50 repeated cycle tests. This stable shape and color dual response artificial muscle can simulate traditional windmill devices, while also providing a new design idea for smart textiles and humidity visualization devices.

DEFORMATION INTERACTION OF STRONG EARTHQUAKES OF 2010–2016 IN THE ZONE OF INFLUENCE OF THE HIKURANGA SUPERPLUME (NEW ZEALAND) ACCORDING TO GPS OBSERVATIONS

Between 2010 and 2016, a series of 11 strong M>6 earthquakes occurred in New Zealand. In the area covering the epicentral zones of these seismic events, the spatiotemporal characteristics of movements and deformations of the Earth’s crust were obtained based on the processing of continuous satellite GPS observations at 64 points of the geodetic network. Using these data, we have studied the evolution of horizontal movements and deformations in order to reveal the possible relationship between the observed deformational and seismic processes. Analysis has been made on the total shear deformation, since the main tectonic structures of the region are faults with a shear mechanism of displacement of their sides. The presence of a giant mantle superplume in the area was the reason for the study of the behavior of horizontal dilatation deformation, and horizontal and vertical crustal motions. Based on the obtained digital deformation models, there were created kinematic visualizations, which are synoptic animations providing direct observations of the seismic deformation process and their heuristic analysis. The study revealed that a series of the strongest earthquakes may be interconnected by a long-term single deformation process, which is caused by the occurrence of an anomalous total shear deformation. The general maximum of shear deformation, dilatation deformation, and horizontal and vertical displacements are concentrated in the center of mantle superplume activity. Prior to strong seismic events, there occur zones of deficit (minimum) displacements of the Earth’s crust in the area of future epicenters, which is of research interest in terms of predicting their locations.

Study on damage mechanism and damage distribution of the rear plate under impact of debris cloud

The debris cloud generated by the hypervelocity impact (HVI) of orbiting space debris directly threatens the spacecraft. A full understanding of the damage mechanism of rear plate is useful for the optimal design of protective structures. In this study, the hypervelocity yaw impact of a cylindrical aluminum projectile on a double-layer aluminum plate is simulated by the FE-SPH adaptive method, and the damage process of the rear plate under the impact of the debris cloud is analyzed based on the debris cloud structure. The damage process can be divided into the main impact stage of the debris cloud and the structural response of the rear plate. The main impact stage lasts a short time and is the basis of the rear plate damage. In the stage of structure response, the continuous deformation and inertial motion of the rear plate dominate the perforation of the rear plate. We further analyze the damage mechanism and damage distribution characteristics of the rear plate in detail. Moreover, the connection between velocity space and position space of the debris cloud is established, which promotes the general analysis of the damage law of debris cloud. Based on the relationship, the features of typical damage areas are identified by the localized fine analysis. Both the cumulative effect and structural response cause the perforation of rear plate; in the non-perforated area, cratering by the impact of hazardous fragments is the main damage mode of the rear plate.

Deformation and boundary motion analysis of a faceted twin grain boundary

In this article, molecular dynamics simulations are used to understand how a nickel bicrystal with faceted incoherent Σ3 grain boundaries responds to uniaxial tensile loading. The deformation response is studied over a wide range of temperatures (100 – 900 K) and strain rates (107 – 1010 s−1). The dislocation extraction algorithm and common neighbor analysis are employed to identify the deformation mechanisms. Our results reveal that the yield stress decreases with temperature and increases with strain rate; whereas the elastic modulus decreases with temperature and is independent of strain rate. Furthermore, incipient plasticity is detected ahead of the yield point at lower temperatures and lower strain rates. Interestingly, the incoherent twin grain boundaries are quite mobile under the uniaxial tensile loading at lower temperatures and lower strain rates. But this mobility decreased at higher temperatures and higher strain rates, thereby, confirming this faceted grain boundary's non-Arrhenius (anti-thermal) migration behavior even under mechanical loading. From a deformation perspective, the incoherent twin facet of the grain boundary served as the major source for stacking fault formation at lower temperatures and higher strain rates. However, with the increase in temperature, the stacking faults became shorter and originated from both the incoherent twin facet and the tips of coherent twin facet. These results are in qualitative agreement with the experimental results documented in the literature.

Strong impact and engraving characteristics of multi-layer metallic materials under a two-stage combustion condition

Abstract This work is focused on the strong deformation motion mechanism of multi-layer metallic materials under extreme pressure conditions accompanied by a strong impact effect between the projectile composed of multi-layer metallic materials and the steel barrel of the launcher itself. Here, this transient engraving process of the projectile with complicated stress–strain based on a new type of structure and propulsion pattern is firstly studied by proposing a new physical and mathematical model with a coupling of dynamical equations and two-stage combustion ballistic equations. Comparisons of engraving distance of computational results are done with experimental studies, and a reasonable match has been obtained in these comparisons. Further, the thickness effect of the metal jacket of the projectile on the engraving stress and deformation level is analyzed to obtain universal engraving resistance equations. The results have also shown that the projectile's whole transient deformation motion process in the launcher can be divided into three periods due to the initial slide impact effect under the first-stage propellant gas and the later propelling effect as the second-stage propellant is ignited. However, the engraving durations are all less than 0.7 ms with different levels of elastic-plastic deformations between multi-layer metallic materials for different conditions.