Assessment of atomic structure of material, depending on the task, usually require computationally expensive numerical simulations or some form of direct human participation. One possible alternative approach is involving different kinds of artificial neural networks, which may, in certain problems, offer a satisfactory compromise between precision, performance and reproducibility. Sample of differently compressed aluminum crystals was considered. Based on generated pictures of residual distribution of defects in their post-compression relaxed state, two tasks of predicting spall strength and identifying prevailing type of lattice at maximum compression were solved by means of convolutional neural networks (CNNs). It is shown that machine learning based method that described in paper is efficient for solving such tasks, even if the algorithm and form of data representation are probably not optimal and dataset size is relatively small, and have perspective for further improvements via pure quantitative enhancements.

This work presents a multi-scale investigation of the competing mechanisms of dislocation-precipitate interaction in Al-Cu alloys under dynamic deformation. Molecular dynamics (MD) simulations uncover two competing stress-assisted bypass pathways that deviate from classical models: (i) dislocation climb at the precipitate front side, facilitated by the precipitate stress field and easer generation/sink of point defects, and (ii) cross-slip of screw segments along the precipitate lateral surfaces. Both processes are shown to operate without the need for long-range diffusion, making them viable at high strain rates. Probabilistic micromechanical models for climb and cross-slip, parameterized against MD data, are developed and embedded into a 2D discrete dislocation dynamics (DDD) framework. Large-scale DDD simulations demonstrate that these mechanisms are essential for accurately predicting the flow stress, as their exclusion leads to pronounced over-strengthening. The extensive DDD dataset is used to train an artificial neural network, which efficiently predicts the mechanical response and the quantitative contribution of each overcoming mechanism (Orowan loop, cutting, climb and cross-slip) across a wide range of microstructural parameters. The results provide a comprehensive, physics-based framework for modeling the strength of precipitation-hardened alloys under dynamic loading, emphasizing the necessity of incorporating non-conservative and cross-slip mechanisms.

It is well known that aluminum and copper exhibit structural phase transformations in quasi-static and dynamic measurements, including shock wave loading. However, the dependence of phase transformations in a wide range of crystallographic directions of shock loading has not been revealed. In this work, we calculated the shock Hugoniot for aluminum and copper in different crystallographic directions ([100], [110], [111], [112], [102], [114], [123], [134], [221] and [401]) of shock compression using molecular dynamics (MD) simulations. The results showed a high pressure (>160 GPa for Cu and >40 GPa for Al) of the FCC-to-BCC transition. In copper, different characteristics of the phase transition are observed depending on the loading direction with the [100] compression direction being the weakest. The FCC-to-BCC transition for copper is in the range of 150-220 GPa, which is consistent with the existing experimental data. Due to the high transition pressure, the BCC phase transition in copper competes with melting. In aluminum, the FCC-to-BCC transition is observed for all studied directions at pressures between 40 and 50 GPa far beyond the melting. In all considered cases we observe the coexistence of HCP and BCC phases during the FCC-to-BCC transition, which is consistent with the experimental data and atomistic calculations; this HCP phase forms in the course of accompanying plastic deformation with dislocation activity in the parent FCC phase. The plasticity incipience is also anisotropic in both metals, which is due to the difference in the projections of stress on the slip plane for different orientations of the FCC crystal. MD modeling results demonstrate a strong dependence of the FCC-to-BCC transition on the crystallographic direction, in which the material is loaded in the copper crystals. However, MD simulations data can only be obtained for specific points in the stereographic direction space; therefore, for more comprehensive understanding of the phase transition process, a feed-forward neural network was trained using MD modeling data. The trained machine learning model allowed us to construct continuous stereographic maps of phase transitions as a function of stress in the shock-compressed state of metal. Due to appearance and growth of multiple centers of new phase, the FCC-to-BCC transition leads to formation of a polycrystalline structure from the parent single crystal.

A physically based model of laser shock peening is established and experimentally verified. The laser-induced generation of stress wave in the confined geometry is considered directly through the heating and evaporation of the surface layer of copper described by a wide-range equation of state. The structure and attenuation of the stress wave is described by the dislocation plasticity model. In the experimental part, copper plates of three different thicknesses (0.5, 0.8, and 1.0 mm) were irradiated by 11-ns (FWHM) 1064-nm laser with energy densities of 64, 95, 127, and 191 J/cm(2), and the back free surface velocity histories were registered by means of photonic Doppler velocimetry. Consideration of different plate thicknesses allows us to decouple the effects of stress wave generation and attenuation and to verify independently the corresponding parts of the model. It is shown that the widely used Fabbro's model tends to underestimate the interface pressure pulse in copper because the stationary plasma expansion assumed in this model is established only after 30-60 ns of laser irradiation with a constant power density. The efficiency value of phi = 1 in Fabbro's model is optimal to reproduce the interface pressure pulse at nanosecond irradiation in contrast to the efficiency value of phi = 0.5, which is optimal to estimate the stationary level of pressure established for constant power density.

This study aims to develop a macroscopic continuum model of dynamic deformation of porous metals based on the application of artificial neural networks. Data sets obtained by modeling the compression of representative volumes of porous media by smoothed particle hydrodynamics based on a dislocation plasticity model previously parameterized for copper are used to train the ANN. Such modeling is used both for training data sets and to investigate the deformation physics of porous copper with micrometer and millimeter scale pores.

The surface of liquid aluminum in the absence of oxygen and after reacting with different levels oxygen gas has been investigated by performing molecular dynamics simulations based on embedded atom model potential. Results in the oxygen-free condition indicate that liquid aluminum exhibits surface-induced layering. The surface is randomly close-packed at the highest possible packing density. Upon exposure to oxygen, oxygen atoms get adsorbed and embedded within the top layers, relaxing the surface. The layered surface transforms into Al-rich amorphous alumina layer. The first amorphous alumina to form is Al5O. With increasing oxygen content, O/Al ratio increases, resulting in further relaxation. When the O/Al ratio approaches 1.0, Al-rich γ-alumina nucleates and grows epitaxially underneath the amorphous surface alumina. The change in molar surface area with surface relaxation is nonmonotonic. Implications of these results on the surface tension of partially oxidized liquid aluminum are presented in the paper.

A novel method to measure dynamic flow stress and corresponding strain rates obtained from Taylor tests using profiled samples with a reduced cylindrical head part was applied to study the dynamic characteristics of similar commercial 7075 and V95T1 aluminum alloys. The measured dynamic flow stress is verified using a classical Taylor’s approach with uniform cylinders and compared with the literature data. Our study shows that the dynamic flow stress of 7075 alloy, which is 786 MPa at strain rates of (4–8) × 103 s−1, exceeds the value of 624 MPa for V95T1 alloy at strain rates of (2–6) × 103 s−1 by 25%. The threshold impact velocity resulting in fracture of the 4 mm head part of the profiled samples is 116–130 m/s for 7075 alloy and only 108 m/s for V95T1 alloy. The fracture pattern is also different between the alloys with characteristic shear-induced cracks oriented at 45° to the impact direction in the case of V95T1 alloy and perpendicular to the breaking off head part in the case of 7075 alloy. On the other hand, the compressive fracture strain of V95T1 alloy, which is 0.29–0.36, exceeds that of 7075 alloy, which is 0.27–0.33, by approximately 8%. Thus, V95T1 aluminum alloy exhibits less strength but is more ductile, while 7075 aluminum alloy exhibits more strength but is simultaneously more brittle.

Increasing the strain rate up to about 1/ns in the conditions of ultra-short-pulse powerful laser irradiation of thin metal foils of submicron thickness reveals very strong elastic precursors, for which decoupling of pressure and stress deviators is unreasonable. A nonlinear stress-strain relationship (tensor equation of state-TEOS) is required for establishing theoretical models of such dynamic processes and unsteady shock waves. We proposed an ANN-TEOS model, which adopts an artificial neural network (ANN) trained on the data of density functional theory (DFT) calculations for the cold curve and analytical form for thermal contributions fitted to molecular dynamics (MD) data. We showed the efficiency of feed-forward ANN in approximation of the cold curve. Besides, the range of applicability of Hooke's law for approximation of the cold curve was examined. The DFT data were used to train a cold-curve ANN and to fit elastic constants of the Hooke's law with nonlinear corrections for copper and aluminum. Whereas the ANN is applicable for a complex approximation within a wide range of deformed states, the Hooke's law applicability is restricted to the strain level of about 0.1. MD simulations were used to construct and fit the thermal contributions. The developed ANN-TEOS model was applied to calculate the shock adiabats for both plastic shock wave (nearly hydrostatic omnidirectional loading) and elastic shock wave (uniaxial loading). Elastic shock Hugoniots lie above the plastic ones for both metals providing an opportunity for a shock wave to split into elastic precursor and plastic wave even for conditions, in which the plastic shock wave velocity exceeds the longitudinal sound speed. A simultaneous consideration of TEOS and plasticity model is required for the prediction of splitting and two-wave structure. Our comparison of several interatomic potentials with the stress sates calculated by means of DFT showed much higher precision of the classical force field in comparison with the examined machine-learning potentials for the considered problem of severe deformation. This result elucidates one more time the known problem of the restricted range of applicability of the machine-learning potentials and the need to include the required range of states in the training dataset for these potentials.

In this work, we perform a comprehensive study of the dynamic deformation and fracture of brass, including Taylor tests with classical and profiled cylinders and ball throwing experiments reaching the strain rates of about (0.1-1)/mu s, as well as atomistic and continuum-level numerical modeling. Molecular dynamics (MD) simulations are used to construct the equation of state (EOS) of brass and to study its fracture characteristics at shear deformation under negative pressure. An original model of fracture under combined tensile-shear loading is formulated, which takes into account both the accumulation of empty volume in the process of lattice loosening due to the lattice defect production in the course of plastic deformation and further mechanical growth of voids controlled by the dislocation plasticity. This atomic-scale model is transmitted to the macroscopic experiment-scale level and embedded into 3D dislocation plasticity model to describe the dynamic deformation and fracture of brass using the numerical scheme of smoothed particle hydrodynamics (SPH). A part of experimental data is used to find the optimal parameters of the dislocation plasticity model by means of the Bayesian global optimization method accelerated with the help of artificial-neural-network (ANN)-based emulator of the 3D model. Another part of experimental data is used to fit the fracture model parameter. The remaining experimental data, which are not used in the parameterization, are applied to verify the parameterized model. The developed physical-based model provides correct and meaningful description of the dynamic deformation and fracture of brass, while the developed formalized approach to its parameterization opens a way to wider use of this type of models in the engineering applications, including studies on dynamic performance and high-speed processing technologies.

Based on a database obtained using a high-speed plate impact model that relates impact parameters and material model parameters to the free surface velocity profile, the study compares the learning process and accuracy of a feedforward artificial neural network and a recursive neural network. A recursive neural network provides a significantly greater accuracy and requires less training time. Using a recursive neural network as a fast model emulator and Bayesian calibration can make it possible to solve the inverse problem of determining the substance model parameters from the free surface velocity profile with a greater accuracy.

The development of dynamic plasticity models with accounting of interplay between several plasticity mechanisms is an urgent problem for the theoretical description of the complex dynamic loading of materials. Here, we consider dynamic plastic relaxation by means of the combined action of dislocations and phase transitions using Al-Cu solid solutions as the model materials and uniaxial compression as the model loading. We propose a simple and robust theoretical model combining molecular dynamics (MD) data, theoretical framework and machine learning (ML) methods. MD simulations of uniaxial compression of Al, Cu and Al-Cu solid solutions reveal a relaxation of shear stresses due to a combination of dislocation plasticity and phase transformations with a complete suppression of the dislocation activity for Cu concentrations in the range of 30–80%. In particular, pure Al reveals an almost complete phase transition from the FCC (face-centered cubic) to the BCC (body-centered cubic) structure at a pressure of about 36 GPa, while pure copper does not reveal it at least till 110 GPa. A theoretical model of stress relaxation is developed, taking into account the dislocation activity and phase transformations, and is applied for the description of the MD results of an Al-Cu solid solution. Arrhenius-type equations are employed to describe the rates of phase transformation. The Bayesian method is applied to identify the model parameters with fitting to MD results as the reference data. Two forward-propagation artificial neural networks (ANNs) trained by MD data for uniaxial compression and tension are used to approximate the single-valued functions being parts of constitutive relation, such as the equation of state (EOS), elastic (shear and bulk) moduli and the nucleation strain distance function describing dislocation nucleation. The developed theoretical model with machine learning can be further used for the simulation of a shock-wave structure in metastable Al-Cu solid solutions, and the developed method can be applied to other metallic systems, including high-entropy alloys.

The playability limits in the bow force and bow acceleration parameter space (usually visualized as a Guettler diagram) define the conditions for establishing Helmholtz motion during bowed attacks in bowed-string instruments. Despite few theoretical and numerical studies, there is little empirical validation of these limits. The experimental scanning of a Guettler diagram is a tedious process, as it requires the use of a bowing machine to control the bowing parameters. This study proposes a method for collecting and analyzing data to produce measured Guettler diagrams. Experiments were conducted using a cello string in a monochord arrangement with fixed terminations. The string was excited using a robotic arm that allows control of the playing conditions by changing the bow force and bow acceleration. Each set of measurements contains more than 900 data points to obtain high-resolution Guettler diagrams. In addition, the measurements are repeated in reverse order and after dismounting and remounting the string. The results are compared with existing literature, with particular attention to theoretical limits. The experimental results suggest that the static and dynamic friction coefficients vary with bow force and acceleration, modifying the playability limits. [Work supported by the Austrian Science Fund (FWF) (P34852-N).]

The work examines the features of plastic relaxation in an aluminum single crystal containing an edge dislocation, deformed at a shear strain rate of 0.85-3.35 - 3.35 ns- 1.- 1 . An atomistic study shows that at shear strain rates up to 1.13 ns-1,- 1 , the dislocation slips in the subsonic regime usually assumed for aluminum, with an upper speed limit equal to the transverse sound speed. At a shear strain rate of 2.55 ns- 1 or higher, after a short movement with velocity limited by the transverse sound speed, the dislocation undergoes into the transonic regime, when its velocity is limited by the longitudinal sound speed. In all the cases considered, the rate of plastic relaxation provided by the slip of single initially existing dislocation is insufficient to relax the shear stress in the system. The strain increase in the system leads to the nucleation of secondary dislocations near the initially existing one. The nucleation of secondary dislocations always occurred in the local stress field created by the initially existing and moving dislocation. The nucleation of secondary dislocations in the subsonic and transonic regimes differs due to the change of the local stress field of the moving dislocation. A simple continuum model of stress relaxation is proposed that describes the observed features of the motion and nucleation of dislocations. The model is parameterized using the automatic Bayesian parameter identification. The parameters determined in this way are in reasonable agreement with the values known in the literature.

Numerous experimental and theoretical methods have focused on the bow–string interaction in bowed string instruments, including several artificial bowing setups. The current research aims to present an experimental approach to reproduce bowing techniques using a robotic arm. First, optical motion capture is used to track the 3D kinematics of the bow. The cello bow and corpus are equipped with reflective markers. The cello is mounted on a playing platform. The recorded 3D trajectories of the bow markers are used to control the motion of the robotic arm. This process requires converting the 3D data between the coordinate frames of the two systems. This conversion is described in detail in this paper. To demonstrate the performance of the proposed method, an experienced cellist was asked to play an adapted piece on the cello, which was then repeated using the robotic arm. The robotic arm is capable of accurately reproducing the bow velocity, but even minimal variations in position can compromise proper bow–string contact. To illustrate this, the study compares two similar robotic situations and discusses the challenges of adapting the robot’s coordinates as a function of a given playing parameter or the sound produced.

Data transfer from the level of atomistic simulations to the level of continuum modeling is one of the urgent problems in the mechanics of materials. In this work, we perform multiple molecular dynamics (MD) simulations of the uniaxial (along different crystallographic directions) and volumetric (isotropic) compression of porous HCP magnesium single crystals in order to study the atomistic processes of the plastic deformation of anisotropic porous material and to create a reference dataset (strain dependencies of stresses, porosity and dislocation density at different temperatures and initial porosities). The MD data are used to train two artificial neural networks (ANNs): the first ANN is a surrogate of the constitutive equation of porous magnesium, while the second ANN describes the dislocation nucleation/emission as the onset of plastic flow. In contrast to the previously considered case of porous FCC aluminum, the present approach takes into account the anisotropy of the matrix material. The ANN-based constitutive equation is successfully used within the continuum mechanics model for numerical study on the shock wave structure in porous magnesium in comparison with direct MD simulation of the shock wave problem.

Understanding the dynamics of bowed-string attacks involves exploring the relationship between bow acceleration, bow force, and the generation of Helmholtz motion during transients. This study addresses the following research question: How do theoretical limits of “playability” predict these parameters? Motivated by the need for experimental evidence in this domain, we present a comprehensive investigation into bowed-string transients within the bow acceleration and bow force parameter space, known as the Guettler diagram. This study exclusively employs an experimental methodology. The setup, employing a robotic arm, permits the collection of transient data under varying bowing conditions. Analysis of the bridge force waveform allows for the extraction of pre-Helmholtz transient times. Our results reveal a triangular playable region in the Guettler diagram, consistent with theoretical predictions and previous experimental findings. However, Guettler’s analytical limits for playable regions during transients show limitations. We investigate the role of friction, a key parameter idealized in the model used for obtaining these limits. Measured friction coefficients from transients reveal discrepancies with prior experimental studies, highlighting the need for further investigations in this direction.

This study investigates the influence of string properties on bowed string attack playability. To assess the attack playability of different string types, a variety of bow forces and bow accelerations were chosen to excite the strings and measure the transient response under different bowing control parameters. The experimentally obtained playability maps of transient duration as function of bow force and acceleration (Guettler diagram) were obtained with a robotic bowing machine, from four different types of cello G2 strings. Results indicate variations in playability across string types, suggesting that string properties impact attack duration.

Spall strength is an important property of material subjected to the shock wave loading. It substantially depends on the initial microstructure and its evolution during the preliminary shock compression. By an example of aluminum single crystal, a number of random axisymmetric compressed states are examined by means of molecular dynamics (MD) simulations, different deformation modes are revealed and the spall strength is calculated during subsequent volumetric tensile tests. A domain of elastic states with the spall strength of about 9 GPa, plastically deformed (dislocation plasticity) states with the spall strength of 6-7 GPa and the states with BCC phase transformations and the spall strength in the range 6-9 GPa are revealed as the main scenarios depending on the combination of strains at preliminary compression. MD results are generalized by means of artificial neural networks (ANNs), which allow interpolation between MD-studied points and plotting of continuous dependencies.

The paper is devoted to the development of the method of laser shock peening (LSP) of metals. To optimize the mode of LSP for Ti-6Al-4V specimens a deep learning model for predicting residual stresses by laser shock peening was developed. A numerical-experimental method was used to carry out the model training, in which an experimental study of the effect of different processing mode on the depth and distribution of residual stresses was carried out. The Johnson-Cook model was used as the governing relationship for modeling the dynamic deformation process. At the second stage, the problem of static equilibrium of a body with a plastically deformed area was numerically solved to determine residual stresses. The results of research on determination of the optimal configuration of the deep learning model showed that when using sinusoidal activation function of the neural network with 4 hidden layers and the number of neurons 10, the best level of accuracy in solving the problem is achieved. The obtained model allows us to optimally determine the LSP mode according to the given limitations of values and depth of residual stresses.