Prediction Of Mechanical Response Research Articles

A micromechanics-based artificial neural networks model for rapid prediction of mechanical response in short fiber reinforced rubber composites

A micromechanics-based artificial neural networks model for rapid prediction of mechanical response in short fiber reinforced rubber composites

Tensile performance prediction of CFRPs with voids using multiscale analysis and neural networks

The overall work provides a novel multiscale modeling strategy applicable to the mechanical response prediction of arbitrarily laid porous laminates under uniaxial tension by establishing representative defective volumes (RDVs) for the inter-ply and intra-ply regions. A representative defective volume modeling method based on void characterization results using X-ray computed tomography for the interlaminar region was proposed. Elastic properties, tensile strength, and fracture energies of porous and poreless microscale models were obtained and used in the macroscale model. Defective properties were assigned to partial elements in the macroscale model according to the preset distribution mode. The Extended Finite Element Method was taken to simulate the failure process. Tensile properties of porous laminates with stacking sequences of [90]8, [90/0/90/0]2S, and [45/90/-45/0]2S were experimentally tested and numerically simulated. The predicted tensile properties and crack propagation behavior coincide well with the test results. Besides, the effects of micro-voids and macroscopic void distribution were discussed in detail. Further, a back-propagation neural network was used to achieve a fast prediction of the tensile properties and save computational costs. The computation time (<1 s) greatly improved compared with multiscale modeling (>38 h). This work provides an efficient tool for composite performance prediction, design, and optimization.

A novel multi-scale modeling strategy based on variational asymptotic method for predicting the static and dynamic performance of composite sandwich structures

To establish a universal and convenient mathematical model for predicting static and dynamic performance of composite sandwich structures, a novel three-dimensional equivalent homogenized model (3D-EHM) is proposed based on variational asymptotic method. The multiscale mechanical analysis of composite hexagonal and re-entrant honeycomb sandwich structures is conducted by 3D-EHM, enabling reasonable transmission of mechanical information at different scales and facilitating accurate predictions of mechanical responses in sandwich structures. The comparison analysis with 3D printing experimental results and 3D finite element model results demonstrates that the 3D-EHM exhibits remarkable precision and computational efficiency in forecasting static displacement distributions and higher-order vibration modes, eliminating the need for time-consuming and expensive experiments. Moreover, the effects of the ply angle of the face sheet and core geometric parameters on the effective properties of composite hexagonal honeycomb sandwich structures are systematically investigated. In summary, 3D-EHM is an effective applied mathematical model for designing and analyzing composite sandwich structures, fully combining the systematicity of variational methods and the asymptotic convergence of asymptotic methods.

Advanced Computational Analysis of Cobalt-Based Superalloys through Crystal Plasticity.

This study introduces an advanced computational method aimed at accelerating continuum-scale processes using crystal plasticity approaches to predict mechanical responses in cobalt-based superalloys. The framework integrates two levels, namely, sub-grain and homogenized, at the meso-scale through crystal plasticity finite element (CPFE) platforms. The model is applicable across a temperature range from room temperature up to 900 °C, accommodating various dislocation mechanisms in the microstructure. The sub-grain level explicitly incorporates precipitates and employs a dislocation density-based constitutive model that is size-dependent. In contrast, the homogenized level utilizes an activation energy-based constitutive model, implicitly representing the γ' phase for efficiency in computations. This level considers the effects of composition and morphology on mechanical properties, demonstrating the potential for cobalt-based superalloys to rival nickel-based superalloys. The study aims to investigate the impacts of elements including tungsten, tantalum, titanium, and chromium through the homogenized constitutive model. The model accounts for the locking mechanism to address the cross-slip of screw dislocations at lower temperatures as well as the glide and climb mechanism to simulate diffusions at higher temperatures. The model's validity is established across diverse compositions and morphologies, as well as various temperatures, through comparison with experimental data. This advanced computational framework not only enables accurate predictions of mechanical responses in cobalt-based superalloys across a wide temperature range, but also provides valuable insights into the design and optimization of these materials for high-temperature applications.

Prediction of Mechanical Response of Nickel based Superalloy Subjected to Creep Fatigue Interaction Loading using Unified Mechanics Theory

In order to simulate and predict material's real-time responses for a component under complex mechanical and thermal loads, continuum damage mechanics (CDM) is employed. However, majority of the models found in the literature are phenomenological and primarily based on curve fitting, which offer limited understanding of the underlying physics of the problem. A few physics-based models have been developed that provide greater insights. Unified mechanics theory (UMT) is one such approach that captures entropy generation due to various dissipative mechanism which aims to explain the physics of the problem. During hold time in strain-controlled creep-fatigue interaction loading, stress relaxation is observed. This study attempts to capture stress relaxation response due to creep-fatigue interaction of nickel-based superalloys using UMT, which is regarded as a more scientific method than simply fitting curves. The evolution of creep strain energy with hold time is used to understand how material ages over time due to stress relaxation during creep-fatigue interaction loading.

A methodology for stress-strain behavior characterization and mixture optimization of recycled aggregate concrete based on machine learning

The characterization of stress-strain behaviors of recycled aggregate concrete (RAC) excluding physical properties of recycled aggregates (RAs) may result in an inaccurate prediction of mechanical responses in practical applications. In this study, a data-driven model using a refined long short-term memory (LSTM) network is established based on the Bayesian optimization algorithm, with the motivation to accurately predict the uniaxial compressive stress-strain behaviors of RAC, including the stress-strain relation, elastic modulus, peak stress, and the peak strain. Training and testing of the proposed model require the integration of the mixture content and the fundamental physical properties of RAs with the stress-strain relation of ordinary concrete featured by prominent sequential attributes. Upon a dataset containing 100 experimental samples from independent studies, covering a wide range of RA substitution rates, the superior prediction capability of the proposed LSTM network is demonstrated in comparison with the analytical results of three empirical mechanics-driven models. Finally, the trained LSTM network is further employed to optimize the mixture for RAC using the Bayesian optimization technique innovatively, to achieve a balance between the mechanical performance and requirement to the quality of RAs. This study constitutes a beneficial addition to the understanding of the RAs-dependent stress-strain behaviors of RAC from the perspective of the data paradigm and provides a practical tool for the optimal design of RAC materials and structures.

Comprehensively characterizing heterogeneous and transversely isotropic properties of femur cortical bones

Comprehensive characterization of the transversely isotropic mechanical properties of long bones along both the longitudinal and circumferential gradients is crucial for developing accurate mathematical models and studying bone biomechanics. In addition, mechanical testing to derive elastic, plastic, and failure properties of bones is essential for modeling plastic deformation and failure of bones. To achieve these, we machined a total of 336 cortical specimens, including 168 transverse and 168 longitudinal specimens, from four different quadrants of seven different sections of 3 bovine femurs. We conducted three-point bending tests of these specimens at a loading rate of 0.02 mm/s. Young’s modulus, yield stress, tangential modulus, and effective plastic strain for each specimen were derived from correction equations based on classical beam theory. Our statistical analysis reveals that the longitudinal gradient has a significant effect on the Young’s modulus, yield stress, and tangential modulus of both longitudinal and transverse specimens, whereas the circumferential gradient significantly influences the Young’s modulus, yield stress, and tangential modulus of transverse specimens only. The differences in Young’s modulus and yield stress between longitudinal specimens from different sections are greater than 40%, whereas those between transverse specimens are approximately 30%. The Young’s modulus and yield stress of transverse specimens in the anterior quadrant were 18.81%/15.46% and 18.34%/14.88% higher than those in the posterior and lateral quadrants, respectively. There is no significant interaction between the longitudinal gradient and the circumferential gradient. Considering the transverse isotropy, it is crucial to consider loading direction when investigating the impact of circumferential gradients in the anterior, lateral, medial, and posterior directions. Our findings indicate that the conventional assumption of homogeneity in simulating the cortical bone of long bones may have limitations, and researchers should consider the anatomical position and loading direction of femur specimens for precise prediction of mechanical responses.

Prediction of mechanical response of hexagonal honeycomb SPS blast wall under explosive loading: In-depth review and empirical formula

Despite comprehensive prevention strategies, offshore operations consistently grapple with health, safety, and environmental (HSE) challenges. Though explosions on offshore structures are rare, their devastating impact cannot be understated. Blast walls, functioning as pivotal passive barriers, play a crucial role in protecting the topsides of these structures by dissipating explosive energy. Sandwich panel structures (SPS), with their lightweight attributes, enhanced bending rigidity and exceptional energy absorption, are a cost-effective countermeasure. This study introduces a groundbreaking design guideline, anchored in the LS-Dyna nonlinear finite-element method (NLFEM), tailored for hexagonal SPS offshore blast walls under explosive strains. Our rigorous numerical simulations, encompassing 450 varied scenarios, meticulously evaluated an array of geometric configurations and explosive strengths (spanning from 1 kg to 3 kg of TNT). From these simulations, crucial parameters affecting maximum mid-span plate deflection were discerned. In addition to these simulations, our research offers in-depth technical reviews, shedding light on the nuances of the subject. An empirical formula was subsequently formulated to anticipate this deflection, corroborating its efficacy by FE simulation outcomes. This work provides invaluable guidance for refining early-stage offshore blast wall design.

An electroplastic constitutive model of fcc metals and their alloys under high current density

The mechanical behavior of fcc (face center cubic) metals and their alloys such as copper-based and aluminum-based materials changes obviously under high current density, but there is a lack of suitable constitutive model to describe the electroplastic behavior of these materials, which limits the accuracy of the mechanical response prediction in applications, such as electromagnetic launch. In this paper, a novel nonlinear constitutive model for electroplastic behavior under the high current density of fcc metals and their alloys is proposed. The model proposed is based on the theory of dislocation evolution, energy transfer theory and electron transport theory and takes into account the effects of high current density on dislocation density, strain rate and resistivity. Considering the evolution of forward and reverse dislocations caused by pulsed current, the relationship between the evolution rate of reverse dislocation and strain is established. Due to the sensitivity of the electroplastic effect to strain rate, the strain rate under electric current is obtained by combining the theory of dislocation thermal activation and energy transfer. According to the concept of thermal resistance and dislocation resistance, the relationship between resistance and current density is derived. The quantitative results demonstrate that this model can well capture the flow stress softening under high current density (>3000 A/mm2) and the transient hardening after current removal because of the consideration of a more suitable strain rate and dislocation evolution. The critical current density that has a significant effect on the strain rate and the current density corresponding to the subsection point of the resistivity are quantitatively obtained. Especially, it is shown that the evolution rate of the reverse dislocation decreases nonlinearly with the increase of strain after the current is removed. These results will be helpful to study the mechanical behavior of the rail during electromagnetic launch and further optimize the design of electromagnetic launch device.

Dynamic mechanical response prediction model of honeycomb structure based on machine learning method and finite element method

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 design parameters of the honeycomb cells in the ML model. A pixelization method of FE model was proposed to separate the complete cell structure from the honeycomb FE grid data 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). The crushing velocity and the extracted latent features were used as the input of the long-short term memory (LSTM) network to predict the crushing deformation and stress-strain curve of the honeycomb structure under different 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. The mechanical response prediction model provided a method for rapidly evaluating the dynamic mechanical response behavior of similar periodic array structures using FE model data, which could be beneficial for the design and development of equipment based on bionic structures.

Investigating effective yield surface of porous metals exhibit different hardening behaviors by cell models with randomly distributed spherical voids

AbstractThis study focuses on the development of effective yield surface of porous metals, which is characterized with the two constitutive parameters (i.e., q1 and q2) in the widely used Gurson–Tvergaard–Needleman (GTN) model. It was found that the influence of q1 and q2 on the mechanical responses may be masked by adjusting the two failure parameters (fc and fF), yielding a set of GTN parameters deviated from the actual situation and leading to the errors in failure predictions. Therefore, the two constitutive parameters should be carefully calibrated according to the hardening behavior of matrix. Further investigations on the void density and distributions proved that cubic cells containing 100 randomly distributed non‐intersecting spherical voids are sufficient in characterizing the homogenization. On this basis, effective yield surface of porous metals with a wide range of void volume fractions (from 0.4% to 20%) was investigated, and a correlation between the constitutive parameters and the power‐law hardening behavior of matrix was established. Finally, experimental verifications were conducted on low alloy steel SA508, indicating the q1 and q2 from this study can yield more accurate mechanical response predictions then the previously suggested values.

Simulation of Mechanical Response in Machining of Ti-6Al-4V Based on Finite Element Model and Visco-Plastic Self-Consistent Model

The predictions of mechanical responses (stress–strain variations) in the machining of Ti-6Al-4V alloy are important to analyze the deformation conditions of machining to optimize the machining parameters and investigate the generation of a machined surface. The selection of a constitutive model is an essential factor that determines the deformation behavior in the machining simulation model. In this paper, two constitutive models of a modified Johnson–Cook (JC) equation and visco-plastic self-consistent (VPSC) model were used to investigate the stress–strain evolutions in the machining process of Ti-6Al-4V. A finite element (FE) machining model was established, considering the influences of grain refinement and deformation twins, based on a modified JC equation. The VPSC model was fitted based on the macro-strain rate sensitivity of the JC equation. The prediction results of the stress–strain curves of two models were compared, and their validities were further proved. The results show that flow stress hardening and inhomogeneities are caused by multi-scale grain refinement during the machining process of Ti-6Al-4V. Five slip deformation modes and one compressive twinning mode were activated in the VPSC model to be consistent with the macro-deformation behavior predicted with the FE model. The validations show the effectiveness of the modified JC equation, considering microstructural changes and the fitted VPSC model, in predicting dynamic behavior in the machining process of Ti-6Al-4V. The results provide two aspects of macro-deformation and polycrystal plasticity to elucidate the stress variations that occur during the machining of Ti-6Al-4V.

Correlating atomistic characteristics of zeolites to their 3D-Printed Macro structural properties for prediction of mechanical response

A wide range of mechanical properties are vital in structures, from macro (e.g., load-bearing) down to atomistic (nanomaterial) level. To design structures with the target mechanical properties, it is crucial to understand the correlation between the mechanical characteristics and structural information. To this end, we explored the similarity in the mechanical behavior between atomistic structures and actual 3D-printed zeolite structures. The zeolite structure was chosen because of its various structural parameters such as pore size, distribution, and geometry. Molecular dynamics (MD) simulations confirmed that similar behavior was observed in the mechanical responses at an atomic scale and with a 3D-printed macro-scale structure. 3D printing with ductile thermoplastic polyurethane (TPU) filaments showed a high degree of agreement with microstructure-level simulations. The mechanical response of zeolite structures is classified depending on their linearity and the characteristics with respect to the applied strain, to anticipate the potential applications of mechanical metamaterials. Further comparative analysis was conducted between the structural characteristics and mechanical properties, linking the changes in the stress to factors, such as density, porosity, angle, and bond length. This study demonstrates that metamaterial design with a mechanical response can be achieved using atomic-level structural design degrees of freedom.

RESEARCH ON ONLINE PREDICTION OF SOFT TISSUE MECHANICAL RESPONSE BASED ON GREY MODEL

Soft tissue is an important operation object in robot-assisted surgery and its mechanical response is of great significance to the precision of surgical operation. Accurate prediction of soft tissue mechanical properties can effectively avoid the potential damage of biological tissue caused by excessive operating force. In this paper, three typical mechanical responses of soft tissue were obtained by no-slip compression experiments of liver tissue. Second, the prediction model of soft tissue mechanical response based on gray prediction was established, and the influence of key parameters of the model on the precision of mechanical response prediction was analyzed. The results show that the gray prediction model can accurately predict the mechanical response of soft tissue, and the prediction accuracy is the highest when the number of historical data is 7. The prediction method of soft tissue mechanical response proposed in this paper will provide important data reference for accurate operation of surgery.

Highly efficient and accurate algorithm for multiscale equivalent modeling and mechanical performance simulation of printed circuit boards

The increasing prevalence of reliability issues related to printed circuit boards (PCBs) has led to significant interest in applying finite element analysis (FEA) for PCBs. This study presents an equivalent property algorithm based on the mesoscale finite element method (FEM), which took into account the intricate structural features of conductive layers in PCBs. Additionally, a series of automated scripts were developed to streamline the modeling and calculating process, markedly enhancing the accuracy and efficiency of equivalent modeling and simulation of the PCB. Based on the proposed algorithm, a multiscale three-point bending finite element model of a PCB was established. By comparing the results to experimental data from three-point bending tests, it was demonstrated that the stress field simulated using the proposed algorithm accurately represented the anisotropic properties of the intricate copper traces in the conductive layers, leading to more accurate predictions of mechanical responses than those obtained using conventional algorithms. Furthermore, the research on the impact of partition size on both simulation accuracy and computational efficiency clearly demonstrated that partition size significantly affected these aspects. In conclusion, the proposed algorithm provided a more reliable and efficient approach for applying FEA and FEM to PCBs, contributing to advancements in the field of PCB analysis and fostering improvements in electronics design and manufacturing.

Thermo-mechanical analysis of porous functionally graded graphene reinforced cylindrical panels using an improved third order shear deformable model

In this paper, a novel shear deformable model is developed to investigate the temperature-dependent thermo-mechanical responses of porous functionally graded graphene reinforced composite (FG-GPLRC) cylindrical panels subjected to an initial blast pressure. An improved third order shear deformation theory, which has more rigorous kinematic of displacement, is implemented in the construction of the proposed model. Then, the current model is used to predict the thermo-mechanical responses of the nanocomposite panels made of metal foams reinforced by graphene platelets (GPLs). Lagrange equations are used to derive the governing equations corresponding to thermal buckling as well as the free and forced vibrations of the porous FG-GPLRC cylindrical panels. A Navier-type solution, which is free from computational errors, is implemented. Validation works demonstrate that the present model provides accurate predictions of mechanical responses for composite structures. A comprehensive parameter study is carried out to explore the influences of several key parameters regarding the thermo-mechanical behaviors of the porous FG-GPLRC cylindrical panels.

Fast prediction of the mechanical response for layered pavement under instantaneous large impact based on random forest regression

The layered pavements usually exhibit complicated mechanical properties with the effect of complex material properties under external environment. In some cases, such as launching missiles or rockets, layered pavements are required to bear large impulse load. However, traditional methods cannot non-destructively and quickly detect the internal structural of pavements. Thus, accurate and fast prediction of the mechanical properties of layered pavements is of great importance and necessity. In recent years, machine learning has shown great superiority in solving nonlinear problems. In this work, we present a method of predicting the maximum deflection and damage factor of layered pavements under instantaneous large impact based on random forest regression with the deflection basin parameters obtained from falling weight deflection testing. The regression coefficient R 2 of testing datasets are above 0.94 in the process of predicting the elastic moduli of structural layers and mechanical responses, which indicates that the prediction results have great consistency with finite element simulation results. This paper provides a novel method for fast and accurate prediction of pavement mechanical responses under instantaneous large impact load using partial structural parameters of pavements, and has application potential in non-destructive evaluation of pavement structure.

Mechanical Response Prediction Model of Honeycomb Structures Under Dynamic Crushing

Mechanical Response Prediction Model of Honeycomb Structures Under Dynamic Crushing

Exploring electromechanical utility of GaAs interdigitated transducers; using finite-element-method-based parametric analysis and experimental comparison

Analysis of interdigitated transducers often relies on phenomenological models to approximate device electrical performance. While these approaches prove essential for signal processing applications, phenomenological models provide limited information on the device’s mechanical response and physical characteristics of the generated acoustic field. Finite element method modeling, in comparison, offers a robust platform to study the effects of the full device geometry on critical performance parameters of interdigitated transducer devices. In this study, we fabricate a surface acoustic wave resonator on semi-insulating GaAs (100), which consists of an interdigitated transducer and acoustic mirror assembly. The device is subsequently modeled using fem software. A vector network analyzer is used to measure the experimental device scattering response, which compares well with the simulated results. The wave characteristics of the experimental device are measured by contact-mode atomic force microscopy, which validates the simulation’s mechanical response predictions. We further show that a computational parametric analysis can be used to optimize device designs for series resonance frequency, effective coupling coefficient, quality factor, and maximum acoustic surface displacement.

Prediction of Mechanical Responses of a Uniaxially Stretched Neural Fiber Bundle: Theoretical Approach for a Traumatic Loading Condition

Abstract Viscoelasticity of the spinal nerve roots may play a significant role in predicting nerve root damage caused by overall spinal motion. However, only a few studies have investigated the complex mechanical behavior of this tissue. The current study presents a theoretical protocol for predicting mechanical responses of soft biological materials, and this method was used to a uniaxially stretched neural fiber bundle isolated from porcine spinal nerve roots with various loading configurations. Stress relaxation tests were performed to systematically determine a set of parameters dictating the stress decaying process, i.e., a set of relaxation moduli and the corresponding time constants. Based on the obtained experimental and numerical test data, it was confirmed that the proposed method is effective even for the prediction of mechanical response to a cyclic stretch immediately after the ramp-hold test. In addition, an elastic response, i.e., a stress–strain relationship under a high-rate loading regime, was determined analytically. The results demonstrated that instantaneous mechanical responses of neural fiber bundles can be stiffened against very rapid stretch (&amp;gt;10 s−1); however, the fibers are relatively insensitive to moderate loading rates (&amp;lt;1 s−1). The ultimate tensile strength was estimated to be approximately 8 MPa at the structural failure strain (15%). This information will enable the computational assessment of traumatic nerve root injuries sustained during traffic accidents and contact sports.