Predicting the perforation limit of composite laminates is an important design aspect and is a complex task due to the multi-mode failure mechanism and complex material constitutive behaviour ...required. This requires high-fidelity numerical models for a better understanding of the physics of the perforation event. This work presents a numerical study on the perforation behaviour of a satin-weave S2-glass/epoxy composite subjected to low-velocity impact. A novel strain-rate-dependent finite-discrete element model (FDEM) is presented and validated by comparison with experimental data for impacts at several energies higher and lower than their perforation limit. The strain rate sensitivity was included in the model by developing a novel user-defined material model, which had a rate-dependent bilinear traction separation cohesive behaviour, implemented using a VUSDFLD subroutine in Abaqus/Explicit. The capability of the model in predicting the perforation limit of the composite was investigated by developing rate-sensitive and insensitive models. The results showed that taking the strain rate into account leads to more accurate predictions of the perforation limit and damage morphology of the laminate subjected to impacts at different energies. The experimental penetration threshold of 89 J was estimated as 79 J by the strain-rate-sensitive models, which was more accurate compared to 52 J predicted by the strain-rate-insensitive model. Additionally, the coupling between interlaminar and intralaminar failure modes in the models led to a more accurate prediction of the delamination area when considering the rate sensitivity.
This paper presents an experimental and numerical investigation on the influence of pre-existing impact damage on the low-velocity impact response of Carbon Fiber Reinforced Polymer (CFRP). A ...continuum damage mechanics-based material model was developed by defining a user-defined material model in Abaqus/Explicit. The model employed the action plane strength of Puck for the damage initiation criterion together with a strain-based progressive damage model. Initial finite element simulations at the single-element level demonstrated the validity and capability of the damage model. More complex models were used to simulate tensile specimens, coupon specimens, and skin panels subjected to low-velocity impacts, being validated against experimental data at each stage. The effect of non-central impact location showed higher impact peak forces and bigger damage areas for impacts closer to panel boundaries. The presence of pre-existing damage close to the impact region leading to interfering delamination areas produced severe changes in the mechanical response, lowering the impact resistance on the panel for the second impact, while for non-interfering impacts, the results of the second impact were similar to the impact of a pristine specimen.
Manufacturing defects, such as porosity and inclusions, can significantly compromise the structural integrity and performance of additively manufactured parts by acting as stress concentrators and ...potential initiation sites for failure. This paper investigates the effects of pore system morphology (number of pores, total volume, volume fraction, and standard deviation of size of pores) on the material response of additively manufactured Ti6Al4V specimens under a shear–compression stress state. An automatic approach for finite element simulations, using the J2 plasticity model, was utilized on a shear–compression specimen with artificial pores of varying characteristics to generate the dataset. An artificial neural network (ANN) surrogate model was developed to predict peak force and failure displacement of specimens with different pore attributes. The ANN demonstrated effective prediction capabilities, offering insights into the importance of individual input variables on mechanical performance of additively manufactured parts. Additionally, a sensitivity analysis using the Garson equation was performed to identify the most influential parameters affecting the material’s behaviour. It was observed that materials with more uniform pore sizes exhibit better mechanical properties than those with a wider size distribution. Overall, the study contributes to a better understanding of the interplay between pore characteristics and material response, providing better defect-aware design and property–porosity linkage in additive manufacturing processes.
Abstract
This study presents a data-driven finite element-machine learning surrogate model for predicting the end-to-end full-field stress distribution and stress concentration around an ...arbitrary-shaped inclusion. This is important because the model’s capacity to handle large datasets, consider variations in size and shape, and accurately replicate stress fields makes it a valuable tool for studying how inclusion characteristics affect material performance. An automatized dataset generation method using finite element simulation is proposed, validated, and used for attaining a dataset with one thousand inclusion shapes motivated by experimental observations and their corresponding spatially-varying stress distributions. A U-Net-based convolutional neural network (CNN) is trained using the dataset, and its performance is evaluated through quantitative and qualitative comparisons. The dataset, consisting of these stress data arrays, is directly fed into the CNN model for training and evaluation. This approach bypasses the need for converting the stress data into image format, allowing for a more direct and efficient input representation for the CNN. The model was evaluated through a series of sensitivity analyses, focusing on the impact of dataset size and model resolution on accuracy and performance. The results demonstrated that increasing the dataset size significantly improved the model’s prediction accuracy, as indicated by the correlation values. Additionally, the investigation into the effect of model resolution revealed that higher resolutions led to better stress field predictions and reduced error. Overall, the surrogate model proved effective in accurately predicting the effective stress concentration in inclusions, showcasing its potential in practical applications requiring stress analysis such as structural engineering, material design, failure analysis, and multi-scale modeling.
Abstract This study investigates the application of machine learning models to predict time-evolving stress fields in complex three-dimensional structures trained with full-scale finite element ...simulation data. Two novel architectures, the multi-decoder CNN (MUDE-CNN) and the multiple encoder–decoder model with transfer learning (MTED-TL), were introduced to address the challenge of predicting the progressive and spatial evolutional of stress distributions around defects. The MUDE-CNN leveraged a shared encoder for simultaneous feature extraction and employed multiple decoders for distinct time frame predictions, while MTED-TL progressively transferred knowledge from one encoder–decoder block to another, thereby enhancing prediction accuracy through transfer learning. These models were evaluated to assess their accuracy, with a particular focus on predicting temporal stress fields around an additive manufacturing (AM)-induced isolated pore, as understanding such defects is crucial for assessing mechanical properties and structural integrity in materials and components fabricated via AM. The temporal model evaluation demonstrated MTED-TL’s consistent superiority over MUDE-CNN, owing to transfer learning’s advantageous initialization of weights and smooth loss curves. Furthermore, an autoregressive training framework was introduced to improve temporal predictions, consistently outperforming both MUDE-CNN and MTED-TL. By accurately predicting temporal stress fields around AM-induced defects, these models can enable real-time monitoring and proactive defect mitigation during the fabrication process. This capability ensures enhanced component quality and enhances the overall reliability of additively manufactured parts.
This paper studies the behavior of S2-glass woven fabric reinforced polymer composite under low-velocity impact at 18–110 J energy. A macro-homogeneous finite element model for the prediction of ...their response is implemented, considering the non-linear material behavior and intralaminar and interlaminar failure modes for the prediction of impact damage. The model accurately predicted the permanent indentation caused by impact. By applying the Ramberg-Osgood formulation, different initial stiffness values are examined to assess the post-impact unloading response. This approach reveals the significant role of initial stiffness in inelastic strain accumulation and its consequent effect on permanent indentation depth. A higher initial stiffness correlates with increased inelastic strain, influencing the impactor rebound and resulting in greater permanent indentation. By accurately predicting permanent indentation, and damage accumulation for different impact energies, this study contributes to a better understanding of the impact behavior of composite materials, thereby promoting their wider application.
Creating computationally efficient models that link processing methods, material structures, and properties is essential for the development of new materials. Translating microstructural details to ...macro-level mechanical properties often proves to be an arduous challenge. This paper introduces a novel deep learning-based framework to predict 3D material stress fields, mechanical behavior, and progressive damage in ceramic materials informed by the microstructural features of the material. We construct a dataset of synthetic representative volume elements utilizing X-ray computed tomography scans and employ an automated finite element (FE) modeling approach to generate datasets of alumina ceramics with varying inclusion morphologies. The deep learning model, a U-Net based convolutional neural network (CNN), is trained to understand the structure-property linkages and mechanical responses directly from FE-generated data without transforming them into image format. The CNN's architecture is optimized for capturing both local and global contextual information from the microstructural data, enabling accurate prediction of stress fields and damage evolution. Inclusions within the material are shown to play a crucial role in the initiation and propagation of damage. The CNN model demonstrated robust performance in predicting the stress field, stress-strain curve, and progressive damage curve, with training and test data both showing high and consistent similarity between predictions and the ground truth. Overall, this research offers a generalized approach that can be adapted for different materials and structures toward creating efficient and accurate digital replicas for optimizing material performance in real-world applications.
The objective of the current study is to numerically investigate the effect of repeated localized impulsive loading on the performance and dynamic plastic response of monolithic and multi-layered ...circular plate configurations made of either high- and low-strength aluminum or steel or a combination of these materials in the impulse range of 12.5–30 N s. For this, several numerical models were developed using ABAQUS/Explicit commercial FEM software via a FORTRAN subroutine VDLOAD in combination with the Johnson-Cook thermoviscoplastic constitutive relation, as well as the Johnson-Cook damage model. In order to validate the numerical models, the available experimental results on monolithic and multi-layered plate configurations under single localized load were used. Afterward, 18 different numerical models and 96 various cases including monolithic, double- and triple-layered plate configurations with an equivalent areal density subjected to five consecutive loads, were employed. The numerical simulation results indicated that a double-layered mixed configuration with a thin back steel layer and a thick front aluminum layer performs better compared to other configurations made of similar and dissimilar materials at higher impulses subjected to multiple impulsive loading, particularly while steel and aluminum materials have lower strength.
•Performance of monolithic and multi-layered metallic plates was investigated under multiple impulsive loading.•For similar materials, a monolithic plate exhibits a better performance compared to a multi-layered plate configuration.•At higher impulses, a double-layered mixed configuration exhibits the best performance compared to other configurations considered.•The best double-layered mixed configuration consisted of a thin back steel layer and a thick front aluminum layer.
This research focuses on comparing the two progressive damage models available in the explicit nonlinear finite element software LS-Dyna. To explore the prediction capabilities in terms of mechanical ...response and dominating failure modes in S2 glass woven composites, low velocity impact response at four different energies ranging from 27.9 J to 109.7 J were considered in this study. A macro-homogeneous solid element formulated finite element model was simulated to understand the response and failure mechanics in the laminate under low-velocity impact. The material modeling was carried out utilizing the MAT 55 and MAT 162 material models. An effort has been made for robust calibration of the various physical and non-physical parameters in both material cards for accurate predictions. The prediction capabilities of the models were then examined by comparing them against the experimental results, which fall within the deviation of ∼11%. The results show that MAT 162 yields a better resemblance with the damage morphology patterns and the delamination for the accounted impact zone, due to inclusion of strain-rate effect. Overall, this paper provides insight into the limitations and advantages of both material models, which establishes the route for the selection of the appropriate material model for simulating impact behavior in woven composites.
•Investigation on low-velocity impact behavior of S2 glass woven composites.•Calibration of the physical and non-physical parameters from two progressive damage models.•MAT 55 models estimated well for the performance of the laminate under lower impact energies.•MAT 162 showed better correlation at higher impact energies due to consideration of strain-rate effect.
The effect of polyurea coating on the structural response of metallic plates is still a challenge for the industry and scientific community. In this paper, experimental and numerical investigations ...were carried out to study the influence of polyurea coating as a strengthening layer on the dynamic plastic response and resistance of aluminum plates under gas detonation load. A single-stage Gas Detonation Forming (GDF) apparatus was used to conduct a number of experiments on rectangular aluminum plates with and without polyurea coating sprayed onto the rear side of the metallic plate. The residual deformations of polyurea-coated aluminum (PU–Al) plates were measured and compared with single-layer aluminum plates with the same areal density. It was found that the central permanent deflection of metallic plates decreased significantly with the use of coatings, and PU-Al configurations had superior performance compared to uncoated aluminum plates, which was more pronounced at higher total pre-detonation pressures. Additionally, an empirical equation based on dimensionless numbers was presented to predict the central permanent deflection of PU-Al configurations. Furthermore, the Response Surface Methodology (RSM) was used to establish a relationship between the output response and input independent parameters based on ANOVA results, and to understand the interaction between parameters. Eventually, a single-objective optimization analysis was carried out using the RS equation to find optimum ranges of input variables that can minimize the response. The optimization results were validated by conducting new experiments.
•The effect of polyurea as a strengthening layer on the resistance of aluminum plates under gas detonation load was studied.•A single-stage gas detonation forming apparatus was used to conduct experiments.•Response surface methodology was used to establish a relation between the output response and input independent parameters.•An empirical model was presented to predict the maximum deflections of polyurea-coated aluminum plates.•The optimization results were validated by conducting new experiments.
