Study on impact resistance and parameter optimization of patch‐repaired plain woven composite based on multi‐scale analysis

Optimization for maximizing the impact-resistance of patch repaired CFRP laminates using a surrogate-based model

A novel multiscale modeling strategy of the low-velocity impact behavior of plain woven composites

Temperature-dependent thermal expansion behaviors of carbon fiber/epoxy plain woven composites: Experimental and numerical studies

Probabilistic finite element predictions of the human lower limb model in total knee replacement

This contribution investigates the application of surrogate modeling, specifically Kriging and Response Surface Methodology (RSM), in optimizing injection molding simulation processes. Injection molding simulations are computationally intensive due to the multi-physical interactions involved, making surrogate models an attractive alternative to reduce computational effort. The authors employ Kriging and RSM models to simulate the injection molding of three different geometrical configurations, assessing their performance in predicting parameters for process quality/efficiency like deformation, shrinkage, and cycle time. The study makes use of MATLABs fmincon optimization algorithm with the models, emphasizing cycle time minimization while maintaining deformation and shrinkage within acceptable limits. In addition to the prediction accuracy, this contribution demonstrates that both surrogate models reduce the computational cost per evaluation by several orders of magnitude compared to full injection molding simulations and provide fast, iterative optimization. Findings indicate that Kriging outperforms RSM, especially in complex geometries, by providing more accurate predictions with lower error rates. The validation against digital twin simulations supports the effectiveness of Kriging, making it preferable for applications requiring high precision in process optimization.

Testing and multiscale modeling of a foam-based lung surrogate subjected to underwater shock loading

Investigation of electromechanical properties of piezoelectric structural fiber composites with micromechanics analysis and finite element modeling

A two-stage molecular dynamics (MD)-finite element (FE) modeling method is developed based on the concepts of representative volume element (RVE) and equivalent solid fibers (ESFs) containing functionalized carbon nanotubes (ESFs-fCNTs). First, the influences of nanotubes’ chirality, different percent of functionalization (), various functional atoms, and polymers on the tensile and shear properties of the fCNTs inserted into the polymer matrix (fCNTs/polymer) are discovered using MD simulations. Then, using MD information as input data, the effective Young’s modulus of polymeric unit cell strengthened by ESFs-fCNTs (ESFs-fCNTs/polymer) is explored through FE modeling. The ratio of effective Young’s modulus of the unit cell () to Young’s modulus of the polymeric cube () is reported and all findings () are compared to the ESFs-pure CNTs/polymer results as well. It is found that longitudinal Young’s modulus () of nanofillers/polymer RVEs affects remarkably the of the ESFs-nanofillers/polymer nanocomposites. The decreases by increasing the Generally, the reinforcing impact of zigzag nanotubes compared to armchair ones on the of polymer RVEs is more considerable. Additionally, FE-based results illustrate that as the volume fraction of ESFs () increases, the is enhanced. At a specific the reinforcing effect of the ESFs-armchair and zigzag fCNTs is more in favor of polyethylene nanocomposites than that of the polypropylene systems.

Multi-scale approach based constitutive modelling of plain woven textile composites

Carbon fiber-reinforced polymer (CFRP) laminates, a key composite material, are widely used in aircraft structures and are susceptible to low-velocity impact (LVI) damage from bird strikes, lightning strikes, hail impacts and other situations. Therefore, finding a method that repairs the damaged structure and detects the effect of these repairs under LVI is a very important goal. In this work, the repair effect of LVI damage in CFRP laminates repaired with patches of various sizes is investigated via experimental and numerical nonlinear Lamb wave analyses. An integrated numerical procedure that combines LVI with nonlinear Lamb wave detection is developed to predict the nonlinear Lamb wave behavior in LVI-damaged patch-repaired CFRP laminates. The CFRP laminate damage in the nonlinear Lamb wave simulation is evaluated based on relative acoustic nonlinearity parameters (RANPs). As a result, the integrated numerical procedure is validated with drop-weight impact tests and RAM-5000 SNAP nonlinear ultrasonic detection system. An optimal patch design is established via interpolation to optimize the absorbed energy, delamination surface area, second RANP and third RANP with different patch repair sizes. These parameters exhibit consistent curve fitting trends, indicating that they can be used as important indicators of impact damage. The optimal circular patch design with a radius of 2.5 r has better impact resistance behavior and repair performance.

Plastic-metal joints with a laser-structured metal surface have a high potential to reduce cost and weight compared to conventional joining technologies. However, their application is currently inhibited due to the absence of simulation methods and models for mechanical design. Thus, this paper presents a model-based approach for the strength estimation of laser-based plastic-metal joints. The approach aims to provide a methodology for the efficient creation of surrogate models, which can capture the influence of the microstructure parameters on the joint strength. A parametrization rule for the shape of the microstructure is developed using microsection analysis. Then, a parameterized finite element (FE) model of the joining zone on micro level is developed. Different statistical plans and model fits are tested, and the predicted strength of the FE model and the surrogate models are compared against experiments for different microstructure geometries. The joint strength is predicted by the FE model with a 3.7% error. Surrogate modelling using half-factorial experimental design and linear regression shows the best accuracy (6.2% error). This surrogate model can be efficiently created as only 16 samples are required. Furthermore, the surrogate model is provided as an equation, offering the designer a convenient tool to estimate parameter sensitivities.

In this paper, a finite element model updating (FEMU) method is proposed based on the response surface model (RSM) and genetic algorithm (GA) to establish a high-precision finite element (FE) model of space station scientific experiment racks. First, the fine solid and mixed FE models are established, respectively, and a comparison of the modal test results is conducted. Then, an orthogonal experimental design is used to analyze the significance of the parameters, and the variables to be modified are determined. The design parameters are sampled via the Latin hyperbolic method and are substituted into the FE model to obtain the modal parameters of the scientific experiment rack. The mapping relationship between the design and modal parameters is fitted by constructing the Kriging function, and the RSM is established. The design parameters of the scientific experiment rack are optimized via GA, and the initial FE model is updated, which has the advantage of improving the computing efficiency. Finally, the updated FE model of the experiment rack is verified by frequency sweep and random vibration tests. The experimental results show that the proposed approach has high precision and computing efficiency, and compared with the test results, the modal frequency errors of the updated model are within 5%, and the vibration response errors under random excitation of the updated model are within 7%.

A hierarchical multiscale off-road mobility model is enhanced through the development of an artificial neural network (ANN) surrogate model that captures the complex material behavior of deformable terrain. By exploiting the learning capability of neural networks, the incremental stress and strain relationship of granular terrain is predicted by the ANN representative volume elements (RVE) at various states of the stress and strain. A systematic training procedure for ANN RVEs is developed with a virtual tire test rig model for producing training data from the discrete-element (DE) RVEs without relying on computationally intensive full vehicle simulations on deformable terrain. The ANN surrogate RVEs are then integrated into the hierarchical multiscale computational framework as a lower-scale model with the scalable parallel computing capability, while the macroscale terrain deformation is described by the finite element (FE) approach. It is demonstrated with several numerical examples that off-road vehicle mobility performances predicted by the proposed FE-ANN multiscale terrain model are in good agreement with those of the FE-DE multiscale model while achieving a substantial computational time reduction. The accuracy and robustness of the ANN RVE for fine-grain sand terrain are discussed for scenarios not considered in training datasets. Furthermore, a drawbar pull test simulation is presented with the ANN RVE developed with data in the cornering scenario and validated against the full-scale vehicle test data. The numerical results confirm the predictive ability of the FE-ANN multiscale terrain model for off-road mobility simulations.

Numerical modelling of damage susceptibility of an inhomogeneous representative material volume element of polymer composites

The first and foremost aim with this book is to serve as a guide and reference for engineers that want to use surrogate modeling techniques when they design or develop products, systems and components. Additionally, the book constitutes part of the literature for the courses in Design Optimization and Multidisciplinary Design Optimization at Linköping University. It is the intention of the author that the content should serve as an easily understood introduction to surrogate modeling. The more advanced details are briefy mentioned but can be found in the scientifc papers referenced in this book. There exist numerous books and review articles regarding surrogate modeling, e.g. Forrester, Sóbester, and Keane, 2008 and Krawczyk and Arabas, 2025, which means that all methods presented here can be found elsewhere. The two main advantages of this book are Many methods are exemplifed using numerical examples Some practical guidelines recommended by the author are presented