Stress evaluation plays a pivotal role in the design of material systems, often accomplished through the finite element method (FEM) for intricate structures. However, the substantial costs and time requirements associated with multi-scale FEM analyses have prompted a growing interest in adopting more efficient, machine-learning-driven strategies. This study investigates the utilization of advanced machine learning techniques for predicting local stress fields in composite materials, presenting it as a superior alternative to traditional FEM approaches. The primary objective of this research is to develop a predictive model for stress field maps in composite components featuring diverse configurations of fibers distributed within the matrix. To achieve this, we employ a Convolutional Neural Network (CNN) with a specialized U-Net architecture, enabling the correlation of spatial fiber organization with the resultant von Mises stress field. The CNN model was extensively trained using four distinct data sets, encompassing uniform fibrous structures, non-uniform fibrous structures, irregularly shaped fibrous structures, and a comprehensive combination of these data sets. The trained U-Net models demonstrate exceptional proficiency in predicting von Mises stress fields, yielding impressive structural similarity index scores (SSIM) of 0.977 and mean squared errors (MSE) of 0.0009 on a dedicated test set. This research harnesses 2D cross-sectional imagery to establish a surrogate model for finite element analysis, offering an accurate and efficient approach for predicting stress fields in composite material design, irrespective of geometric complexity or boundary conditions.

In this study, a finite element (FE)-based machine learning model was developed to predict the mechanical properties of bioglass (BG)-collagen (COL) composite hydrogels. Based on the experimental observation of BG-COL composite hydrogels with scanning electron microscope, 2000 microstructural images with randomly distributed BG particles were created. The BG particles have diameters ranging from 0.5 µm to 1.5 µm and a volume fraction from 17% to 59%. FE simulations of tensile testing were performed for calculating the Young’s modulus and Poisson’s ratio of 2000 microstructures. The microstructural images and the calculated Young’s modulus and Poisson’s ratio by FE simulation were used for training and testing a convolutional neural network regression model. Results showed that the network developed in this work can effectively predict the mechanical properties of the composite hydrogels. The R-squared values were 95% and 83% for Young’s modulus and Poisson’s ratio, respectively. This work provides a surrogate model of finite element analysis to predict mechanical properties of BG-COL hydrogel using microstructure images, which could be further utilized for characterizing heterogeneous materials in big data-driven material designs.

Injection molding is one of the most important processes for the mass production of plastic parts. In recent years, many researchers have focused on predicting the occurrence and intensity of defects in injected molded parts, as well as the optimization of process parameters to avoid such defects. One of the most frequent defects of manufactured parts is blush, which usually occurs around the gate location. In this study, to identify the effective parameters on blush formation, eight design parameters with effect probability on the influence of this defect have been investigated. Using a combination of design of experiments (DOE), finite element analysis (FEA), and ANOVA, the most significant parameters have been identified (runner diameter, holding pressure, flow rate, and melt temperature). Furthermore, to provide an efficient predictive model, machine learning methods such as basic artificial neural networks, their combination with genetic algorithms, and particle swarm optimization have been applied and their performance analyzed. It was found that the basic artificial neural network (ANN), with an average accuracy error of 1.3%, provides the closest predictions to the FEA results. Additionally, the process parameters were optimized using ANOVA and a genetic algorithm, which resulted in a significant reduction in the blush defect area.

Trauma to the eye resulting from a soccer ball is a common sports-related injury. Although the types of ocular pathologic features that result from impact have been documented, the underlying pathophysiologic mechanics are not as well studied. The purpose of this study was to evaluate the biomechanical events after the collision of a soccer ball with the eye to better understand the pathophysiology of observed ocular and retinal injuries and to compare them with those observed in abusive head trauma (AHT). Computer simulation study. None. A finite element model of the eye was used to investigate the effects of a collision of a soccer ball on the eye. Intraocular pressure and stress. Impact of the soccer ball with the eye generated a pressure wave that traveled through the vitreous, creating transient pockets of high and negative pressure. During the high-frequency phase, pressure in the vitreous near the posterior pole ranged from 39.6 to –30.9 kPa. Stress in ocular tissue was greatest near the point of contact, with a peak of 66.6 kPa. The retina experienced the greatest stress at the vasculature, especially at distal branches, where stress rose to 15.4 kPa. On average, retinal stress was greatest in the subretinal layer, but was highest in the preretinal layer when considering only vascular tissue. The high intraocular pressure and stress in ocular tissue near the point of soccer ball impact suggest that injuries to the anterior segment of the eye can be attributed to direct transmission of force from the ball. The subsequent propagation of a pressure wave may cause injuries to the posterior segment as the positive and negative pressures exert compressive and tractional forces on the retina. The linear movement of the pressure wave likely accounts for localization of retinal lesions to the posterior pole or superior temporal quadrant. The primarily linear force in soccer ball trauma is the probable cause for the more localized injury profile and lower retinal hemorrhage incidence compared with AHT, in which repetitive angular force is also at play.

Conducting computational stress-strain analysis using finite element methods (FEM) is a common approach when dealing with the complex geometries of atherosclerosis, which is a leading cause of global mortality and complex cardiovascular disease. The considerable expense linked to FEM analysis encourages the substitution of FEM with a considerably faster data-driven machine learning (ML) approach. This study investigated the potential of end-to-end deep learning tools as a more effective substitute for FEM in predicting stress-strain fields within 2D cross sections of arterial walls. We first proposed a U-Net-based fully convolutional neural network (CNN) to predict the von Mises stress and strain distribution based on the spatial arrangement of calcification within arterial wall cross-sections. Further, we developed a conditional generative adversarial network (cGAN) to enhance, particularly from the perceptual perspective, the prediction accuracy of stress and strain field maps for arterial walls with various calcification quantities and spatial configurations. On top of U-Net and cGAN, we also proposed their ensemble approaches to improve the prediction accuracy of field maps further. Our dataset, consisting of input and output images, was generated by implementing boundary conditions and extracting stress-strain field maps. The trained U-Net models can accurately predict von Mises stress and strain fields, with structural similarity index scores (SSIM) of 0.854 and 0.830 and mean squared errors of 0.017 and 0.018 for stress and strain, respectively, on a reserved test set. Meanwhile, the cGAN models in a combination of ensemble and transfer learning techniques demonstrate high accuracy in predicting von Mises stress and strain fields, as evidenced by SSIM scores of 0.890 for stress and 0.803 for strain. Additionally, mean squared errors of 0.008 for stress and 0.017 for strain further support the model’s performance on a designated test set. Overall, this study developed a surrogate model for finite element analysis, which can accurately and efficiently predict stress-strain fields of arterial walls regardless of complex geometries and boundary conditions.

Purpose: Various types of trauma can cause retinal hemorrhages in children, including accidental and nonaccidental head trauma. We used animal eyes and a finite element model of the eye to examine stress patterns produced during purely linear and angular accelerations, along with stresses attained during simulated repetitive shaking of an infant.Methods: Using sheep and primate eyes, sclerotomy windows were created by removing the sclera, choroid, and retinal pigment epithelium to expose the retina. A nanofiber square was glued to a 5 mm2 area of retina. The square was pulled and separated from vitreous while force was measured. A finite element model of the pediatric eye was used to computationally measure tension stresses during shaking.Results: In both sheep and primate eyes, tension stress required for separation of retina from vitreous range from 1 to 5 kPa. Tension stress generated at the vitreoretinal interface predicted by the computer simulation ranged from 3 to 16 kPa during a cycle of shaking. Linear acceleration generated lower tension stress than angular acceleration. Angular acceleration generated maximal tension stress along the retinal vasculature. Linear acceleration produced more diffuse force distribution centered at the poster pole.Conclusions: The finite element model predicted that tension stress attained at the retina during forcible shaking of an eye can exceed the minimum threshold needed to produce vitreoretinal separation as measured in animal eyes. Furthermore, the results show that movements that involve significant angular acceleration produce strong stresses localized along the vasculature, whereas linear acceleration produces weaker, more diffuse stress centered towards the posterior pole of the eye.