Fused deposition modeling (FDM)-printed carbon fiber-reinforced polyetherketoneketone (CF/PEKK) composites represent promising lightweight structural materials for ultraviolet (UV)-exposed service environments. Nevertheless, their UV-induced degradation mechanisms and residual strength evolution remain poorly understood. Herein, FDM-printed CF/PEKK composites were subjected to UVA-340 accelerated aging for up to 480 h. Changes in mass, tensile strength, flexural strength, and interlaminar shear strength were quantified, and the underlying degradation mechanisms were investigated. After 480 h of UV exposure, average mass losses reached 0.0478, 0.0565, and 0.132 g cm-2 for tensile, flexural, and interlaminar shear specimens, with the corresponding strength reductions of 7.65%, 13.72%, and 18.12%, respectively. UV exposure induced surface defect accumulation, fiber exposure, and photo-oxidative degradation of the PEKK matrix, accompanied by the formation of oxygen-containing species and a decrease in crystallinity from 24.12% to 17.79%. Thermal stability decreased slightly, whereas the intrinsic thermal transition temperatures changed only marginally. A simplified residual strength model captured the monotonic degradation trends of all three mechanical properties under the present aging condition. These results indicate that UV damage is primarily surface-localized but sufficient to degrade interfacial integrity and mechanical properties, providing essential insights for durability assessment of FDM-printed CF/PEKK composites under UV exposure.

Thermomechanical fatigue is an intrinsically coupled dissipative process in which cyclic viscoplastic deformation, heat generation, temperature evolution, material degradation, and crack propagation interact throughout the loading history. To address this complexity, this work develops a variational phase field model for thermomechanical fatigue crack growth by extending the mixed variational formulation for coupled thermomechanical dissipative solids. In contrast to conventional approaches, which couple thermal, inelastic, and fracture processes through separately postulated phenomenological equations, the present formulation derives the evolution of all these fields consistently from a single mixed variational principle. The predictive capability of the proposed model is assessed through different numerical examples. The model is first validated against cyclic thermal-shock experiments on notched ring specimens, and subsequently against fatigue crack growth in CT and SENT specimens under various thermomechanical loading conditions. Finally, the model is utilized to predict fatigue crack growth in the solder layer of an IGBT module for power-electronics thermal management. The contributions of the energetic driving forces governing the TMF process are analyzed in detail. The simulation results demonstrate that the proposed phase field model can reasonably predict the TMF behavior of elasto-viscoplastic solids.

Surface protective coatings on magnesium alloys have been developed to control the corrosion rate of biomedical magnesium implants under mechano-chemical loadings. Quantifying the effect of coating's microstructural features on the corrosion behaviour of magnesium alloys facilitates the innovative design of biodegradable magnesium implants from the surface to the bulk. The present work is devoted to exploring the applicability of deep learning methods for efficiently predicting the in vitro pitting corrosion behaviour of coated magnesium alloys. To this end, the proposed machining learning method employs different CNN models for predicting the corrosion curve and the evolution of corrosion interfaces. In the proposed deep learning method, phase field simulations with varying coating microstructures are used to generate the required corrosion datasets for training and validating the models. The method is applied to a PEO coated WE43 magnesium alloy to assess its feasibility based on in vitro experiments. Performance analysis shows that the multi-input CNN is superior to the single-input CNN in predicting the corrosion curve. The proposed encoder-decoder architecture can predict the evolution of corrosion interfaces with an average error about 1%. These results demonstrate that the proposed CNN models provide a promising alternative to conventional simulation methods for evaluating the protective performance of coatings.

This study investigates the flexural behaviour of the laminated composite shells in the framework of Higher-Order Shear Deformation Theory (HSDT) and Peridynamic Differential Operator (PDDO), namely PD-HSDT, for the first time. Laminated composite shell structures are widely used in aerospace, automotive, and marine industries due to their high strength-to-weight ratio and design flexibility. Therefore, understanding their mechanical behavior under various loading conditions is crucial for ensuring structural reliability and performance optimization. However, such structures may possess complex curvatures and highly heterogenous laminate stackings, leading to inaccurate numerical stress analyses. The HSDT successfully captures displacement and stress distributions as well as cross-sectional warping through higher-order functions exist in the kinematics. Moreover, the PDDO represents the local derivatives in their nonlocal form, making it well-suited for problems involving higher-order derivatives and discontinuities. The governing equations and boundary conditions of the HSDT are solved by using the PDDO to accurately achieve the stress and displacement fields in the laminated composite shells. The robustness of the PD-HSDT is established by considering various loading and boundary conditions. The proposed approach demonstrates high accuracy in stress and displacement predictions when validated against reference solutions available in existing literature. This indicates strong potential for extending the methodology to more complex loading scenarios and damage mechanisms in future studies.

PURPOSE:Vertebral Body Tethering (VBT) is emerging as a promising approach for treating Adolescents with Idiopathic Scoliosis. This study aims to address the limited experimental research on vertebral body tethering by examining its biomechanical effects on the segmental spinal range of motion (ROM). METHODS:Six human spine samples (T10-L3) were subjected to pure moment testing under four different conditions: native, and instrumentation with single-tether (T10-L3), double-tether (T11-L3), and hybrid (T12-L2) techniques in flexion (FL) and extension (EX), lateral bending (LB), and axial rotation (AR). The intersegmental ROM was measured from sensors inserted in each vertebra using an electromagnetic tracking system. RESULTS:All instrumented cases preserved at least 80% of the native segmental ROM during FL-EX for all tested segments. In AR, all segments preserved at least 88% ROM mobility for single-tether and double-tether, or 65% for the hybrid technique. In LB, the ROM was reduced to 55% for a single-tether, 47% for a double-tether, and 29% for a hybrid system. The hybrid construct tended to relatively increase the ROM of adjacent levels near the titanium rod when compared with the single-tether or double-tether. CONCLUSION:This study provided experimental data on individual segment motion under VBT. The findings indicate that VBT techniques preserve a significant portion of FL-EX and AR ROM for all segments. However, the tested VBT constructs provide stability for the spine in LB.

Data diversity and quantity are crucial for training deep learning models. However, the impact of dataset diversity and size on biomechanical variable estimation models has not been explicitly investigated during drop landings. This work investigates the impact of the number of subjects and the number of trials per subject on the performance of wearable IMU-driven deep-learning models for knee moment and ground reaction force estimation during drop landing tasks. An investigation dataset with 16 subjects and 25 trials per subject was collected in a biomechanical laboratory. The impact of subject and trial quantification was explored under different model complexity and types, as well as data augmentation methods using the investigation dataset. The deeplearning models were implemented by a feature extractor and an estimator realized by several fully connected layers. The feature extractor was independently evaluated with fully-connected neural networks, convolutional neural network (CNN), long short-term memory (LSTM) model, and transformer model. Three transformation-based data augmentation methods were proposed and compared with the measured dataset. The results showed that the minimum required number of subjects and trials for the models to achieve an estimation performance of 0:85 of R-squared, 0:4 body weight × body height of RMSE, and 0:1 of rRMSE is five subjects and five trials. Intriguingly, adding more subjects to the dataset improved the estimation performance while adding more trials did not. Additionally, the proposed data augmentation can alleviate the data scarcity issue when the number of trials is small.

Silylation treatment improves the hydrophobicity of cellulose by reducing the number of hydroxyl groups in the cellulose chains that are available to react with moisture in the surrounding environment. Additionally, silylation increases stress transfer from cellulose to synthetic nanofillers by forming covalent bonds between the hydroxyl groups of cellulose and the oxidized surface of these nanofillers. This study investigates the impact of silane coupling agents on the tensile properties of cellulose nanocomposites. The cellulose nanocomposites are reinforced with four types of C/SiC-based nanofillers: carbon nanotubes, graphene nanoplatelets, silicon carbide nanotubes, and silicon carbide nanoplatelets. Subsequently, the nanofillers are subjected to surface treatment using the silane coupling agent KH550. The mechanical properties of the cellulose nanocomposites are evaluated by molecular dynamics simulations based on the polymer’s consistent forcefield. The results indicate that the reinforcements of silylated silicon carbide nanotubes and carbon nanotubes increase the tensile modulus of cellulose by 18.03% and 24.58%, respectively, compared to their untreated counterparts. Furthermore, the application of silylation treatment on the surface of C/SiC nanofillers increases the yield strength and ultimate tensile strength of cellulose nanocomposites due to enhanced load transfer between cellulose and these reinforcements.

Understanding nanoscale behavior in glasses is challenging due to their inherent long-range disorder and the absence of clearly defined defect regions. However, shear transformation zone theory suggests that certain “soft spots,” which are prone to plastic rearrangements, can be identified even in stress-free configurations. Various predictive methods, such as energy approximations and local structural indicators, have been proposed with varying success. The local yield stress (LYS) method, however, has demonstrated strong potential by identifying these soft spots based on local mechanical properties. In this study, we use the LYS method to analyze the distribution of soft spots in 2D silica network glass, where the local critical stresses are probed using mechanical simulations. Samples with different heterogeneities are generated through a Monte Carlo bond-switching algorithm and subjected to shear via an athermal quasistatic (AQS) deformation protocol. Our results reveal a high correlation between regions with low critical stresses and the locations of rearrangement events, especially for events that happen early in the shear simulations, though this correlation decreases as the system approaches failure. These findings highlight the method's effectiveness in predicting plastic activity in structural glasses, and confirming that soft spots can be identified from the initial configuration. Additionally, we optimized the probing length scale for different levels of heterogeneity and analyzed the resulting statistical distributions of the local critical stresses, which are essential for accurate macroscopic modeling of silica glass.

Network glass fracture occurs as a sequence of elementary events occurring at weak sites in the glass structure. Fracture is a highly complex process that occurs suddenly and without obvious structural or thermodynamic signs prior to the event’s occurrence. We show that a stress threshold value quantified by local mechanical probing highly correlates with nanoscale crack nucleation in a two-dimensional network glass. Subsequently, a neural network-based predictor, the local intelligent stress threshold indicator (LISTI), links the local stress threshold with the undeformed local structural topology. LISTI yields a reliable heatmap indicating soft spots that strongly correlate with the localized initiation and development of the fracture process. Finally, we show that LISTI can be used to find local zones prone to rearrangement in real-measured two-dimensional silica glass structures. Network glass fracture is a sudden, complex process lacking clear precursors. Here, the authors develop the local intelligent stress threshold indicator (LISTI), a neural network-based tool that predicts nanoscale crack nucleation by correlating local stress thresholds with structural topology, offering a method to identify fracture-prone zones in network glasses.

Pedestrian safety remains a major public health concern. Although numerical human body models are widely used in the automotive industry, most lack active muscle representation, a factor that can influence body kinematics during low-speed collisions. This study investigates how skeletal muscle activation impacts pedestrian kinematics and injury. A skeletal muscle model combining 3D tetrahedral and 1D line elements was developed. Passive behavior was modeled using the Ogden model, while active behavior was based on the Hill-type muscle model. The model was validated against published data and integrated into a finite element pedestrian model. Four levels of muscle activation were applied to examine their effects on kinematics and injury metrics. Active muscle significantly influences pedestrian response during collisions. Models with active muscle demonstrated higher contact forces, head injury criteria, lower extremity bending moments, and knee shear distance but lower knee bending angles. For instance, at 20 km/h, comparing passive to fully active models revealed a significant 179% increase in Head Injury Criterion (HIC), a 10% decrease in knee bending angle, and a 7.7% increase in shear distance. Varying activation levels had a minimal effect on lower extremity forces and moments but influenced HIC and knee metrics. The same trends were observed at higher impact speeds. These results underscore that including active muscle behavior is essential for accurate pedestrian injury prediction in low-speed collisions.

We proposed a deep-learning attention-based methodology to predict acoustic sources obtained from pendulum impact experiments using the Cluster-Self Adaptive Network (CSAN) and showed that the experimental data required for training can be reduced by 50% without losing significant localization accuracy. Acoustic signals due to pendulum impacts on a homogeneous steel plate were recorded by an asymmetric microphone array. Important wavelet features were extracted by transforming the acoustic signals using continuous wavelet functions and reduced the data dimensionality by principal component analysis. Two data sampling strategies (random and Latin hypercube) were investigated to study the effect of the density of training domains on the model performance. The attention-based modulation strategy was employed on microphone positions for data augmentation and prediction of acoustic sources. A comprehensive analysis of the CSAN-based localization results including error estimation was performed. The outcome was contrasted against delay-and-sum beamforming localization results.

Regarding laminated structures, an electromechanically coupled Finite Element (FE) model based on Layerwise Third-Order Shear Deformation (LW-TOSD) theory is proposed for static and dynamic analysis. LW-TOSD ensures the continuity of in-plane displacements and transverse shear stresses. The current LW-TOSD can be applied to arbitrary multi-layer laminated structures with only seven Degrees of Freedom (DOFs) for each element node and eliminates the use of the shear correction factors. Moreover, a shear penalty stiffness matrix is constructed to satisfy artificial constraints to optimize the structural shear strain. A dynamic finite element model is obtained based on LW-TOSD using the Hamilton’s principle. First, the accuracy of the current model is validated by comparing with literature and ABAQUS results. Then, this study carries out numerical investigations of piezolaminated structures for different width-to-thickness ratios, length-to-width ratios, penalty stiffness matrix, boundary conditions, electric fields and dynamics.

We present a method based on the time difference of arrival (TDOA) and index minimal-error subsets of microphones to localize sudden cracking sound events, which appear somewhere in flax-fiber reinforced concrete specimens. Validation tests with small-scale pendulum impacts and known impact locations were carried out. Error estimation was performed and error ellipses were calculated. Microphone subsets leading to the smallest localization error were indexed. We validated the localization accuracy against localization results calculated using the delay-and-sum beamforming technique. Further, tension tests on concrete specimens were performed until failure; crack patterns were recorded by photogrammetry. The cracking sound events were localized. With the good match between TDOA-based localization results and crack patterns, we demonstrate that the proposed localization procedure is reliably applicable for real-time localization of concrete cracking.

Rare-earth containing magnesium alloys are promising biomedical materials for a new generation of biodegradable orthopaedic implant systems due to their excellent biocompatibility, mechanical and biodegradation properties. However, chemo-mechanical interactions in aggressive physiological corrosion environments result in rapid degradation and early loss of mechanical integrity, limiting its broader application for orthopaedic implants. To date, only few studies have assessed the corrosion-fatigue behaviour of medical-grade magnesium alloys in an organic physiological corrosion environment, especially under sterile test conditions. In the present work, the corrosion-fatigue behaviour of fine-grained medical-grade magnesium alloy WE43MEO was systematically analysed under in vitro conditions using an organic physiological fluid DMEM. The experimental results showed that the fatigue strength of the alloy is nearly unaffected by a 1-day precorrosion, while a 7-day precorrosion resulted in a significant deterioration of mechanical integrity. In corrosion-fatigue experiments, the fatigue life was considerably reduced by interactions between corrosion and fatigue damages. The SEM analysis revealed that the mixed mode of intergranular and transgranular fracture in the crack propagation zone transits to intergranular cracking dominant mode under the corrosion-fatigue conditions due to hydrogen embrittlement.

Combined robotic arms and mobile platforms, mobile construction robots(MCRs) are providing an energizing choice for the digitalization of the building industry. To enhance the comprehension of the research trajectory towards MCR applications and technologies in building construction, we focus on the following aspect: Current representative applications of MCRs in built environments and critical technologies involved. This comprehensive review identified 184 publications in the last 15 years to unravel MCRs in construction applications, scrutinized the crucial technologies involved, and deliberated on challenges and opportunities. Results indicate that MCRs are a growing application field, although the majority are still confined to laboratory settings. To further expand the application of MCR in construction scenarios, this paper proposes corresponding research roadmaps to address the challenges identified. The findings of this review provide an in-depth insight into digital construction and robotics, benefiting researchers and constructors in advancing robotic commercialization.

Biodegradable magnesium alloys have been developed as promising biomedical materials for temporary implants. To facilitate the development of implant design, it is essential to understand and quantify the corrosion behaviour of magnesium alloys under mixed chemo-mechanical loadings. In this study, a multiphase-field model is proposed based on the variational principle to capture the interactions between corrosion and ductile fracture in biodegradable magnesium alloys. Multiple order parameters are introduced to track the interfaces associated with crack propagation and magnesium dissolution. The deformation-fracture-corrosion interactions are considered in the energetic variational formulation with coupling functions. The governing equations are discretised using an incremental variational method and solved by a staggered scheme. Parametric studies on the coupling functions are performed to demonstrate the flexibility of the model. The proposed multiphase-field model is calibrated and validated by in vitro experiments with tensile specimens of rare-earth magnesium alloy WE43MEO. The experimental and computational results demonstrate that the proposed model can capture the degradation and stress corrosion cracking behaviours in the immersion tests and slow strain rate tensile-corrosion tests.

Anterior Vertebral Body Tethering (VBT) is a novel fusionless treatment option for selected adolescent idiopathic scoliosis patients which is gaining widespread interest. The primary objective of this study is to investigate the effects of tether pre-tension within VBT on the biomechanics of the spine including sagittal and transverse pa-rameters as well as primary motion, coupled motion, and stresses acting on the L2 superior endplate. For that purpose, we used a calibrated and validated Finite Element model of the L1-L2 spine. The VBT instrumentation was inserted on the left side of the L1-L2 segment with different cord pre-tensions and submitted to an external pure moment of 6 Nm in different directions. The range of motion (ROM) for the instrumented spine was measured from the initial post-VBT position. The magnitudes of the ROM of the native spine and VBT-instrumented with pre-tensions of 100 N, 200 N, and 300 N were, respectively, 3.29 degrees, 2.35 degrees, 1.90 degrees and 1.61 degrees in extension, 3.30 degrees, 3.46 degrees, 2.79 degrees, and 2.17 degrees in flexion, 2.11 degrees, 1.67 degrees, 1.33 degrees and 1.06 degrees in right axial rotation, and 2.10 degrees, 1.88 degrees, 1.48 degrees and 1.16 degrees in left axial rotation. During flexion-extension, an insignificant coupled lateral bending motion was observed in the native spine. However, VBT instrumentation with pre-tensions of 100 N, 200 N, and 300 N generated coupled right lateral bending of 0.85 degrees, 0.81 degrees, and 0.71 degrees during extension and coupled left lateral bending of 0.32 degrees, 0.24 degrees, and 0.19 degrees during flexion, respectively. During lateral bending, a coupled extension motion of 0.33-0.40 degrees is observed in the native spine, but VBT instrumentation with pre-tensions of 100 N, 200 N, and 300 N generates coupled flexion of 0.67 degrees, 0.58 degrees, and 0.42 degrees during left (side of the implant) lateral bending and coupled extension of 1.28 degrees, 1.07 degrees, and 0.87 degrees during right lateral bending, respectively. Therefore, vertebral body tethering generates coupled motion. Tether pre-tension within vertebral body tethering reduces the motion of the spine.

Additive manufacturing (AM) of metallic components has recently become a viable option for series production. In this, the powder of various metallic materials such as steel and aluminum can be processed layer-by-layer to produce dense parts with excellent properties. One of the major challenges in this process is the occurrence of residual stresses, which negatively affect the strength and functionality of the produced components. Understanding the mechanism of distribution of these stresses and the detrimental deformations is one of the main themes covered in this work.In the numerical treatment with the finite element method, a phase-field model (PFM) for phase-change materials is utilized together with a thermo-elastoplastic model to simulate the multi-layer AM process and to evaluate the occurring residual stresses. Using the PFM allows tracking the diffusive melting front and, thus, distinguishing between the melted (soft) and the unmelted (hard) states of the material. One of the novel contributions is the definition of a phase-field history variable, which can capture the irreversible process of metallic powder melting. Additionally, phase-field-dependent material properties are proposed.The coupled governing equations are solved in the open-source FE package FEniCS Project, where three-dimensional initial-boundary-value problems are introduced and the results are compared with reference data from the literature.