Degradation Rules Research Articles

Predicting the fatigue life of T800 carbon fiber composite structural component based on fatigue experiments of unidirectional laminates

This study is based on the fatigue experiment results of unidirectional laminates and investigates the fatigue performance of T800 carbon fiber/epoxy resin composite structural components through experimental and numerical analysis. Fatigue experiments were performed on [0]16, [90]16, and [±45]8 laminates individually, obtaining the fundamental fatigue parameters necessary for modeling. The fatigue life model for T800 carbon fiber/epoxy resin unidirectional laminates was refined, and fatigue degradation rules were provided for the fatigue progressive damage model. A fatigue progressive damage analysis model for T800 laminates was established based on the 3D Hashin criterion, predicting the fatigue life and fatigue damage failure process of the laminates. A fatigue life prediction model was developed for T800 carbon fiber composite I-beam structural components, which can predict the primary types of fatigue failure, fatigue life and the location of fatigue failure occurrence. The predicted fatigue life of structural components shows good consistency with the experimental results. This method can calculate structural components including ply angles of 0°, 45°, and 90° and obtain the fatigue life contour.

Fatigue Analysis of Composite Bolted Joints under Random and Constant Amplitude Fatigue Loadings.

This paper attempts to analyze the random fatigue life and failure modes of joints using two calculation methods. Three kinds of tests were carried out, which were the static test, constant amplitude fatigue test and the random fatigue test, and four kinds of joints were designed. After the static test, the joint was subjected to a constant amplitude fatigue test by selecting different percentages of load according to the static strength. In order to predict the random fatigue life more precisely, two calculation methods were carried out, which were the linear cumulative damage method and the equivalent loading finite element method. Based on the linear cumulative damage hypothesis, the fatigue life of the joint was established as a function of the load amplitude, and then, the random life prediction was calculated by the amplitude distribution of the random loading. Another method was the equivalent loading method, which was to obtain the equivalent constant amplitude fatigue loading of the random loading spectrum. The finite element model was established based on the stiffness and strength degradation rule. The equivalent random life and fatigue failure modes of the joint were modeled. The two life prediction methods show good agreement with the fatigue experimental result, and all prediction results were included in a scatter band of the factor of 2.

A knowledge distillation based cross-modal learning framework for the lithium-ion battery state of health estimation

The accurate prediction of a lithium-ion battery’s State of Health is of critical importance for efficient battery health management. Existing data-driven estimation methodologies grapple with issues such as high model complexity and a dearth of guidance from prior knowledge, which impose constraints on their efficacy. This work introduces a novel cross-modal distillation network for battery State of Health estimation, structured around a TransformerEncoder as the teacher network and a Convolutional Neural Network as the student network. Initially, the teacher model is pre-trained offline using State of Health degradation data to learn the degradation patterns. The directly measurable feature data (such as voltage, temperature, and current) is subsequently fed into the student network for online training and computation of a hard loss. the student network’s output is then directed into the pre-trained the teacher network to compute a soft loss, thereby offering prior knowledge of degradation laws and steering the optimization process of the student network. Rigorous experiments are conducted utilizing various datasets, with the outcomes validating the superior estimation accuracy and degradation rule adherence of the model. Notably, among five different models, this model demonstrates the best performance on almost all datasets, achieving an RMSE of 0.0097 and an MAE of 0.0065 on Cell1 of the Oxford dataset. Moreover, the model also demonstrates robust performance across different usage scenarios, inclusive of multi-battery estimation. Furthermore, this paper also introduces a fine tuning method for State of Health predictions only using the first half of the data. Comparative analysis with other models underscores the competitiveness of the proposed model, showcasing its potential for broader application.

Effect of complex-grain-size curves on shear modulus degradation of calcareous sand

The dynamic shear modulus G and its degradation tendency G / G max are essential parameters for characterizing the dynamic properties of soil. Coastal facilities constructed on calcareous sand foundations are subjected to multiple types of cyclic loadings. To investigate the effects of the uniformity coefficient C u , effective confining pressure σ 0 ′ , relative density D r , and median grain size d 50 on the dynamic G and its degradation rule for calcareous sand, a set of resonant column tests is conducted on calcareous sand with different grain-size-distribution curves. The experimental results clearly show that the G degrades faster for calcareous sand with a lower σ 0 ′ and a larger C u . By contrast, the degradation of the G is affected less by D r and d 50 . The G / G max values decreased slightly as γ increased but remained less than 10 − 5 , whereas the G / G max curve descended rapidly as γ increased beyond 10 − 5 . Based on the test results obtained in this study and previously published data, a new mathematical model characterizing the degradation tendency of G / G max with respect to the shear strain level of calcareous sands with different gradation conditions is proposed. The relevant parameters associated with the proposed model are correlated with C u and σ 0 ′ .

Stochastic degradation modeling and remaining useful lifetime prediction based on long short-term memory network

In this paper, a novel remaining useful lifetime (RUL) prediction method that fuses stochastic degradation modeling and machine learning is proposed to improve the fitness of the model and quantify the uncertainty of the prediction results. First, a stochastic degradation model based on the Wiener process is built, and the drift increment is extracted using empirical mode decomposition (EMD). Second, a long short-term memory (LSTM) network is trained to learn the equipment degradation rule and predict the drift increment. The diffusion coefficient of the degradation model is then estimated according to the maximum likelihood principle. The final step is to derive the analytical expression for the probability distribution of remaining useful lifetime (RUL) based on the concept of first hitting time and the difference principle. The lithium battery degradation test confirmed the efficacy of the proposed method, achieving a life cycle average prediction accuracy of up to 97.45%.

Geometry Effects on Joint Strength and Failure Modes of Hybrid Aluminum-Composite Countersunk bolted Joints

This paper presents the effects of geometry parameters (width/hole diameter, and edge distance/hole diameter ratios) on the damage initiation and growth in the CFRP (Carbon Fiber Reinforced Polymer) composite-aluminum countersunk bolted joints. Strain gauge measurements conducted with an Instron testing machine along with a detailed 3D finite element model incorporating geometric, material, and friction-based contact nonlinearities were used to investigate the geometry parameters on the Progressive Damage Analysis (PDA) of the orthotropic material model. The PDA material model integrates the lamina nonlinear shear deformation, Hashin-type failure criteria, and strain-based continuum degradation rules, using the UMAT user subroutine in the MSC Software Corporation Patran-Nastran commercial software. The results showed that the geometry effects on damage initiation and failure modes are quite accurately predicted by the PDA material model, which proved to be computationally efficient, and therefore can predict failure propagation and damage mechanisms. Plate geometry is an important parameter in the design process of an adequate bolted joint while its effects on damage initiation and failure modes were quite accurately predicted by the analysis. The latter proved to be computationally efficient, and could successfully predict failure propagation and damage mechanism in hybrid metal-composite countersunk bolted joints.

Flexible Performance-Based Control for Nonlinear Systems Under Strong External Disturbances.

Addressing external disturbances has been a critical issue for control design to ensure reliable operation of systems. This article investigates the tracking control problem for the uncertain nonlinear systems with the strong external disturbance and the prescribed performance. The flexible performance-based control scheme is developed by introducing an external disturbance criterion into the prescribed performance. It is capable of guaranteeing the prescribed performance if the external disturbance is less than a specified threshold and degrading that in light of the user-appointed rule otherwise. Particularly, the disturbance interval observer is synthesized to generate the boundaries of the external disturbances and realize the judgment of that criterion. With the generated boundaries, the interval-type auxiliary system is designed to provide the modified performance functions (MPFs) that characterize performance requirement and degradation rule simultaneously. Based on the positive system theory and the Lyapunov method, it is theoretically shown that the system output can always track the reference signal and satisfy the constraints of MPFs. Finally, both the numerical simulation and the application of flight control design verify that the results are effective and valid.

Progressive failure analysis on composite structures using a wave based method

This research examines a Progressive Damage Model for laminated composites using a wave-based technique. Using conventional numerical methodologies, such studies may require a significant amount of CPU time. Compared to standard techniques, the approach proposed can provide a quick and cost-effective evaluation. This work presents a numerical approach for dealing with periodic media subjected to an impact load that can provide damage initiate and evolution. The proposed model is based on a hybrid formulation for the wave-damage interaction that describes wave scatterings, as well as a stress analysis of a composite structure subjected to an impact force. The failure analysis is carried out by identifying the damage location and then describing the modification of the material properties of the damaged elements using Hashin’s failure criteria and material property degradation rules. A comparison of stress distribution with results from the conventional finite element approach was performed in order to confirm the hybrid WFE/FE method, and good agreement was noted. Finally, an impact on a composite beam was studied. The evolution of the structural failure is presented. This article takes into consideration material degradation over time.

Experimental study on seismic performance of Chinese traditional mortise and tenon joints with different lengths that tenons pull out of mortise

This paper presents the results of a study on seismic performance of mortise-tenon joints with different lengths that tenons pull out of joints. Three 1:3.52 scaled mortise-tenon joint specimens were fabricated: one with through-tenon joints, one with half-tenon joints, and one with dovetail joints. Seismic data of the joints, such as hysteretic curves, skeleton curves, stiffness degradation rules, and energy dissipation capacity curves, were obtained by low-cycle reversed loading test. The influence of lengths that tenons pull out of joints on the mortise-tenon joints was analyzed. The seismic performance of three types of mortise-tenon joints was compared. The results showed that all hysteretic loops are z shaped. The seismic performance of the through joint was the best among three types of mortise tenon joint. The length that the tenon pulls out of the joint significantly affected the performance of the mortise and tenon joints.

Simplified Evaluation of Shear Stiffness Degradation of Diagonally Cracked Reinforced Concrete Beams.

Shear cracking in concrete box-girder bridges, which could cause excessive deflection during the serviceability limit state, cannot be effectively avoided by code-guided design. While elastic shear deformation only accounts for a small proportion of total deformation for un-cracked reinforced concrete (RC) beams, the magnitude of after-cracking shear deformation becomes comparable to flexural deformation for RC beams. However, there is still a lack of practical models to predict the after-cracking shear deformation of RC beams. First, six thin-webbed I beams were tested to investigate the shear stiffness degradation mechanism and the decrease ratio. Then, a very simple truss strut angle formula, which is the crucial parameter for shear stiffness, was established. Furthermore, a stiffness degradation rule for partially cracked beams was proposed considering the influence of concrete tension stiffening, which is essential for predicting the development process of after-cracking shear deformation. Finally, directly measured shear strains were used to validate the proposed shear stiffness model. The results showed that the shear stiffness drops to about 30~40% of the original stiffness after the first diagonal crack, and the remaining shear stiffness is only about 10% of the original one when the stirrup yields. Increasing the stirrup ratio is a more effective method to control shear stiffness degradation for diagonally cracked RC beams. Also, the proposed shear stiffness model well captures the main features of the shear stiffness degradation, and it provides a relatively accurate prediction of the equivalent shear stiffness at the post-cracking stage.

Mechanical properties and constitutive model of Q460 steel during the fire-cooling stage

The mechanical properties of steel under fire-induced elevated temperatures is fundamental and crucial for structural fire-resistance design and analysis. Mechanical properties of high-strength steel (HSS) during the fire-heating and post-fire stages have been extensively investigated, while those during the fire-cooling stage were rarely studied. This study investigated the mechanical properties of HSS Q460 during the fire-cooling stage by steady-state tensile experiments Results show that the mechanical performance of HSS Q460 at the fire-cooling stage is not only related to the test temperature (Tt) but also the peak heating temperature (Tp) before cooling for tests. The strength of HSS Q460 at identical Tt shows an overall download trend with the increase of Tp. The mechanical properties of HSS Q460 at the fire-cooling are similar to those at the fire-heating stage when Tp is not greater than a certain value. This value is 600 °C for Young’s modulus and yield strength f0.2, and 400 °C for yield strength f1.0, f2.0 and ultimate strength. When Tp exceeds certain values, the Young’s modulus and strengths at the fire-cooling stage are less than those at the fire-heating stage. Moreover, the mechanical properties of HSS Q460 were compared with those of HSS Q690, indicating that the degradation rules of mechanical properties of HSSs Q460 and Q690 differ during the fire-cooling stage. Besides, formulas were proposed to calculate the mechanical properties and stress–strain relationship of the HSS Q460 during the fire-cooling stage.

Res-HSA: Residual hybrid network with self-attention mechanism for RUL prediction of rotating machinery

Remaining useful life (RUL) prediction is of great significance for improving maintenance efficiency and ensuring the reliability of rotating machinery. In recent years, there are a large number of deep-learning-based methods for RUL prediction of rotating machinery. However, the effect of conventional end-to-end RUL prediction methods relies on the distribution consistency of training data and test data, and conventional health indicator extrapolation RUL prediction methods are susceptible to interference from abnormal fluctuations in the health curve. To overcome the problem, this paper proposes a new RUL prediction method for rotating machinery using health indicators constructed by the residual hybrid network with self-attention mechanism (Res-HSA). First of all, we propose the residual hybrid network combined with self-attention mechanism to extract the high-level degenerate feature. Then, the health assessment model based on Res-HSA is proposed to generate the health indicators of the equipment. To assist in network training, the segmented data labels based on the degradation rule are applied to optimize the labels of training sets. Finally, to address the problem of abnormal fluctuations in the health curve, a fitting interval selection method is used to optimize conventional curve fitting schemes to calculate RUL. Two public datasets, IEEE-PHM-2012-challenge datasets and C-MAPSS datasets, are used to verify the effectiveness of the proposed method. The experiment results on two public datasets show that the RUL prediction method proposed in this paper has good prediction performance. Compared to the state-of-the-art method, the method proposed in this article reaches the most advanced level in some test projects, while the rest of the projects can be very close to the most advanced method.

Field measures of strengthen plant-microbial remediation of PAHs-FQs compound pollution.

In this study, five PAHs (benzo [b] fluoranthene (BbF), phenanthrene (Phe), fluoranthene (Flu), fluorene (Fl), benzo [A] pyrene (Bap)), and five FQs (ofloxacin (OFL), enrofloxacin (ENR), ciprofloxacin (CIP), norfloxacin (NOR), lomefloxacin (LOM)) were selected as ligands; peroxidase (1NML) was selected as receptor degrading protein. In the plant-microbial degradation, the factors with significant inhibitory effects are NOR, Bap, CIP, ENR, OFL, Flu, LOM, Phe, Fl, and BbF by the fractional factorial design experiment and molecular docking-assisted molecular dynamics methods. Using Taguchi experiment and molecular dynamics simulation methods, the main external field measures were designed and screened to effectively promote the degradation of PAHs-FQs under the combined pollution scenarios of Bap-CIP and BbF-NOR, respectively. The peroxidase mutation design plans with enhanced substrate affinity were then designed and screened using the DS software by predicting the virtual key amino acid of peroxidase. The novel biodegradable enzymes 2YCD-1, 2YCD-4, 2YCD-5, 2YCD-7, and 2YCD-9 had better structures and showed excellent degradability for PAHs and FQs. This study explored the degradation rules of the composite pollutants in the coexistence systems of multiple PAHs and FQs, providing the best external field measures for the control and treatment of the combined pollution effects of different PAHs and FQs. Overall, the current study has important practical significance for promoting the plant-microbial joint remediation of PAHs-FQs pollution and for reducing the combined pollution of PAHs and FQs in farmland systems.

Damage Analysis of CFRP Hybrid Bonded-Bolted Joint during Insertion of Interference-Fit Bolt.

In this study, experiments and finite element analysis (FEA) were used to evaluate the impact of interference-fit sizes on CFRP hybrid bonded-bolted (HBB) joint damage during bolt insertion. The specimens were designed in accordance with the ASTM D5961 standard and bolt insertion tests were performed at selected interference-fit sizes (0.4%, 0.6%, 0.8%, and 1%). Damage to composite laminates was predicted using the Shokrieh-Hashin criterion and Tan's degradation rule via the user subroutine USDFLD, while damage to the adhesive layer was simulated by the Cohesive Zone Model (CZM). The corresponding bolt insertion tests were performed. The variation of insertion force with interference-fit size was discussed. The results showed that matrix compressive failure was the main failure mode. With the growth of the interference-fit size, more failure modes appeared, and the failure region expanded. Regarding the adhesive layer, it did not completely fail at the four interference-fit sizes. This paper will be helpful in designing composite joint structures and especially for understanding CFRP HBB joint damage and failure mechanisms.

The Long-Term Interfacial Evolution and Prediction of Carbon- and Glass-Fiber-Reinforced Epoxy Hybrid Rods under a Hygrothermal Environment.

In order to promote the engineering applications of carbon- and glass-fiber-reinforced epoxy hybrid rods, it is necessary to fully understand its long-term hygrothermal durability. In the present study, the water absorption behaviors of a hybrid rod in a water immersion environment are studied experimentally, the degradation rules of the mechanical properties are obtained, and establishing a life prediction model is attempted. The water absorption of the hybrid rod confirms to the classical Fick's diffusion model, and the water absorption concentration is determined by radial position, immersion temperature, and immersion time. In addition, the radial position of water molecules diffused into the rod is positively correlated with the diffusion concentration. The short-beam shear strength of the hybrid rod decreased significantly after 360 days of exposure; this is because water molecules interact with the polymer through hydrogen bonds to produce bound water during the immersion process, leading to resin matrix hydrolysis and plasticization, as well as interfacial debonding. In addition, the ingression of water molecules caused degradation in the viscoelastic behavior of the resin matrix in hybrid rods. The glass transition temperature of hybrid rods decreased by 17.4% after exposure at 80 °C for 360 days. The Arrhenius equation was used calculate the long-term life of short-beam shear strength in the actual service temperature based on the time-temperature equivalence theory. The stable strength retention for SBSS was found to be 69.38%, which is a useful durability design parameter for hybrid rods in civil engineering structures.

Digital twin driven production progress prediction for discrete manufacturing workshop

• This article focuses on a novel synchronous update method for digital twin workshop model with integrating learning approach. • The representation model is established to describe the performance degradation rules of discrete manufacturing workshop. • An Adaboost-DNN-LSTM based synchronous update method is proposed to guarantee the consistency between DTM and physical workshop. • A competitive election mechanism is designed to select base learner among DNN and LSTM between historical and dynamic sample. In make-to-order manufacturing enterprises, accurate production progress (PP) prediction is an important basis for dynamic production process optimization and on-time delivery of orders. Digital twin technology offers an enabling tool for PP analysis. Although the production process can be observed, analyzed, and controlled in real-time by digital twin model (DTM), there exist some uncertain events, degradation of manufacturing elements, and abnormal disturbance in physical workshop (PW), which would cause the deviation between DTM and PW performance and affect the prediction accuracy of PP. Synchronous evolution of DTM for precision holding to ensure the consistency between DTM and the performance of PW, and guarantee the accuracy of DTM is still a challenging issue, especially when dealing with new dynamic samples for complex production environment of discrete manufacturing workshop (DMW). This article focuses on how to effectively construct DTM synchronous update methods based on dynamic sample data for DMW. This study proposes a representation model of performance degradation and an Adaboost-DNN-LSTM based synchronous update model with competitive election mechanism to enhance the accuracy of PP prediction with time in industrial environment. The experiment is conducted in the realistic production dataset, which demonstrates that the proposed synchronous evolution model has good performance for realizing the synchronization of the performance of physical workshop in industrial environment, and can greatly improve the prediction ability for PP.

A novel creep-fatigue life evaluation method for ceramic-composites components

A novel progressive creep-fatigue damage analysis method based on multiscale information was proposed to accurately predict the service life of SiC/SiC composites and their structural components under complex mechanical loads and external environments. First, the strength degradation of SiC fibers involving five damage mechanisms and degradation of the properties of the interface under high-temperature fatigue loading were described quantitatively based on experimental results. Subsequently, a micromechanical model incorporating the global load sharing model, two-parameter Weibull model, shear-lag model, property degradation models of various constituents, and the effect of the braiding angle was utilized to calculate the volume fraction of broken fibers. The fatigue life of 3D 4-directional SiC/SiC composites was predicted and exhibited a trend similar to that of the experimental data. The rupture life of the SiC/SiC composites was calculated considering the stress transfer among microscopic constituents and the influence of creep slow crack growth on the fiber strength. The volume fraction of broken fibers obtained in these micromechanical models was used to perform a gradual strength degradation in the novel progressive creep-fatigue damage analysis method. Microscopic constituents damage and macroscopic property degradation were quantitatively linked in this step. This infuses the micro information into the traditional phenomenological progressive damage method. The gradual stiffness degradation rules were derived from the fitting of the residual elastic modulus obtained from the fatigue and creep experiments. Finally, the damage evolution and service life of SiC/SiC turbine blades were simulated and evaluated as a preliminary validation of the entire process.

Continuum damage mechanics based failure prediction and damage assessment in laminated composite structures

In the present work, nonlinear mechanical response of laminated composite structures is investigated by a constitutive numerical model based on damage mechanics. A numerical method is included in progressive damage analysis to simulate the damage accumulation and failure response of composite structures under service load conditions. This model is capable of predicting the residual strength and residual stiffness of laminate with arbitrary lay-up and geometry. The state-of-the-art progressive damage model exploits a set of stress-based Hashin-type criteria and a set of appropriate degradation rule of ply discount. The constitutive model is developed wherein the damage initiation criteria are based on Hashin failure criterion for damage evolution. The proposed progressive damage model involves the steps of stress analysis, failure analysis, and material properties degradation. This work involves a comprehensive study of quasi-isotropic elliptical hole specimen to develop the failure model. Overall, the results of the exercise aid in design applications to predict the impact of progressive damage in aircraft composite structures under service loads.

Improved particle filter remaining life prediction method for switch mode power supply using variance of case temperature

Switch mode power supply plays a crucial role in industrial systems as an energy source. Its remaining life prediction is vital to help in schedule maintenance to avoid the unexpected failures of industrial systems. Due to the high cost of monitoring the degradation characterization parameter and the complexity of the degradation rule, predicting remaining life for the switch mode power supply is challenging. This study used a novel degradation characterization parameter, i.e., the variance of the case temperature, to characterize switch mode power supply degradation. Multi-stage state equations and degradation thresholds of each stage were established based on the change in the variance of the case temperature. Additionally, the state equations and degradation thresholds were introduced into the traditional particle filter method to form an improved particle filter, which is a useful life prediction method. The effect of the proposed method was evaluated and compared with other methods. The results show that over the life of a switch mode power supply, there are three change stages in the variance of the case temperature: the constant tendency stage, linear increase stage, and exponential increase stage. The degradation thresholds of these stages were 20 %, 70 %, and 200 %, respectively. The predicted curve of the variance of the case temperature was in good agreement with the test curve, and the prediction interval at a 90 % confidence level was narrow. Compared with other algorithms, the improved particle filter method could maintain the prediction error within 5 %, and was more suitable for predicting the remaining life of a switch mode power supply with multi-stage degradation characteristics.

Characterization and Gel Properties of Low-Molecular-Weight Carrageenans Prepared by Photocatalytic Degradation.

Low-molecular-weight carrageenan has attracted great interest because it shows advantages in solubility, absorption efficiency, and bioavailability compared to original carrageenan. However more environment-friendly and efficient methods to prepare low-molecular-weight carrageenan are still in great need. In the present study, a photocatalytic degradation method with only TiO2 has been developed and it could decrease the average molecular weight of κ-carrageenan to 4 kDa within 6 h. The comparison of the chemical compositions of the degradation products with those of carrageenan by FT-IR, NMR, etc., indicates no obvious removement of sulfate group, which is essential for bioactivities. Then 20 carrageenan oligosaccharides in the degradation products were identified by HPLC-MSn, and 75% of them possessed AnGal or its decarbonylated derivative at their reducing end, indicating that photocatalysis is preferential to break the glycosidic bond of AnGal. Moreover, the analysis results rheology and Cryo-SEM demonstrated that the gel property decreased gradually. Therefore, the present study demonstrated that the photocatalytic method with TiO2 as the only catalyst has the potential to prepare low-molecular-weight carrageenan with high sulfation degree and low viscosity, and it also proposed the degradation rules after characterizing the degradation products. Thus, the present study provides an effective green method for the degradation of carrageenan.