Physical Failure Mechanisms Research Articles

A nanofibril network model of biological silks

Biological silks produced by, for examples, spiders and silkworms exhibit exceptional mechanical properties, including high strength, toughness, and extensibility, which arise from their optimized hierarchical structures. Understanding the relationship between their structures and properties is crucial for the design of bioinspired materials. However, it remains a challenge to recapitulate their macroscopic mechanical behaviors of biological silks with a micromechanical model that integrates the physical mechanisms of deformation and failure, such as molecular unfolding and nanofibrous remodeling. In this paper, we propose a nanofibril network (NFN) model to analyze the deformation, damage, and fracture processes of biological silks. The NFN model reveals how molecular unfolding affects the deformation and fracture of biological silks at the nanofibrous scale. It is observed that molecular unfolding consecutively occurs, evolving into band or elliptical morphologies with increasing tensile strain. Intermolecular hydrogen bonds tend to stabilize the expansion of unfolding zones. The NFN model also unveils how the instability of molecular unfolding is suppressed by nanofibrous architecture and how intermolecular hydrogen bonds interact with intramolecular unfolding to toughen the biological silk. This work provides a theoretical tool for studying and designing biomimetic nanofibrous materials.

A nonlinear and rate-dependent fracture phase field framework for multiple cracking of polymer

Designing durable polymer-based materials and structures requires understanding how the physical processes and failure mechanisms associated with moisture intrusion and loading rate combine to produce the complex damage modes observed in their applications. While numerous experiments have assisted us in gaining general insight into the above issues, in-situ characterization is still lacking, and rational prediction on the failure procedure of polymer in the moisture–mechanical​ coupling environment remains challenging. Here we present a thermodynamically consistent theoretical framework and its computational implementation to reveal how the dependence of epoxy’s mechanical behavior on rate- and moisture dictates the initiation and evolution of highly localized deformation zones and cracks. A multi-physical hydrothermal-viscoelastic–viscoplastic-damage model is first proposed to fully couple the effects of strain rate, moisture degradation, strain softening, and adiabatic heating of the bulk epoxy in a highly humid environment. The constitutive models are augmented by a phase field that enables spontaneous tracing of the nucleation and growth of cracks under a wide range of strain rates (from 4.60 × 10−5/s to 3.80 × 103/s). The theoretical model is implemented into an efficient computational procedure that is used to calculate illustrative examples to demonstrate the influence of the intimate coupling between the multi-physical phenomena. The results highlight significant mechanisms underlying the coupled damage process, including: heat accumulation along the crack propagation path; identification of the energies associated with moisture-induced toughening; prediction of crack deflection in the presence of moisture absorption; and illustration that a portion of the plastic work plays a dominant role in the nucleation of cracks at a wide scale of strain rates.

How Small Slip Surfaces Evolve Into Large Submarine Landslides—Insight From 3D Numerical Modeling

AbstractSubmarine landslides are a major marine geohazard affecting resilience of offshore infrastructure and coastal urban centers. Attention has previously been paid to quantifying post‐failure dynamics of catastrophic submarine debris flows and their consequences. However, pre‐failure initiation and growth of a small slip surface evolving into a large submarine landslide are still less understood. This study aims to explore the physical failure mechanism of submarine landslides initiated from small slip surfaces and to quantify key features of failure evolution. They are achieved by modeling the entire three‐dimensional (3D) landslide evolution, integrating the initiation and growth of slip surface, failure of slab above the slip surface, post‐failure mass movement and re‐deposition of transported sediments, using a novel numerical method. The characteristics of the slip surface growth within a favored layer and the patterns of the slab failure in the overlying layer have been thoroughly discussed. The transition from the pre‐failure slip surface growth to the diverse post‐failure mass movement are first observed and discussed with the 3D geometry effects, revealing the complex cascading mass movement mechanisms. The criterion for unstable growth of a planar slip surface and critical condition for slab failure are proposed. The findings from the study facilitate scientific understanding of the evolution of historic events and help safeguarding offshore developments against submarine landslide recurrence.

Phase field mechanics of residually stressed ceramic composites

ABSTRACT A phase field theory for crystalline solids accounting for thermoelasticity, fracture, twinning, and limited slip is presented. Residual stresses are incorporated via referencing thermoelastic strain to a reference state that is not always stress-free. Rate dependence, dissipated energy, and residual strain energy (possibly degraded by local fracture) are included in the theory, with physically valid predictions first verified for homogeneous stress states. A variational form of the model is implemented in finite element (FE) calculations of three-dimensional (3-D) polycrystalline aggregates consisting of up to two different crystal constituents and a binding matrix along grain and phase boundaries. Model specifics correspond to constituents of boron carbide-titanium diboride (BC-TiB) ceramic composites. Effects of thermal-residual stresses incurred during processing, as well as other local microstructure properties and physical features, on deformation and failure mechanisms are revealed. Peak aggregate strengths observed for different boundary conditions demonstrate a pressure-dependent failure surface.

Predicting early failure of quantum cascade lasers during accelerated burn-in testing using machine learning

Device life time is a significant consideration in the cost of ownership of quantum cascade lasers (QCLs). The life time of QCLs beyond an initial burn-in period has been studied previously; however, little attention has been given to predicting premature device failure where the device fails within several hundred hours of operation. Here, we demonstrate how standard electrical and optical device measurements obtained during an accelerated burn-in process can be used in a simple support vector machine to predict premature failure with high confidence. For every QCL that fails, at least one of the measurements is classified as belonging to a device that will fail prematurely—as much as 200 h before the actual failure of the device. Furthermore, for devices that are operational at the end of the burn-in process, the algorithm correctly classifies all the measurements. This work will influence future device analysis and could lead to insights on the physical mechanisms of premature failure in QCLs.

A novel adaptive boosting algorithm with distance-based weighted least square support vector machine and filter factor for carbon fiber reinforced polymer multi-damage classification

Adaptive boosting (AdaBoost) algorithms fuse multiple weak classifiers to generate a strong classifier by adaptively determining the fusion weights of the weak classifiers. According to incorrect or correct classification results, sample weights become larger or smaller. However, this weight update scheme neglects valuable information in the results. Moreover, an important requirement for weak classifiers is an accuracy higher than random guessing. This requirement is likely to lead to an unexpected result. This means that several generated weak classifiers with similar classification results cannot learn from each other. Consequently, the advantage of fusing multiple weak classifiers disappears. The classification and therefore distinction of different failure modes in materials is a typical task for classical nondestructive testing approaches as well as for new approaches based on machine learning methods. In the case different approaches can be applied, the main question is, which and how tuned approaches provide the best results in terms of accuracy or similar metrics. In the multi-damage classification task distinguishing different physical failure mechanisms in Carbon Fiber Reinforced Polymer (CFRP), the above two aspects complicate the application of AdaBoost algorithms. To improve the results, a novel AdaBoost with distance-based weighted least square support vector machine (WLSSVM) and filter factor is proposed. The distance-based WLSSVM is employed to increase the diversity of weak classifiers, the distances of the classification plane and samples are used to measure the classification task. The filter factor is proposed to filter out unnecessary classifiers contributing less to the final decision. The experimental results demonstrate that the improved AdaBoost schemes with distance-based WLSSVM and filter factor outperform state-of-the-art algorithms. The effects of the scheme using the new weighted update and the filter factor on the algorithm are discussed, respectively. The experimental results show that the combination of the two schemes perform better than other schemes.

A multiscale constitutive model for metal forming of dual phase titanium alloys by incorporating inherent deformation and failure mechanisms

Ductile metals undergo a considerable amount of plastic deformation before failure. Void nucleation, growth and coalescence is the mechanism of failure in such metals. α–β titanium alloys are ductile in nature and are widely used for their unique set of properties such as specific strength, fracture toughness, corrosion resistance and resistance to fatigue failures. Voids in these alloys have been reported to nucleate on the phase boundaries between α and β phase. Based on the findings of crystal plasticity finite element method investigations of the void growth at the interface of α and β phases, a void nucleation, growth, and coalescence model has been formulated. An existing single-phase crystal plasticity theory is extended to incorporate underlying physical mechanisms of deformation and failure in dual phase titanium alloys. Effects of various factors [stress triaxiality, Lode parameter, deformation state (equivalent stress), and phase boundary inclination] on void nucleation, growth and coalescence are used to formulate a phenomenological constitutive model while their interaction with a conventional crystal plasticity theory is established. An extensive parametric assessment of the model is carried out to quantify and understand the effects of the material parameters on the overall material response. Performance of the proposed model is then assessed and verified by comparing the results of the proposed model with the RVE study results. Application of the constitutive model for utilisation in the design and optimisation of the forming process of α–β titanium alloy components is also demonstrated using experimental data.

Simulation-based study of single-event burnout in 4H-SiC high-voltage vertical superjunction DMOSFET: Physical failure mechanism and robustness vs performance tradeoffs

We explore and elucidate physical failure mechanisms in a 4H-SiC, high voltage, superjunction (SJ) vertical DMOSFET from a single heavy ion strike using three-dimensional electro-thermal transient simulations. The single-event burnout (SEB) failure is thermal runaway from second breakdown, initiated by impact ionization and terminated with mesoplasma formation, at the center of the P-pillar/N+ substrate interface. We also demonstrate that the SEB performance of this SiC SJ DMOSFET is insensitive to the pillar width but sensitive to the strike location with ion strike at the P-pillar causing SEB at a lower blocking voltage than at the N-pillar. Compared to commercially available 1.2 kV blocking-rated non-SJ DMOSFETs, which have been demonstrated to survive SEB up to 525 V, the SJ DMOSFET increases SEB survival threshold voltage (VSEB) by a factor of 2.2, making it close to 1200 V, while the on-resistance is increased by only 11%. Using our recently developed figure of merit (FoM), which considers the trade-off between VSEB and on-state performance, we find that the SiC SJ DMOSFET achieves a FoM that is 14 times better, making it superior to conventional 1.2 kV SiC DMOSFETs for long-term radiation-tolerant operation in space applications.

Physical property and failure mechanism of self-piercing riveting joints between foam metal sandwich composite aluminum plate and aluminum alloy

Self-piercing riveting is a promising method to join thin-wall structures in the automobile industry, especially for the connection of different materials. Due to their excellent strength-to-weight ratios and vibration/noise reduction characteristics, foam metal sandwich composite aluminum plates are the best choices for modern automobiles. In this paper, the self-piercing riveting forming qualities and joint strengths of foam iron-nickel/copper sandwich composite aluminum plates with AA5052 aluminum alloys are investigated, and the fracture morphologies of tensile failure samples are characterized. The results showed that: the foam metal sandwich composite aluminum plates can increase the interlock width and improve the self-locking performance of the joints, and the bottom thicknesses are significantly increased when the foam metal sandwich composite aluminum plates are riveted as the bottom plates. In the tension-shear tests, the foam sandwiches reduce the maximum failure loads and increase the maximum failure displacements of the joints. Moreover, the macro/micro-structure characteristics of the foam metal sandwich composite aluminum plates affect the failure modes of the joints. When the foam metal sandwich composite aluminum plates are used as the top plates, the failure mode is that the composite aluminum plates break down in the direction perpendicular to the loading of the plates. When they are riveted as the bottom plates, the failure mode is that the rivets are entirely pulled out from the bottom base plates of the composite plates, partially separated from the foam metal sandwich composite aluminum plates, and the top base plates and the sandwich metals are torn apart at the same time.

Experimental and numerical evaluation of conduction welded thermoplastic composite joints

The capability of joining two thermoplastic composite parts by welding is a key technology to reduce the weight and cost of assembled parts and enables high volume manufacturing of future aeronautical structures made of thermoplastic composite materials. However, there is not much experimental understanding of the mechanisms involving welded joint failure, and the computational tools available for the simulation of thermoset composites have not yet been completely assessed for thermoplastic materials. In this work, a numerical and experimental evaluation is performed to investigate the strength and failure behavior of conduction welded thermoplastic composite joints. A welded single lap shear joint is designed, manufactured, tested and analyzed proposing two distinct modeling approaches. A simplified modeling strategy which only accounts for damage at the weld is compared to a high-fidelity model which can take into account the physical failure mechanisms at the lamina level. The high-fidelity modeling methodology is able to predict the experimental failure mode of the investigated welded joints with high accuracy and is used to gain new insights into the key-variables that influence the strength of thermoplastic welded joints. It is also found that the joint strength is highly influenced by the failure mechanisms not only of the welded interface but also of the surrounding plies.

Low-Depth Mechanisms for Quantum Optimization

One of the major application areas of interest for both near-term and fault-tolerant quantum computers is the optimization of classical objective functions. In this work, we develop intuitive constructions for a large class of these algorithms based on connections to simple dynamics of quantum systems, quantum walks, and classical continuous relaxations. We focus on developing a language and tools connected with kinetic energy on a graph for understanding the physical mechanisms of success and failure to guide algorithmic improvement. This physical language, in combination with uniqueness results related to unitarity, allow us to identify some potential pitfalls from kinetic energy fundamentally opposing the goal of optimization. This is connected to effects from wavefunction confinement, phase randomization, and shadow defects lurking in the objective far away from the ideal solution. As an example, we explore the surprising deficiency of many quantum methods in solving uncoupled spin problems and how this is both predictive of performance on some more complex systems while immediately suggesting simple resolutions. Further examination of canonical problems like the Hamming ramp or bush of implications show that entanglement can be strictly detrimental to performance results from the underlying mechanism of solution in approaches like QAOA. Kinetic energy and graph Laplacian perspectives provide new insights to common initialization and optimal solutions in QAOA as well as new methods for more effective layerwise training. Connections to classical methods of continuous extensions, homotopy methods, and iterated rounding suggest new directions for research in quantum optimization. Throughout, we unveil many pitfalls and mechanisms in quantum optimization using a physical perspective, which aim to spur the development of novel quantum optimization algorithms and refinements.

Fault Coverage Re-Evaluation of Memory Test Algorithms With Physical Memory Characteristics

A memory fault model (FM) is an abstraction of the physical mechanism of memory failure. When the physical failure mechanisms are not fully represented in FMs, the coverage of the FMs can be different from that of the failure mechanisms. However, it is impractical (or impossible) to model every electrical aspect of the failure mechanisms with one or more FMs. This problem has become even worse with emerging technologies. Thus, in this study, the fault coverage (FC) consequences are investigated when the physical memory characteristics are not properly linked to the FMs or even test algorithms. Three physical characteristics were considered for this exploration: electrical masking, address scrambling, and electrical neighborhoods. To this end, memory fault simulations were performed, and the test algorithms were re-evaluated in terms of FC. Simulations were performed on the 1 kB area of the example SRAM model; three classes of FMs (56 static faults (SFs), 126 dynamic faults (DFs), and 192 neighborhood pattern-sensitive faults (NPSFs)) were simulated for FC evaluation; and March MSS, March MD2, and March 12N were used to re-evaluate the FCs of SFs, DFs, and NPSFs, respectively. From the simulation results, we observed the negative impact of physical characteristics on FC. When masking was considered, FC reductions of 10.72% SFs and 9.52% DFs were observed; when address scrambling was not available, an FC reduction of 80.21% NPSFs was observed. Finally, considering electrical neighborhood changes depending on the physical memory structure, an FC reduction of 41.67% NPSFs was observed.

Influence of Microscopic Effects on the Static Tensile Strength of Gray Cast Iron HT200 Specimens

Gray cast iron HT200 material is a kind of pearlitic gray cast iron. Flake graphite in the gray cast iron greatly destroys the integrity of the matrix and affect the static strength. The influence of microscopic effects on the tensile static strength of gray cast iron HT200 specimens is investigated. The microstructures are observed by the scanning electron microscope. The failure tests are done under the static loads for the cylinder specimens of gray cast iron HT200. Then, an energy density zone (EDZ) model is applied to the simulation of the fracture process of the specimens. The energy density zone model is a macro/micro‐trans‐scale crack growth model that can depict a fracture process from an initial microdefect at the microscale to the final break at the macroscale. Three scale transitional functions as well as the size of the initial microdefect in the model represent the microscopic effects in a fracture process. Three scale transitional functions are speculated in view of the physical failure mechanisms. Two other material parameters in the model are determined from the test data. Thereby, the fracture process of gray cast iron specimens is numerically simulated, and the static strength values are calculated. The calculated values of static strength of gray cast iron specimens are identical to the test values. It is seen that the energy density zone model can accurately describe a fracture process of brittle materials like gray cast iron. In addition, the calculated results show that the microscopic effects did affect the static strength of gray cast material.

Creep Rupture: From Physical Failure Mechanisms to Lifetime Prediction of Structures

Creep Rupture: From Physical Failure Mechanisms to Lifetime Prediction of Structures

Failure mechanism of lead-free component boards in thermomechanical test based on recrystallization enhanced cracking

The purpose of this paper is to make a comprehensive understanding of the trends between the lifetime and the thermomechanical test setup parameters, based on the physical failure mechanism of SnAgCu solder interconnections in the component boards. The thermomechanical tests, including thermal cycling and thermal shock, were employed to investigate the failure of the component boards under different loading conditions. The in situ observation during the thermomechanical tests was employed to study the microstructure evolution and the crack propagation of solder interconnections. Electrical failures of the component boards under all setup conditions are mainly caused by the cracks, which propagate along the continuous network of the high-angle grain boundaries produced by the distinguished recrystallization of solder interconnections. The propagation of cracks is highly dependent on the expansion of the recrystallized volume in the solder interconnections. In addition, the expansion of the recrystallization would be influenced by the setup parameters of the thermomechanical test. The paper discusses the relationship among the four factors of the lifetime, the crack growth, the restoration phenomenon and the test setup parameters, in order to explain the complicated trend between the component board lifetime and the setup parameters.

The Leakage Mechanism of the Package of the AlGaN/GaN Liquid Sensor

Wide bandgap gallium nitride (GaN)-based devices have attracted a lot of attention in optoelectronics, power electronics, and sensing applications. AlGaN/GaN based sensors, featuring high-density and high-mobility two-dimensional electron gas (2DEG), have been demonstrated to be effective chemical sensors and biosensors in the liquid environment. One of the key factors limiting the wide adoption of the AlGaN/GaN liquid sensor is the package reliability issue. In this paper, the reliability of three types of sensor packaging materials (SiO2/Si3N4, PI, and SiO2/Si3N4/PI) on top of 5-μm metal are tested in Phosphate buffer saline (PBS) solution. By analyzing the I-V characteristics, it is found that the leakage currents within different regimes follow distinct leakage models, whereby the key factors limiting the leakage current are identified. Moreover, the physical mechanisms of the package failure are illustrated. The failure of the SiO2/Si3N4 package is due to its porous structure such that ions in the solution can penetrate into the packaging material and reduce its resistivity. The failure of the PI package at a relatively low voltage (<3 V) is mainly due to the poor adhesion of PI to the AlGaN surface such that the solution can reach the electrode by the “lateral drilling” effect. The SiO2/Si3N4/PI package achieves less than 10 μA leakage current at 5 V voltage stress because it combines the advantages of the SiO2/Si3N4 and the PI packages. The analysis in this work can provide guidelines for the design and failure mechanism analysis of packaging materials.

Estimation of lifespan of diesel locomotive engine

The operating time of a diesel locomotive indicates its mechanical states to a certain scale. Though, even the same variant of diesel locomotive with the same types of operating hours shows different technical states in different types of operative environments. Therefore, it is quite intricate to obtain the data for entire life or general life prediction methods are required to study the physical failure mechanism. In pursuant of the discussed cases, mathematical model of diesel locomotive’s life estimation with respect to downward trend and neural networks is evolved in the study. Primarily, the parameters corresponds to downward trends are selected and the sample data is to be standardized & then the principal component analysis method is being used to simplify different parameters to a pervasive parameter. Method of interpolation is being used for the parameter’s time series data as the train data of neural network. Finally, the life span estimation model of the locomotive engine based on neural network is evolved.

Lifetime of Diesel Locomotive With Respect To Degradation Data

The working time of a diesel engine reflects its technical states to a certain extent. Although, even the same kind of engine with the same kind of working hours exhibits different technical states in different kinds of working environments. At the same time, it is quite complicated to derive the complete life data or physical failure mechanism required by traditional life prediction method. In terms of the above cases, a model of diesel engine life prediction based on degradation data and neural networks is developed in the paper. First of all, the degradation parameters are selected and the sample data is to be standardized. After that the principal component analysis method is to be used to simplify different parameters to an extensive parameter. The interpolation method is applied to obtain the parameter’s time series data as the train data of neural network. At last, the life estimation model of the engine based on neural network is established.

Complex and Diverse Rupture Processes of the 2018 Mw 8.2 and Mw 7.9 Tonga‐Fiji Deep Earthquakes

AbstractDeep earthquakes exhibit strong variabilities in their rupture and aftershock characteristics, yet their physical failure mechanisms remain elusive. The 2018 Mw 8.2 and Mw 7.9 Tonga‐Fiji deep earthquakes, the two largest ever recorded in this subduction zone, occurred within days of each other. We investigate these events by performing waveform analysis, teleseismic P wave backprojection, and aftershock relocation. Our results show that the Mw 8.2 earthquake ruptured fast (4.1 km/s) and excited frequency‐dependent seismic radiation, whereas the Mw 7.9 earthquake ruptured slowly (2.5 km/s). Both events lasted ∼35 s. The Mw 8.2 earthquake initiated in the highly seismogenic, cold core of the slab and likely ruptured into the surrounding warmer materials, whereas the Mw 7.9 earthquake likely ruptured through a dissipative process in a previously aseismic region. The contrasts in earthquake kinematics and aftershock productivity argue for a combination of at least two primary mechanisms enabling rupture in the region.

Simulation-Informed Probabilistic Methodology for Common Cause Failure Analysis

Common Cause Failures (CCFs) are critical risk contributors in complex technological systems as they challenge multiple redundant systems simultaneously. To improve the CCF analysis in Probabilistic Risk Assessment (PRA), this research develops the Simulation-Informed Probabilistic Methodology (S-IPM) for CCFs. This new methodology utilizes simulation models of physical failure mechanisms to capture underlying causalities and to generate simulation-based data for the CCF probability estimation. To operationalize the S-IPM in PRA, a computational algorithm is developed that generates simulation-based estimates of CCF parameters and, using the Bayesian approach, integrates them with the data-driven CCF parameters (if relevant data available) from the existing PRA. This computational algorithm is equipped with the Probabilistic Validation that quantifies the degree of confidence in the simulation-based parameter estimates by characterizing and propagating epistemic uncertainty in multiple levels of analysis. The S-IPM can (i) provide more realistic CCF probability estimates by considering CCF data generated from simulations; (ii) reflect as-built, as-operated plant conditions, considering the updates in design, operational, and maintenance policies; and (iii) contribute to more effective prevention and mitigation of CCFs by providing “cause-specific” quantitative risk insights. The paper shows a case study that applies S-IPM to the CCFs of emergency service water pumps of NPPs.