An interpretable machine learning method, physics-informed genetic programming-based symbolic regression (P-GPSR), is integrated into a continuum thermodynamic approach to developing constitutive models. The proposed strategy for combining a thermodynamic analysis with P-GPSR is demonstrated by generating a yield function for an idealized material with voids, i.e., the Gurson yield function. First, a thermodynamic-based analysis is used to derive model requirements that are exploited in a custom P-GPSR implementation as fitness criteria or are strongly enforced in the solution. The P-GPSR implementation improved accuracy, generalizability, and training time compared to the same GPSR code without physics-informed fitness criteria. The yield function generated through the P-GPSR framework is in the form of a composite function that describes a class of materials and is characteristically more interpretable than GPSR-derived equations. The physical significance of the input functions learned by P-GPSR within the composite function is acquired from the thermodynamic analysis. Fundamental explanations of why the implemented P-GPSR capabilities improve results over a conventional GPSR algorithm are provided.
Accurate prediction of ductile failure is critical to Sandia’s NW mission, but the models are computationally heavy. The costs of including high-fidelity physics and mechanics that are germane to the failure mechanisms are often too burdensome for analysts either because of the person-hours it requires to input them or because of the additional computational time, or both. In an effort to deliver analysts a tool for representing these phenomena with minimal impact to their existing workflow, our project sought to develop modern data-driven methods that would add microstructural information to business-as-usual calculations and expedite failure predictions. The goal is a tool that receives as input a structural model with stress and strain fields, as well as a machine-learned model, and output predictions of structural response in time, including failure. As such, our project spent substantial time performing high-fidelity, three-dimensional experiments to elucidate materials mechanisms of void nucleation and evolution. We developed crystal-plasticity finite-element models from the experimental observations to enrich the findings with fields not readily measured. We developed engineering length-scale simulations of replicated test specimens to understand how the engineering fields evolve in the presence of fine-scale defects. Finally, we developed deep learning convolutional neural networks, and graph-based neural networks to encode the findings of the experiments and simulations and make forward predictions in time for structural performance. This project demonstrated the power of data-driven methods for model development, which have the potential to vastly increase both the accuracy and speed of failure predictions. These benefits and the methods necessary to develop them are highlighted in this report. However, many challenges remain to implementing these in real applications, and these are discussed along with potential methods for overcoming them.
The damage mechanisms that lead to failure in engineering alloys have been studied extensively, but converting this knowledge into constitutive models that are suitable for engineering-scale analysis remains a challenge. Evolution laws for continuum damage have been developed in the past and have proven effective but suffer from many non-physical assumptions that inhibit the overall accuracy of the model. Further, the assumptions inherent in these existing models prevent them from being applicable to a broad class of materials. At the same time, computational models of fine-scale damage mechanisms continue to advance making it tractable to generate large training data sets through computer simulation. Data-driven machine learning approaches can leverage these data sets to avoid making limiting assumptions, and instead produce models directly from the results of microstructural simulations and/or experiments. Many of these machine learning approaches are rapid and accurate, but they offer little to no insight into the underlying relationships among state variables being discovered. Conversely, genetic programming symbolic regression (GPSR) is a machine learning method that produces analytic expressions relating the state variables, allowing maximal insight and interpretability. To that end, we propose using GPSR as a data-driven method of obtaining microstructurally informed continuum damage models. Data is generated using microstructural simulations of damage evolution, parameterized over microstructural statistics (i.e., pore shape) and nominally applied deformations. Analytic expressions for damage evolution are obtained from the data using GPSR, and these expressions are then utilized within a continuum constitutive model. Overall, this approach is a promising method of automatically obtaining analytic relations describing constitutive phenomena in a material.
High pressure hydrogen storage vessels are frequently retired upon reaching their designed number of pressure cycles, even in cases where the in-use pressure cycles are significantly less severe than the design pressure cycle. One method for extending the life of hydrogen vessels is recertification through non-destructive evaluation (NDE); however, NDE techniques are frequently evaluated with machined defects in test samples rather than fatigue cracks which occur during pressure cycling and may be more difficult to detect. In this paper, 50 mm wide ring sections (called C-rings, since they represent slightly more than half the circumference) were extracted from pressure vessels and mechanically cycled to establish fatigue cracks. Sub-millimeter starter notches were machined, via plunge electrical discharge machining (EDM), to control the location of crack initiation. Crack growth was monitored via direct current potential difference (DCPD) and backface strain gauges, both of which were shown to be good indicators for crack propagation. The C-ring geometry and fatigue crack growth were modeled to demonstrate the ability to monitor/control the crack length and area, which can be used to develop calibration samples of varying crack depth for NDE techniques. Additionally, this sample is intended to evaluate the influence of residual stresses on the sensitivity of NDE techniques, such as the design stresses in autofrettaged vessels.
Type 2 high-pressure hydrogen vessels for storage at hydrogen refueling stations are designed assuming a predefined operational pressure cycle and targeted autofrettage conditions. However, the resulting finite life depends significantly on variables associated with the autofrettage process and the pressure cycles actually realized during service, which many times are not to the full range of the design. Clear guidance for cycle counting is lacking, therefore industry often defaults to counting every repressurization as a full range pressure cycle, which is an overly conservative approach. In-service pressure cycles used to predict the growth of cracks in operational pressure vessels results in significantly longer life, since most in-service pressure cycles are only a fraction of the full design pressure range. Fatigue crack growth rates can vary widely for a given pressure range depending on the details of the residual strains imparted during the autofrettage process because of their influence on crack driving forces. Small changes in variables associated with the autofrettage process, e.g., the target autofrettage overburden pressure, can result in large changes in the residual stress profile leading to possibly degraded fatigue life. In this paper, computational simulation was used for sensitivity studies to evaluate the effect of both operating conditions and autofrettage conditions on fatigue life for Type 2 highpressure hydrogen vessels. The analysis in this paper explores these sensitivities, and the results are used to provide guidance on cycle counting. In particular, we identify the pressure cycle ranges that can be ignored over the life of the vessel as having negligible effect on fatigue life. This study also examines the sensitivity of design life to the autofrettage process and the impact on life if the targeted residual strain is not achieved during manufacturing.
High pressure Type 2 hoop-wrapped, thick-walled vessels are commonly used at hydrogen refueling stations. Vessels installed at stations circa 2010 are now reaching their design cycle limit and are being retired, which is the motivation for exploring life extension opportunities. The number of design cycles is based on a fatigue life calculation using a fracture mechanics assessment according to ASME Section VIII, Division 3, which assumes each cycle is the full pressure range identified in the User's Design Specification for a given pressure vessel design; however, assessment of service data reveals that the actual pressure cycles are more conservative than the design specification. A case study was performed in which in-service pressure cycles were used to re-calculate the design cycles. It was found that less than 1% of the allowable crack extension was consumed when crack growth was assessed using in-service design pressures compared to the original design fatigue life from 2010. Additionally, design cycles were assessed on the 2010 era vessels based on design curves from the recently approved ASME Code Case 2938, which were based on fatigue crack growth rate relationships over a broader range of K. Using the Code Case 2938 design curves yielded nearly 2.7 times greater design cycles compared to the 2010 vessel original design basis. The benefits of using inservice pressure cycles to assess the design life and the implications of using the design curves in Code Case 2938 are discussed in detail in this paper.
In this study, a multiscale electron microscopy-based approach is applied to understanding how different aspects of the microstructure in a notched AA6061-T6, including grain boundaries, triple junctions, and intermetallic particles, promote localized dislocation accumulation as a function of applied tensile strain and depth from the sample surface. Experimental measurements and crystal plasticity simulations of dislocation distributions as a function of distance from specified microstructural features both showed preferential dislocation accumulation near intermetallic particles relative to grain boundaries and triple junctions. High resolution electron backscatter diffraction and site-specific transmission electron microscopy characterization showed that high levels of dislocation accumulation near intermetallic particles led to the development of an ultrafine sub-grain microstructure, indicative of a much higher level of local plasticity than predicted from the coarser measurements and simulations. In addition, high resolution measurements in front of a crack tip suggested a compounding influence of intermetallic particles and grain boundaries in dictating crack propagation pathways.
We describe an approach to predict failure in a complex, additively-manufactured stainless steel part as defined by the third Sandia Fracture Challenge. A viscoplastic internal state variable constitutive model was calibrated to fit experimental tension curves in order to capture plasticity, necking, and damage evolution leading to failure. Defects such as gas porosity and lack of fusion voids were represented by overlaying a synthetic porosity distribution onto the finite element mesh and computing the elementwise ratio between pore volume and element volume to initialize the damage internal state variables. These void volume fraction values were then used in a damage formulation accounting for growth of these existing voids, while new voids were allowed to nucleate based on a nucleation rule. Blind predictions of failure are compared to experimental results. The comparisons indicate that crack initiation and propagation were correctly predicted, and that an initial porosity field superimposed as higher initial damage may provide a path forward for capturing material strength uncertainty. The latter conclusion was supported by predicted crack face tortuosity beyond the usual mesh sensitivity and variability in predicted strain to failure; however, it bears further inquiry and a more conclusive result is pending compressive testing of challenge-built coupons to de-convolute materials behavior from the geometric influence of significant porosity.
Joining technologies such as welds, adhesives, and bolts are nearly ubiquitous and often lead to concentrated stresses, making them key in analyzing failure of a structure. While high-fidelity models for fasteners have been developed, they are impractical for use in a full system or component analyses, which may involve hundreds of fasteners undergoing mixed loading. Other failure models for fasteners which use specialized boundary conditions, e.g., spot welds, do well in replicating the load-displacement response of a fastener in a mesh independent manner, but are limited in their ability to transmit a bending moment and require constitutive assumptions when there is a lack of experimental data. A reduced-order finite element model using cohesive surface elements to model fastener failure is developed. A cohesive zone allows for more explicitly representing the fracture of the fastener, rather than simply specifying a load-displacement relationship between two surfaces as in the spot weld. This fastener model is assessed and calibrated against tensile and shear loading data and compared to a traditional spot weld approach. The cohesive zone model can reproduce the experimental data, demonstrating its viability as a reduced-order model of fastener behavior.
Partial penetration laser welds join metal surfaces without additional filler material, providing hermetic seals for a variety of components. The crack-like geometry of a partial penetration weld is a local stress riser that may lead to failure of the component in the weld. Computational modeling of laser welds has shown that the model should include damage evolution to predict the large deformation and failure. We have performed interrupted tensile experiments both to characterize the damage evolution and failure in laser welds and to aid computational modeling of these welds. Several EDM-notched and laser-welded 304L stainless steel tensile coupons were pulled in tension, each one to a different load level, and then sectioned and imaged to show the evolution of damage in the laser weld and in the EDM-notched parent 304L material (having a similar geometry to the partial penetration laser-welded material). SEM imaging of these specimens revealed considerable cracking at the root of the laser welds and some visible micro-cracking in the root of the EDM notch even before peak load was achieved in these specimens. The images also showed deformation-induced damage in the root of the notch and laser weld prior to the appearance of the main crack, though the laser-welded specimens tended to have more extensive damage than the notched material. These experiments show that the local geometry alone is not the cause of the damage, but also microstructure of the laser weld, which requires additional investigation.
Sealing glasses are ubiquitous in high pressure and temperature engineering applications, such as hermetic feed-through electrical connectors. A common connector technology are glass-to-metal seals where a metal shell compresses a sealing glass to create a hermetic seal. Though finite-element analysis has been used to understand and design glass-to-metal seals for many years, there has been little validation of these models. An indentation technique was employed to measure the residual stress on the surface of a simple glass-to-metal seal. Recently developed rate- dependent material models of both Schott 8061 and 304L VAR stainless steel have been applied to a finite-element model of the simple glass-to-metal seal. Model predictions of residual stress based on the evolution of material models are shown. These model predictions are compared to measured data. Validity of the finite- element predictions is discussed. It will be shown that the finite-element model of the glass-to-metal seal accurately predicts the mean residual stress in the glass near the glass-to-metal interface and is valid for this quantity of interest.
Microstructural variabilities are among the predominant sources of uncertainty in structural performance and reliability. We seek to develop efficient algorithms for multiscale calcu- lations for polycrystalline alloys such as aluminum alloy 6061-T6 in environments where ductile fracture is the dominant failure mode. Our approach employs concurrent multiscale methods, but does not focus on their development. They are a necessary but not sufficient ingredient to multiscale reliability predictions. We have focused on how to efficiently use concurrent models for forward propagation because practical applications cannot include fine-scale details throughout the problem domain due to exorbitant computational demand. Our approach begins with a low-fidelity prediction at the engineering scale that is sub- sequently refined with multiscale simulation. The results presented in this report focus on plasticity and damage at the meso-scale, efforts to expedite Monte Carlo simulation with mi- crostructural considerations, modeling aspects regarding geometric representation of grains and second-phase particles, and contrasting algorithms for scale coupling.
Traditional singularity-based fracture mechanics theories rely on their ability to infer the crack tip driving force (local field) by surveying macroscopic physical magnitudes far from the crack tip (far field). This key capability allows engineers to employ nominal forces or displacements to estimate the potential for stable or unstable crack growth. In the case of heterogeneous or anisotropic materials, traditional fracture approaches are not fully theoretically sound and applications rely on extrapolating methodologies with ad-hoc corrections. This Express Laboratory Directed Research and Development (ELDRD) program employed mesoscale-sensitive finite element simulations to assess the impact of grain size and texture on the crack tip behavior. A dislocation-based crystal plasticity model conveys grain size effects by computing the constraint on dislocation cell structures. We assessed the effects of microstructural variability on multiple displacement-based measurements of the fracture driving forces for crack opening (Mode I) and sliding (Mode II). We also consider multiple microstructural realizations of single phase metals undergoing ductile failure. The results show that grain size and texture affect the applied fracture driving force and can induce a significant Mode II deformation under force and displacement control, which is completely neglected in homogeneous models. A large variability in driving forces upon identical far field applied conditions is attributed to a buffering effects of the microstructure. Furthermore, crack mouth opening displacement is almost insensitive to microstructure, which suggests that experimental measurements using such a magnitude (e.g., plastic hinge model) may underestimate local crack tip driving force variability.
A Bayesian framework is developed for characterizing the unknown parameters of probabilistic models for material properties. In this framework, the unknown parameters are viewed as random and described by their posterior distributions obtained from prior information and measurements of quantities of interest that are observable and depend on the unknown parameters. The proposed Bayesian method is applied to characterize an unknown spatial correlation of the conductivity field in the definition of a stochastic transport equation and to solve this equation by Monte Carlo simulation and stochastic reduced order models (SROMs). The Bayesian method is also employed to characterize unknown parameters of material properties for laser welds from measurements of peak forces sustained by these welds.
Two fundamental approximations in macroscale solid-mechanics modeling are (1) the assumption of scale separation in homogenization theory and (2) the use of a macroscopic plasticity material model that represents, in a mean sense, the multitude of inelastic processes occurring at the microscale. With the goal of quantifying the errors induced by these approximations on engineering quantities of interest, we perform a set of direct numerical simulations (DNS) in which polycrystalline microstructures are embedded throughout a macroscale structure. The largest simulations model over 50,000 grains. The microstructure is idealized using a randomly close-packed Voronoi tessellation in which each polyhedral Voronoi cell represents a grain. An face centered cubic crystal-plasticity model is used to model the mechanical response of each grain. The overall grain structure is equiaxed, and each grain is randomly oriented with no overall texture. The detailed results from the DNS simulations are compared to results obtained from conventional macroscale simulations that use homogeneous isotropic plasticity models. The macroscale plasticity models are calibrated using a representative volume element of the idealized microstructure. Ultimately, we envision that DNS modeling will be used to gain new insights into the mechanics of material deformation and failure.
The stochastic collocation (SC) and stochastic Galerkin (SG) methods are two well-established and successful approaches for solving general stochastic problems. A recently developed method based on stochastic reduced order models (SROMs) can also be used. Herein we provide a comparison of the three methods for some numerical examples; our evaluation only holds for the examples considered in the paper. The purpose of the comparisons is not to criticize the SC or SG methods, which have proven very useful for a broad range of applications, nor is it to provide overall ratings of these methods as compared to the SROM method. Furthermore, our objectives are to present the SROM method as an alternative approach to solving stochastic problems and provide information on the computational effort required by the implementation of each method, while simultaneously assessing their performance for a collection of specific problems.
Cracks in glass-to-metal seals can be a threat to the hermeticity of isolated electronic components. Design and manufacturing of the materials and processes can be tailored to minimize the residual stresses responsible for cracking. However, this requires high fidelity material modeling accounting for the plastic strains in the metals, mismatched thermal shrinkage and property changes experienced as the glass solidifies during cooling of the assembly in manufacturing. Small plastic strains of just a few percent are typical during processing of glass-to-metal seals and yet can generate substantial tensile stresses in the glass during elastic unloading in thermal cycling. Therefore, experimental methods were developed to obtain very accurate measurements of strain near and just beyond the proportional limit. Small strain tensile characterization experiments were conducted with varying levels and rates of strain ratcheting over the temperatures range of -50 to 550 °C, with particular attention near the glass transition temperature of 500 °C. Additional experiments were designed to quantify the effects of stress relaxation and reloading. The experimental techniques developed and resulting data will be presented. Details of constitutive modeling efforts and glass material experiments and modeling can be found in Chambers et al. (Characterization & modeling of materials in glass-to-metal seals: Part I. SAND14-0192. Sandia National Laboratories, January 2014).
A simple, mode-mixity dependent toughness cohesive zone model (MDGc CZM) is described. This phenomenological cohesive zone model has two elements. Mode I energy dissipation is defined by a traction–separation relationship that depends only on normal separation. Mode II (III) dissipation is generated by shear yielding and slip in the cohesive surface elements that lie in front of the region where mode I separation (softening) occurs. The nature of predictions made by analyses that use the MDGc CZM is illustrated by considering the classic problem of an elastic layer loaded by rigid grips. This geometry, which models a thin adhesive bond with a long interfacial edge crack, is similar to that which has been used to measure the dependence of interfacial toughness on crack-tip mode-mixity. The calculated effective toughness vs. applied mode-mixity relationships all display a strong dependence on applied mode-mixity with the effective toughness increasing rapidly with the magnitude of the mode-mixity. The calculated relationships also show a pronounced asymmetry with respect to the applied mode-mixity. As a result, this dependence is similar to that observed experimentally, and calculated results for a glass/epoxy interface are in good agreement with published data that was generated using a test specimen of the same type as analyzed here.
The material characterization tests conducted on 304L VAR stainless steel and Schott 8061 glass have provided higher fidelity data for calibration of material models used in Glass - To - Metal (GTM) seal analyses. Specifically, a Thermo - Multi - Linear Elastic Plastic (thermo - MLEP) material model has be endefined for S S304L and the Simplified Potential Energy Clock nonlinear viscoelastic model has been calibrated for the S8061 glass. To assess the accuracy of finite element stress analyses of GTM seals, a suite of tests are proposed to provide data for comparison to model predictions.