Deep Learning Based Reliability Models For High Dimensional Data

The reliability estimation of products has crucial applications in various industries, particularly in current competitive markets, as it has high economic impacts. Hence, reliability analysis and failure prediction are receiving increasing attention. Reliability models based on lifetime data have been developed for different modern applications. These models are able to predict failure by incorporating the influence of covariates on time-to-failure. The covariates are factors that affect the subjects’ lifetime. Modern technologies generate covariates which can be utilized to improve failure time prediction. However, there are several challenges to incorporate the covariates into reliability models. First, the covariates generally are high dimensional and topologically complex. Second, the existing reliability models are not efficient in modeling the effect on the complex covariates on failure time. Third, failure time information may not be available for all covariates, as collecting such information is a costly and time-consuming process. To overcome the first challenge, we propose a statistical approach to model the complex data. The proposed model generalizes penalized logistic regression to capture the spatial properties of the data. An efficient parameter estimation method is developed to make the model practical in case of large sample sizes. To tackle the second challenge, a deep learning-based reliability model is proposed. The model can capture the complex effect of the data on failure time. A novel loss function based on the partial likelihood function is developed to train the deep learning model. Furthermore, to overcome the third difficulty, we proposed a transfer learning-based reliability model to estimate failure time based on the failure time of similar covariates. The proposed model is based on a two-level autoencoder to minimize the distribution distance of covariates. A new parameter estimation method is developed to estimate the parameter of the proposed two-level autoencoder model. Various simulation studies are conducted to demonstrate the proposed models. The results show that the proposed models outperformed the traditional statistical and reliability models. Moreover, physical experiments on advanced high strength steel are designed to demonstrate the proposed model. As microstructure images of the steels affect the failure time of the steel, the images are considered as covariates. The results show that the proposed models predict the failure time and hazard function of the materials more accurately than existing reliability models

Conference proceedings: Thermo-mechanical processing of Steels & 5th Gleeble User Workshop India

To bring together the national experts, academia, R&D establishments, industries and students on a common platform for learning, sharing and updating the latest developments in the area of thermo-mechanical processing of steels. To provide a platform for Gleeble users in India to discuss the Gleeble related applications, operations and maintenance issues

Reliability Analysis By Considering Steel Physical Properties

Most customers today are pursuing engineering materials (e.g., steel) that not only can achieve their expected functions but also are highly reliable. As a result, reliability analysis of materials has been receiving increasing attention over the past few decades. Most existing studies in the reliability engineering field focus on developing model-based and data-driven approaches to analyze material reliability based on material failure data such as lifetime data and degradation data, without considering effects of material physical properties. Ignoring such effects may result in a biased estimation of material reliability, which in turn could incur higher operation or maintenance costs. Recently, with the advancement of sensor technology more information/data concerning various physical properties of materials are accessible to reliability researchers. In this dissertation, considering the significant impacts of steel physical properties on steel failures, we propose systematic methodologies for steel reliability analysis by integrating a set of steel physical properties. Specifically, three steel properties of various scales are considered: 1) a macro-scale property called overload retardation; 2) a local-scale property called dynamic local deformation; and 3) a micro-scale property called microstructure effect. For incorporating property 1), a novel physical-statistical model is proposed based on a modification of the current Paris law. To incorporate property 2), a novel statistical model named multivariate general path model is proposed, which is a generalization of an existing univariate general path model. For the integration of property 3), a novel statistical model named distribution-based functional linear model is proposed, which is a generalization of an existing functional linear model. Theoretical property analyses and statistical inferences of these three models are intensively developed. Various simulation studies are implemented to verify and illustrate the proposed methodologies. Multiple physical experiments are designed and conducted to demonstrate the proposed models. The results show that, through the integration of the aforementioned three steel physical properties, a significant improvement of steel reliability assessment is achieved in terms of failure prediction accuracy compared to traditional reliability studies

Meso-scale modelling of deformation, damage and failure in dual phase steels

Advanced high strength steels (AHSS), such as dual phase (DP) and transformation induced plasticity (TRIP) steels, o er high ductility, formability, and strength, as well as high strength-to-weight ratio and improved crash resistance. Dual phase steels belong to a family of high strength grades which consist of martensite, responsible for strengthening, distributed in a ductile ferrite matrix which accommodates the deformation throughout the forming process. It has been shown that the predominant damage mechanism and failure in DP steels depends on the ferrite and martensite grain sizes and their morphology, and can range from a mixture of brittle and ductile rupture to completely ductile rupture in a quasi-static uniaxial tension test. In this study, a hybrid nite element cellular automata model, initially proposed by Anton Shterenlikht (2003), was developed to evaluate the forming behaviour and predict the onset of instability and damage evolution in a dual phase steel. In this model, the nite element constitutive model is used to represent macro-level strain gradients and a damage variable, and two di erent cell arrays are designed to represent the ductile and brittle fracture modes in meso-scale. In the FE part of the model, a modi ed Rousselier ductile damage model is developed to account for nucleation, growth and coalescence of voids. Also, several rate-dependent hardening models were developed and evaluated to describe the work hardening ow curve of DP600. Based on statistical analysis and simulation results, a modi ed Johnson-Cook (JC) model and a multiplicative combination of the Voce-modi ed JC functions were found to be the most accurate hardening models. The developed models were then implemented in a user-de ned material subroutine (VUMAT) for ABAQUS/Explicit nite element simulation software to simulate uniaxial tension tests at strain rates ranging from 0.001s-1to 1000s-1, Marciniak tests, and electrohydraulic free-forming (EHFF). The modi ed Rousselier model could successfully predict the dynamic behaviour, the onset of instability and damage progress in DP600 tensile test specimens. Also, the forming limit curve (FLC) as well as the nal damage geometry in DP600 Marciniak specimens was successfully predicted and compared with experiments. A hybrid FE+CA model was utilized to predict the major fracture mode of DP600 and DP780 sheet specimens under di erent deformation conditions. This hybrid model is able to predict quasi-cleavage fracture in ultra- ne and coarse-grained DP600 and DP780 at low and high strain rates. The numerical results showed the capabilities of the proposed model to predict that higher martensite volume fraction, greater ferrite grain sizes and higher strain rates promote the brittle fracture mechanism whereas ner grain sizes and higher temperature alter the dominant fracture mechanism to ductile mode

Optimisation de la microstructure d'aciers ferrito-martensitiques à 3.5 % pds Mn : des transformations de phases à la micro-mécanique

Ferrite-martensite dual-phase (DP) steels have been widely used in automotiveindustry due to their excellent mechanical properties, such as high work-hardeningrate and a good compromise between strength and ductility allowing high energyabsorbing performance. In order to fully exploit the potential of DP steels and extendthe application, the dual-phase microstructure has to be optimized for bettercombination of strength and formability that is characterized by uniform strainand/or fracture strain. As a starting point, detailed literature review is made on themicrostructure development and mechanical properties of DP steels, and the keyfactors controlling microstructural features and determining mechanical propertiesare identified. Through experimental investigation, microstructures are developed inorder to decouple the effects of various microstructural features, and themicrostructure—mechanical properties relationship is systematically studied.Micromechanical modeling is used to further understand the experimental resultswithin a quantitative framework, and to provide a support for microstructurerefinement of DP steels by parametric study. Strategies of designing DP steels tofulfill specific forming operation have been proposed, and the concept of DP steelswith graded martensite islands has been discussed with FEM analysis as a possibilityof improving strength—formability trade-off.Les aciers Dual-Phase sont largement utilisés dans le secteur de l’automobile enraison de leurs propriétés mécaniques remarquables et du bon compromis résistanceductilité qui lui donne d’intéressante potentialités comme absorbeur d’énergiemécanique. Cependant, la recherche de bons compromis entre les propriétésmécaniques en traction et celles de formabilité nécessite une optimisation desparamètres microstructuraux. Ce travail de thèse s’inscrit dans cet optique. Dans unepremière partie, l’étude bibliographique proposée permet de mieux cerner lesparamètres influençant la formation des microstructures ainsi que les propriétés desaciers DP. Dans une seconde partie, nous proposons un travail expérimental originalpermettant de mieux comprendre la formation des microstructures des aciers DP etde découpler l’effet de certains paramètres microstructuraux sur les propriétés deces aciers. Enfin, la modélisation micromécanique proposée permet de compléter etd’interpréter les données expérimentales acquises. Ce travail ouvre des voiesintéressantes de « design » des microstructures des aciers DP en vue de développerdes aciers de nouvelles générations possédant des propriétés optimisées

Additive manufacturing of thin-walled SS316L-IN718 functionally graded materials by direct laser metal deposition

Functionally graded materials (FGMs) are a good response to those advanced applications that service requirements are diverse and require high performance. Additive manufacturing (AM) technology, with its many advantages, including high flexibility for complex geometries and near-net-shape integration, has attracted special attention in the development of FGMs. In this research, the solidification behavior and microstructure evolution in the laser additive manufacturing of thin-walled stainless steel 316L-Inconel 718 graded materials have been studied with the help of solidification concepts in the welding metallurgy, according to the common principles of welding and additive manufacturing processes. For this purpose, optical and electron microscopy techniques, X-ray energy dispersive spectroscopy, and microhardness measurement were used along the build direction of FGMs with different transition designs. Microstructure evaluation showed that due to re-melting of layers, despite the increased undercooling in the build direction, morphological evolution occasionally occurred periodically between solidification modes, and due to thermal accumulation, a coarser microstructure is formed in the final layers. In addition, in the chemical analysis, it was observed that the mixing of adjacent layers caused by dilution led to a deviation of the composition distribution from the desired design. Also, the microsegregation of some elements during the non-equilibrium solidification of the process caused secondary phases such as carbides and intermetallic compound of Laves, which can have an adverse effect on the mechanical properties of the structure. However, microhardness variations along the cross-section of the samples showed that the gradation of the dissimilar thin-walled structure can effectively bring the properties and behavior of adjacent layers closer together and therefore be very useful in improving the service life

Microstructure-sensitive fatigue modeling of heat treated and shot peened martensitic gear steels

High strength secondary hardening lath martensitic steel is a strong candidate for high performance and reliable transmission systems in aircraft and automotives. The fatigue resistance of this material depends both on intrinsic microstructure attributes, such as fine scale (M2C) precipitates, and extrinsic attributes such as nonmetallic primary inclusions. Additionally, the aforementioned attributes are affected by processing history. The objective of this research is to develop a computational framework to quantify the influence of both extrinsic (primary inclusions and residual stresses) and intrinsic (martensite laths and carbides) microstructure attributes on fatigue crack formation and the early stage of microstructurally small crack (MSC) growth that dominate high cycle fatigue (HCF) lifetime. To model the fatigue response at various microstructure scales, a hierarchical approach is adopted. A simplified scheme is developed to simulate processing effects such as shot peening that is suitable to introduce representative residual stresses prior to conducting fatigue calculations. Novel strategies are developed to couple process route (residual stresses) and microstructure scale response for comprehensive analysis of fatigue potency at critical life-limiting primary inclusions in gear steels. Relevant microstructure-scale response descriptors that permit relative assessment of fatigue resistance are identified. Fatigue crack formation and early growth is highly heterogeneous at the grain scale. Hence, a scheme for physically-based constitutive models that is suitable to investigate crack formation and early growth in martensitic steel is introduced and implemented. An extreme value statistical/probabilistic framework to assess the influence of variability of various microstructure attributes such as size and spatial distribution of primary inclusions on minimum fatigue crack formation life is devised. Understanding is sought regarding the relative role of microstructure attributes in the HCF process, thereby providing a basis to modify process route and/or composition to enhance fatigue resistance. Parametric studies are conducted to assess the effect of hot isostatic pressing and introduction of compliant coatings at debonded inclusion-matrix interface on enhancement of fatigue resistance. A comprehensive set of 3D computational tools and algorithms for hierarchical microstructure-sensitive fatigue analysis of martensitic gear steels is developed as an outcome of this research; such tools and methodologies will lend quantitative and qualitative support to designing improved, fatigue-resistant materials and accelerating insertion of new or improved materials into service.Ph.D.Committee Chair: David L. McDowell; Committee Member: G. B. Olson; Committee Member: K. A. Gall; Committee Member: Min Zhou; Committee Member: R. W. Ne

Statistical models for prediction of mechanical property and manufacturing process parameters for gas pipeline steels

abstract: Pipeline infrastructure forms a vital aspect of the United States economy and standard of living. A majority of the current pipeline systems were installed in the early 1900’s and often lack a reliable database reporting the mechanical properties, and information about manufacturing and installation, thereby raising a concern for their safety and integrity. Testing for the aging pipe strength and toughness estimation without interrupting the transmission and operations thus becomes important. The state-of-the-art techniques tend to focus on the single modality deterministic estimation of pipe strength and do not account for inhomogeneity and uncertainties, many others appear to rely on destructive means. These gaps provide an impetus for novel methods to better characterize the pipe material properties. The focus of this study is the design of a Bayesian Network information fusion model for the prediction of accurate probabilistic pipe strength and consequently the maximum allowable operating pressure. A multimodal diagnosis is performed by assessing the mechanical property variation within the pipe in terms of material property measurements, such as microstructure, composition, hardness and other mechanical properties through experimental analysis, which are then integrated with the Bayesian network model that uses a Markov chain Monte Carlo (MCMC) algorithm. Prototype testing is carried out for model verification, validation and demonstration and data training of the model is employed to obtain a more accurate measure of the probabilistic pipe strength. With a view of providing a holistic measure of material performance in service, the fatigue properties of the pipe steel are investigated. The variation in the fatigue crack growth rate (da/dN) along the direction of the pipe wall thickness is studied in relation to the microstructure and the material constants for the crack growth have been reported. A combination of imaging and composition analysis is incorporated to study the fracture surface of the fatigue specimen. Finally, some well-known statistical inference models are employed for prediction of manufacturing process parameters for steel pipelines. The adaptability of the small datasets for the accuracy of the prediction outcomes is discussed and the models are compared for their performance.Dissertation/ThesisDoctoral Dissertation Materials Science and Engineering 201