Stochastic Energy-based Fatigue Life Prediction Framework Utilizing Bayesian Statistical Inference PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Stochastic Energy-based Fatigue Life Prediction Framework Utilizing Bayesian Statistical Inference PDF full book. Access full book title Stochastic Energy-based Fatigue Life Prediction Framework Utilizing Bayesian Statistical Inference by Dino Anthony Celli. Download full books in PDF and EPUB format.
Author: Dino Anthony Celli Publisher: ISBN: Category : Additive manufacturing Languages : en Pages : 0
Book Description
The fatigue life prediction framework developed and described in the proceeding chapters can concurrently approximate both typical stress versus cycle (SN) behavior as well as the inherent variability of fatigue using a limited amount of experimental data. The purpose of such a tool is for the rapid verification and quality assessment of cyclically loaded components with a limited knowledge-base or available fatigue data in the literature. This is motivated by the novelty of additive manufacturing (AM) processes and the necessity of part-specific structural assessment. Interest in AM technology is continually growing in many industries such as aerospace, automotive, or bio-medical but components often result in highly variable fatigue performance. The determination of optimal process parameters for the build process can be an extensive and costly endeavor due to either a limited knowledge-base or proprietary restrictions. Quantifying the significant variability of fatigue performance in AM components is a challenging task as there are many underlying causes including machine-to-machine differences, recycles of powder, and process parameter selection. Therefore, a life prediction method which can rapidly determine the fatigue performance of a material with little or no prior information of the material and a limited number of experimental tests is developed as an aid in AM process parameter optimization and fatigue performance qualification. Predicting fatigue life requires the use of a previously developed and simplistic energy-based method, or Two-Point method, to generate a collection of life predictions. Then the collected life predictions are used to approximate key statistical descriptions of SN fatigue behavior. The approximated fatigue life distributions are validated against an experimentally found population of SN data at 10^4 and 10^6 cycles failure describing low cycle and high cycle fatigue. A Monte Carlo method is employed to model fatigue life by first modeling SN distributions at discrete stress amplitudes using the predicted fatigue life curves. Then the distributions are randomly sampled and a life prediction model is obtained. The approach is verified by using Aluminum 6061 data due to ample material characterization and previous life prediction analysis available in literature. SN life prediction is modeled via a Random Fatigue Limit (RFL) model using least square regression to determine the model coefficients. The life prediction framework is further developed by incorporating Bayesian statistical inference and stochastic sampling techniques to estimate the RFL model parameters. In addition, digital image correlation (DIC) is leveraged during experimentation to collect hysteresis energy as a novel method to monitor hysteresis strain energy or the assumed critical damage variable. Fatigue life prediction is performed in a dynamic way such that the life prediction model is continually updated with the generation of experimental data. The life prediction framework is applied to conventional Aluminum 6061-T6 and AM Inconel 718 and Titanium 6Al-4V. The framework is validated for life prediction and forecasting SN high cycle fatigue behavior using only low cycle fatigue data. The culmination of this work enables the rapid characterization of fatigue of AM materials by concurrently approximating the variation of fatigue life as well as high cycle fatigue behavior with low cycle fatigue data. The benefit of this framework is the significant reduction in experimental testing time, effort, and cost necessary to accurately assess the fatigue behavior of materials with limited prior information and specimen availability, such as in the case with AM Alloys.
Author: Dino Anthony Celli Publisher: ISBN: Category : Additive manufacturing Languages : en Pages : 0
Book Description
The fatigue life prediction framework developed and described in the proceeding chapters can concurrently approximate both typical stress versus cycle (SN) behavior as well as the inherent variability of fatigue using a limited amount of experimental data. The purpose of such a tool is for the rapid verification and quality assessment of cyclically loaded components with a limited knowledge-base or available fatigue data in the literature. This is motivated by the novelty of additive manufacturing (AM) processes and the necessity of part-specific structural assessment. Interest in AM technology is continually growing in many industries such as aerospace, automotive, or bio-medical but components often result in highly variable fatigue performance. The determination of optimal process parameters for the build process can be an extensive and costly endeavor due to either a limited knowledge-base or proprietary restrictions. Quantifying the significant variability of fatigue performance in AM components is a challenging task as there are many underlying causes including machine-to-machine differences, recycles of powder, and process parameter selection. Therefore, a life prediction method which can rapidly determine the fatigue performance of a material with little or no prior information of the material and a limited number of experimental tests is developed as an aid in AM process parameter optimization and fatigue performance qualification. Predicting fatigue life requires the use of a previously developed and simplistic energy-based method, or Two-Point method, to generate a collection of life predictions. Then the collected life predictions are used to approximate key statistical descriptions of SN fatigue behavior. The approximated fatigue life distributions are validated against an experimentally found population of SN data at 10^4 and 10^6 cycles failure describing low cycle and high cycle fatigue. A Monte Carlo method is employed to model fatigue life by first modeling SN distributions at discrete stress amplitudes using the predicted fatigue life curves. Then the distributions are randomly sampled and a life prediction model is obtained. The approach is verified by using Aluminum 6061 data due to ample material characterization and previous life prediction analysis available in literature. SN life prediction is modeled via a Random Fatigue Limit (RFL) model using least square regression to determine the model coefficients. The life prediction framework is further developed by incorporating Bayesian statistical inference and stochastic sampling techniques to estimate the RFL model parameters. In addition, digital image correlation (DIC) is leveraged during experimentation to collect hysteresis energy as a novel method to monitor hysteresis strain energy or the assumed critical damage variable. Fatigue life prediction is performed in a dynamic way such that the life prediction model is continually updated with the generation of experimental data. The life prediction framework is applied to conventional Aluminum 6061-T6 and AM Inconel 718 and Titanium 6Al-4V. The framework is validated for life prediction and forecasting SN high cycle fatigue behavior using only low cycle fatigue data. The culmination of this work enables the rapid characterization of fatigue of AM materials by concurrently approximating the variation of fatigue life as well as high cycle fatigue behavior with low cycle fatigue data. The benefit of this framework is the significant reduction in experimental testing time, effort, and cost necessary to accurately assess the fatigue behavior of materials with limited prior information and specimen availability, such as in the case with AM Alloys.
Author: Casey M. Holycross Publisher: ISBN: Category : Languages : en Pages :
Book Description
An improved fatigue life prediction method has been developed for Al 6061-T6511 test specimens using strain energy density as the criteria for assessing fatigue strength of plain and notched geometries at various stress ratios and loading spectra for cyclic lives from 10 to 105. The approach features interrogation at continuum and mesoscales using both a traditional fracture mechanics approach and a newly developed experimental procedure to determine strain energy density about machined notch roots in situ using digital image correlation. Testing revealed a critical strain energy density value independent of load ratio, notch geometry, and the effects of localized plasticity, indicating a new material dependent quantity to assess cyclic damage. The method better predicts lifetimes in low cycle fatigue than previously developed approaches, and has inherent capability to describe an endurance limit phenomenon. This study constitutes the most comprehensive investigation of strain energy density within the framework of the energy based fatigue life prediction method developed by Scott-Emuakpor et al., offering significant insight into cyclic damage behavior for all practical length scales and lifetimes.
Author: John Nicholas Wertz Publisher: ISBN: Category : Languages : en Pages : 123
Book Description
Abstract: Substantial progress has been made in advancing a pre-existing energy-based fatigue life prediction method into a powerful tool for real-world application through three distinct analyses, resulting in considerable improvements to the fidelity and capability of the existing model. First, a torsional fatigue life prediction method with consideration for the identification and incorporation of loading multiaxiality was developed and validated against experimental results from testing of Aluminum 6061-T6 specimens at room temperature. Second, a unique isothermal-mechanical fatigue life testing capability was constructed and utilized in the development of an isothermal-mechanical fatigue life prediction method. This method was validated against experimental data generated from testing of Aluminum 6061-T6 specimens at multiple operating temperatures. Third, alternative quasi-static and dynamic constitutive relationships were applied to the isothermal-mechanical fatigue life prediction method. The accuracy of each new relationship was verified against experimental data generated from testing of two material systems with dissimilar properties: Aluminum 6061-T6 at multiple operating temperatures and Titanium 6Al-4V at room temperature. Each investigation builds upon a previously-developed energy-based life prediction capability, which states: the total strain energy dissipated during both a quasi-static process and a dynamic process are equivalent and a fundamental property of the material. Through these three analyses, the energy-based life prediction framework has acquired the capability of assessing the fatigue life of structures subjected to unplanned multiaxial loading and elevated isothermal operating temperatures; furthermore, alternative constitutive relationships have been successfully employed in improving the fidelity of the life prediction models. This work represents considerable advancements of the energy-based method, and provides a firm foundation for the growth of the energy-based life prediction framework into the thermo-mechanical fatigue regime. This future work will utilize many of the models developed for isothermal-mechanical fatigue; additionally, the isothermal-mechanical testing capability will be readily modified to perform thermo-mechanical fatigue.
Author: John Nicholas Wertz Publisher: ISBN: Category : Languages : en Pages : 49
Book Description
Abstract: An energy-based cycle-dependent fatigue life prediction framework for the calculation of torsional fatigue life and remaining life has been developed. The framework for this fatigue prediction method is developed in accordance with previously developed energy-based axial and bending fatigue life prediction approaches, which state: the total strain energy density accumulated during both a monotonic fracture event and cyclic processes is the same material property, where each can be determined by measuring the area beneath the monotonic true stress-strain curve and the area within a hysteresis loop, respectively. The energy-based fatigue life prediction framework is composed of the following entities: (1) the development of a shear fatigue testing procedure capable of assessing cyclic plastic strain energy density accumulation in a pure shear stress state and (2) the incorporation of an energy-based fatigue life calculation scheme to determine the remaining fatigue life of in-service gas turbine materials subjected to pure shear fatigue. Validation of the improved theory was attempted but failed due to undesired axial loading occurring during testing. Future work was proposed to address the issues.
Author: Deborah G. Mayo Publisher: Cambridge University Press ISBN: 1108563309 Category : Mathematics Languages : en Pages : 503
Book Description
Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.
Author: Kenneth L. Reifsnider Publisher: Wiley-Interscience ISBN: Category : Science Languages : en Pages : 470
Book Description
A daring, original approach to understanding and predicting the mechanical behavior of materials "Damage is an abstraction . . . Strength is an observable, an independent variable that can be measured, with clear and familiar engineering definitions." -from the Preface to Damage Tolerance and Durability of Material Systems Long-term behavior is one of the most challenging and important aspects of material engineering. There is a great need for a useful conceptual or operational framework for measuring long-term behavior. As much a revolution in philosophy as an engineering text, Damage Tolerance and Durability of Material Systems postulates a new mechanistic philosophy and methodology for predicting the remaining strength and life of engineering material. This philosophy associates the local physical changes in material states and stress states caused by time-variable applied environments with global properties and performance. There are three fundamental issues associated with the mechanical behavior of engineering materials and structures: their stiffness, strength, and life. Treating these issues from the standpoint of technical difficulty, time, and cost for characterization, and relationship to safety, reliability, liability, and economy, the authors explore such topics as: * Damage tolerance and failure modes * Factors that determine composite strength * Micromechanical models of composite stiffness and strength * Stiffness evolution * Strength evolution during damage accumulation * Non-uniform stress states * Lifetime prediction With a robust selection of example applications and case studies, this book takes a step toward the fulfillment of a vision of a future in which the prediction of physical properties from first principles will make possible the creation and application of new materials and material systems at a remarkable cost savings.
Author: Yudi Pawitan Publisher: OUP Oxford ISBN: 0191650587 Category : Mathematics Languages : en Pages : 626
Book Description
Based on a course in the theory of statistics this text concentrates on what can be achieved using the likelihood/Fisherian method of taking account of uncertainty when studying a statistical problem. It takes the concept ot the likelihood as providing the best methods for unifying the demands of statistical modelling and the theory of inference. Every likelihood concept is illustrated by realistic examples, which are not compromised by computational problems. Examples range from a simile comparison of two accident rates, to complex studies that require generalised linear or semiparametric modelling. The emphasis is that the likelihood is not simply a device to produce an estimate, but an important tool for modelling. The book generally takes an informal approach, where most important results are established using heuristic arguments and motivated with realistic examples. With the currently available computing power, examples are not contrived to allow a closed analytical solution, and the book can concentrate on the statistical aspects of the data modelling. In addition to classical likelihood theory, the book covers many modern topics such as generalized linear models and mixed models, non parametric smoothing, robustness, the EM algorithm and empirical likelihood.
Author: Kenneth Train Publisher: Cambridge University Press ISBN: 0521766559 Category : Business & Economics Languages : en Pages : 399
Book Description
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
Author: Dino Anthony Celli
Author: Dino Anthony Celli
Author: Saikumar Reddy Yeratapally
Author: Casey M. Holycross
Author: John Nicholas Wertz
Author: John Nicholas Wertz
Author: Deborah G. Mayo
Author: 
Author: Kenneth L. Reifsnider
Author: Yudi Pawitan
Author: Kenneth Train