Validation of a model for static and dynamic recrystallization in metals

In this paper, modifications are proposed to a phenomenological plasticity model to account for the evolution of recrystallization and the resultant softening behavior. The novel model includes internal state variables representing dislocation density and the spacing between geometrically necessary subgrain boundaries. In order to capture both single and multiple peak recrystallization, the model tracks the evolution of recrystallized volume fractions for multiple cycles of recrystallization, and has a set of state variables for each volume fraction. A rule of mixtures is used to determine the average stress. The model is capable of capturing static recrystallization as well as both single and multiple peak dynamic recrystallization. Material parameters are fit to data from monotonic compression tests on copper for a wide range of temperatures and strain rates. The model is then validated by using the same parameter set to predict multiple-stage response in which samples are compressed, held at temperature for various lengths of time, and then compressed further. The model predicts both the static recrystallization that occurs between loading stages as well as the dynamic recrystallization occurring during the second loading stage

Refining Time-Activity Classification of Human Subjects Using the Global Positioning System

BACKGROUND:Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns. METHODS:Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods. RESULTS:Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions. CONCLUSIONS:The random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations

Validation of thermal-mechanical modeling of stainless steel forgings

A constitutive model for recrystallization has been developed within the framework of an existing dislocation-based rate and temperature-dependent plasticity model. The theory has been implemented and tested in a finite element code. Material parameters were fit to data from monotonic compression tests on 304L steel for a wide range of temperatures and strain rates. The model is then validated by using the same parameter set in predictive thermal-mechanical simulations of experiments in which wedge forgings were produced at elevated temperatures. Model predictions of the final yield strengths compare well to the experimental results

A mechanism-based approach to modeling ductile fracture.

Ductile fracture in metals has been observed to result from the nucleation, growth, and coalescence of voids. The evolution of this damage is inherently history dependent, affected by how time-varying stresses drive the formation of defect structures in the material. At some critically damaged state, the softening response of the material leads to strain localization across a surface that, under continued loading, becomes the faces of a crack in the material. Modeling localization of strain requires introduction of a length scale to make the energy dissipated in the localized zone well-defined. In this work, a cohesive zone approach is used to describe the post-bifurcation evolution of material within the localized zone. The relations are developed within a thermodynamically consistent framework that incorporates temperature and rate-dependent evolution relationships motivated by dislocation mechanics. As such, we do not prescribe the evolution of tractions with opening displacements across the localized zone a priori. The evolution of tractions is itself an outcome of the solution of particular, initial boundary value problems. The stress and internal state of the material at the point of bifurcation provides the initial conditions for the subsequent evolution of the cohesive zone. The models we develop are motivated by in-situ scanning electron microscopy of three-point bending experiments using 6061-T6 aluminum and 304L stainless steel, The in situ observations of the initiation and evolution of fracture zones reveal the scale over which the failure mechanisms act. In addition, these observations are essential for motivating the micromechanically-based models of the decohesion process that incorporate the effects of loading mode mixity, temperature, and loading rate. The response of these new cohesive zone relations is demonstrated by modeling the three-point bending configuration used for the experiments. In addition, we survey other methods with the potential to provide more detailed information about the near tip deformation fields

A robust, coupled approach for atomistic-continuum simulation.

This report is a collection of documents written by the group members of the Engineering Sciences Research Foundation (ESRF), Laboratory Directed Research and Development (LDRD) project titled 'A Robust, Coupled Approach to Atomistic-Continuum Simulation'. Presented in this document is the development of a formulation for performing quasistatic, coupled, atomistic-continuum simulation that includes cross terms in the equilibrium equations that arise due to kinematic coupling and corrections used for the calculation of system potential energy to account for continuum elements that overlap regions containing atomic bonds, evaluations of thermo-mechanical continuum quantities calculated within atomistic simulations including measures of stress, temperature and heat flux, calculation used to determine the appropriate spatial and time averaging necessary to enable these atomistically-defined expressions to have the same physical meaning as their continuum counterparts, and a formulation to quantify a continuum 'temperature field', the first step towards constructing a coupled atomistic-continuum approach capable of finite temperature and dynamic analyses

Modeling of friction-induced deformation and microstructures.

Frictional contact results in surface and subsurface damage that could influence the performance, aging, and reliability of moving mechanical assemblies. Changes in surface roughness, hardness, grain size and texture often occur during the initial run-in period, resulting in the evolution of subsurface layers with characteristic microstructural features that are different from those of the bulk. The objective of this LDRD funded research was to model friction-induced microstructures. In order to accomplish this objective, novel experimental techniques were developed to make friction measurements on single crystal surfaces along specific crystallographic surfaces. Focused ion beam techniques were used to prepare cross-sections of wear scars, and electron backscattered diffraction (EBSD) and TEM to understand the deformation, orientation changes, and recrystallization that are associated with sliding wear. The extent of subsurface deformation and the coefficient of friction were strongly dependent on the crystal orientation. These experimental observations and insights were used to develop and validate phenomenological models. A phenomenological model was developed to elucidate the relationships between deformation, microstructure formation, and friction during wear. The contact mechanics problem was described by well-known mathematical solutions for the stresses during sliding friction. Crystal plasticity theory was used to describe the evolution of dislocation content in the worn material, which in turn provided an estimate of the characteristic microstructural feature size as a function of the imposed strain. An analysis of grain boundary sliding in ultra-fine-grained material provided a mechanism for lubrication, and model predictions of the contribution of grain boundary sliding (relative to plastic deformation) to lubrication were in good qualitative agreement with experimental evidence. A nanomechanics-based approach has been developed for characterizing the mechanical response of wear surfaces. Coatings are often required to mitigate friction and wear. Amongst other factors, plastic deformation of the substrate determines the coating-substrate interface reliability. Finite element modeling has been applied to predict the plastic deformation for the specific case of diamond-like carbon (DLC) coated Ni alloy substrates

On a Proposal for a Continuum With Microstructure

71 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1981.A recently proposed model for a continuum with microstructure is further substantiated by identifying the microstructure with dislocations. In particular, the continuum is viewed as a superimposed state made up from a perfect lattice state, an immobile dislocation state, and a mobile dislocation state. It is assumed that each state evolves continuously in space-time and transitions from one state to another take place spontaneously according to the balance laws of effective mass and momentum. When the constitutive equations are subjected to the requirements of invariance, familiar statements from dislocation dynamics are deduced. When plastic strain and yield are identified in terms of the parameters characterizing the dislocation states, familiar flow rules and yield surfaces are produced. The capability of the model to predict not only Tresca and Von-Mises plastic behavior but also phenomena such as Prager's kinematic hardening, different responses in tension and compression, latent hardening, and the Bauschinger effect, is shown. Finally, the appropriateness of our equations to model creep, cyclic plasticity, and fatigue, is illustrated.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Formulation and validation of a thermomechanical viscoplastic constitutive model for amorphous glassy polymer

International audiencePolymers exhibit a rich variety of mechanical behaviour originating from their particular microstructure. To capture such intricate structure properties, a number of polymer constitutive models have been proposed and implemented into finite element codes in an effort to solve complex engineering problems. However, developing improved constitutive models for polymers that are physically-based has proven to be a challenging area with important implications for the design of polymeric structural components

Refining Time-Activity Classification of Human Subjects Using the Global Positioning System.

BackgroundDetailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns.MethodsTime-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods.ResultsMaximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions.ConclusionsThe random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations