Analytic prediction of structural stress-strain relations of microstructured metal

Nanostructured and ultra-fine grained metals have higher strength but extremely limited ductility compared to coarse grained metals. However, their ductility can be greatly improved by introducing a specific range of grain sizes in the microstructures. In the paper, multiscale unit cell approach (UCA) is developed and applied to predict the averaged stress-strain relations of the multiscale microstructure metals. The unit cell models are three-phase structured at different scale lengths of 100 nm, 1 μm and 10 μm with different volume fractions and periodic boundary conditions. The contributions of multi-scale microstructures to the macroscopic structural properties of metals are also studied using a analytic approach—two-step mean-field method (TSMF), where three microstructural parameters are introduced and thus mechanical properties such as strength and ductility are presented as a function of these parameters. For verification of these proposed numerical and theoretical algorithms, the structural properties of the pure nickel with three-grain microstructures are studied and the results from FEA and the proposed theory have good agreement.

Determination of stress-strain characteristics of railhead steel using image analysis

True stress-strain curve of railhead steel is required to investigate the behaviour of railhead under wheel loading through elasto-plastic Finite Element (FE) analysis. To reduce the rate of wear, the railhead material is hardened through annealing and quenching. The Australian standard rail sections are not fully hardened and hence suffer from non-uniform distribution of the material property; usage of average properties in the FE modelling can potentially induce error in the predicted plastic strains. Coupons obtained at varying depths of the railhead were, therefore, tested under axial tension and the strains were measured using strain gauges as well as an image analysis technique, known as the Particle Image Velocimetry (PIV). The head hardened steel exhibit existence of three distinct zones of yield strength; the yield strength as the ratio of the average yield strength provided in the standard (σyr=780MPa) and the corresponding depth as the ratio of the head hardened zone along the axis of symmetry are as follows: (1.17 σyr, 20%), (1.06 σyr, 20%- 80%) and (0.71 σyr, > 80%). The stress-strain curves exhibit limited plastic zone with fracture occurring at strain less than 0.1.

Local stress-strain distribution and load transfer across cartilage matrix at micro-scale using combined microscopy-based finite element method

Articular cartilage is the load-bearing tissue that consists of proteoglycan macromolecules entrapped between collagen fibrils in a three-dimensional architecture. To date, the drudgery of searching for mathematical models to represent the biomechanics of such a system continues without providing a fitting description of its functional response to load at micro-scale level. We believe that the major complication arose when cartilage was first envisaged as a multiphasic model with distinguishable components and that quantifying those and searching for the laws that govern their interaction is inadequate. To the thesis of this paper, cartilage as a bulk is as much continuum as is the response of its components to the external stimuli. For this reason, we framed the fundamental question as to what would be the mechano-structural functionality of such a system in the total absence of one of its key constituents-proteoglycans. To answer this, hydrated normal and proteoglycan depleted samples were tested under confined compression while finite element models were reproduced, for the first time, based on the structural microarchitecture of the cross-sectional profile of the matrices. These micro-porous in silico models served as virtual transducers to produce an internal noninvasive probing mechanism beyond experimental capabilities to render the matrices micromechanics and several others properties like permeability, orientation etc. The results demonstrated that load transfer was closely related to the microarchitecture of the hyperelastic models that represent solid skeleton stress and fluid response based on the state of the collagen network with and without the swollen proteoglycans. In other words, the stress gradient during deformation was a function of the structural pattern of the network and acted in concert with the position-dependent compositional state of the matrix. This reveals that the interaction between indistinguishable components in real cartilage is superimposed by its microarchitectural state which directly influences macromechanical behavior.

Effect of mechanical cycling on the stress-strain response of a martensitic Nitinol shape memory alloy

An experimental investigation into the ambient temperature, load-controlled tension�tension fatigue behavior of a martensitic Nitinol shape memory alloy (SMA) was conducted. Fatigue life for several stress levels spanning the critical stress for detwinning was determined and compared with that obtained on an alloy similar in composition but in the austenitic state at room temperature. Results show that the fatigue life of the pseudo-plastic alloy is superior to superelastic shape memory alloy. The stress�strain hysteretic response, monitored throughout the fatigue loading, reveals progressive strain accumulation with the cyclic loading. In addition, the area of hysteresis and recoverable and frictional energies were found to decrease with increasing number of fatigue cycles. Post-mortem characterization of the fatigued specimens through calorimetry and fractography was conducted in order to get further insight into the fatigue micromechanisms. These results are discussed in terms of reversible and irreversible microstructural changes that take place during cyclic loading. Aspects associated with self-heating of martensitic alloy undergoing high frequency stress cycling are discussed.

Intelligent prediction of the stress-strain response of intact and jointed rocks

An application of Artificial Neural Networks for predicting the stress-strain response of jointed rocks under different confining pressures is presented in this paper. Rocks of different compressive strength with different joint properties (frequency, orientation and strength of joints) are considered in this study. The database for training the neural network is formed from the results of triaxial compression tests on different intact and jointed rocks with different joint properties tested at different confining pressures reported by various researchers in the literature. The network was trained using a three-layered network with the feed-forward back propagation algorithm.About 85% of the data was used for training and the remaining 15% was used for testing the network. Results from the analyses demonstrated that the neural network approach is effective in capturing the stress-strain behaviour of intact rocks and the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different jointed rocks, whose intact strength varies from 11.32 MPa to 123 MPa, spacing of joints varies from 10 cm to 100 cm. and confining pressures range from 0 to 13.8 MPa. (C) 2010 Elsevier Ltd. All rights reserved.

Stress-strain characteristics of Tress-strain characteristics of reinforcing steel bars under cyclic loading

This paper presents test results for 22 high strength deformed bars and nine mild steel bars subjected to monotonic repeated and reversed axial loading to determine the stress-strain behavior. Equations have been proposed for the stress-strain curves and have been compared with test results. Satisfactory agreement was obtained.

Stress-Strain Characteristics of Reinforcing Steel Bars Under Cyclic Loading

This paper presents test results for 22 high strength deformed bars and nine mild steel bars subjected to monotonic repeated and reversed axial loading to determine the stress-strain behavior. Equations have been proposed for the stress-strain curves and have been compared with test results. Satisfactory agreement was obtained.

Cement stabilised rammed earth. Part B: compressive strength and stress-strain characteristics

Strength and behaviour of cement stabilised rammed earth (CSRE) is a scantily explored area. The present study is focused on the strength and elastic properties of CSRE. Characteristics of CSRE are influenced by soil composition, density of rammed earth, cement and moisture content. The study is focused on examining (a) role of clay content of the soil on strength of CSRE and arriving at optimum clay fraction of the soil mix, (b) influence of moisture content, cement content and density on strength and (c) stress-strain relationships and elastic properties for CSRE. Major conclusions are (a) there is considerable difference between dry and wet compressive strength of CSRE and the wet to dry strength ratio depends upon the clay fraction of soil mix and cement content, (b) optimum clay fraction yielding maximum compressive strength for CSRE is about 16%, (c) strength of CSRE is highly sensitive to density and for a 20% increase in density the strength increases by 300-500% and (d) in dry state the ultimate strain at failure for CSRE is as high as 1.5%, which is unusual for brittle materials.

Stress-strain response of plastic waste mixed soil

Recycling plastic waste from water bottles has become one of the major challenges worldwide. The present study provides an approach for the use plastic waste as reinforcement material in soil. The experimental results in the form of stress-strain-pore water pressure response are presented. Based on experimental test results, it is observed that the strength of soil is improved and compressibility reduced significantly with addition of a small percentage of plastic waste to the soil. The use of the improvement in strength and compressibility response due to inclusion of plastic waste can be advantageously used in bearing capacity improvement and settlement reduction in the design of shallow foundations. (C) 2010 Elsevier Ltd. All rights reserved.

Stress-strain Curves and Corresponding Structural Parameters in Mulberry and Non-mulberry Silk Fibers

Mulberry fiber (Bivoltine) and non-mulberry fiber (Tassar) were subjected to stress-strain studies and the corresponding samples were examined using wide angle X-ray scattering studies. Here we have two different characteristic stress-strain curves and this has been correlated with changes in crystallite shape ellipsoids in all the fibers. Exclusive crystal structure studies of Tassar fibers show interesting feature of transformation from antiparallel chains to parallel chains.

Stress-Strain Prediction of Jointed Rocks using Artificial Neural Networks

The applicability of Artificial Neural Networks for predicting the stress-strain response of jointed rocks at varied confining pressures, strength properties and joint properties (frequency, orientation and strength of joints) has been studied in the present paper. The database is formed from the triaxial compression tests on different jointed rocks with different confining pressures and different joint properties reported by various researchers. This input data covers a wide range of rock strengths, varying from very soft to very hard. The network was trained using a 3 layered network with feed forward back propagation algorithm. About 85% of the data was used for training and remaining15% for testing the predicting capabilities of the network. Results from the analyses were very encouraging and demonstrated that the neural network approach is efficient in capturing the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different rocks, whose intact strength vary from 11.32 MPa to 123 MPa and spacing of joints vary from 10 cm to 100 cm for confining pressures ranging from 0 to 13.8 MPa.