Effect of mean stress on multiaxial ratcheting life: a simplified life prediction model based on average equivalent stress amplitude

This article proposes a model to predict uniaxial and multiaxial ratcheting life by addressing the three primary parameters that influence failure life: fatigue damage, ratcheting damage and the multiaxial loading path. These three factors are addressed in the present model by (a) the stress amplitude for fatigue damage, (b) mean stress-dependent Goodman equation for ratcheting damage and (c) an inherent weight factor based on average equivalent stress to account for the multiaxial loading. The proposed model requires only two material constants which can be easily determined from uniaxial symmetric stress-controlled fatigue tests. Experimental ratcheting life data collected from the literature for 1025 and 42CrMo steel under multiaxial proportional and nonproportional constant amplitude loading ratcheting with triangular sinusoidal and trapezoidal waveform (i.e. linear, rhombic, circular, elliptical and square stress paths) have shown good agreement with the proposed model.

Sensitivity of the final properties of tailored hot stamping components to the process and material parameters

The final mechanical properties of hot stamped components are affected by many process and material parameters due to the multidisciplinary nature of this thermal-mechanical-metallurgical process. The phase transformation, which depends on the temperature field and history, determines the final microstructure and consequently the final mechanical properties. Tailored hot stamping parts - where the cooling rates are locally chosen to achieve structures with graded properties - has been increasingly adopted in the automotive industry. In this case, the robustness of final part properties is more critical than in the conventional hot stamping parts, where the part is fully quenched. In this study, a wide range of input parameters in a generalized hot stamping model have been investigated, examining the effect on the temperature history and resulting final material properties. A generic thermo-mechanical finite element model of hot stamping was created and a modified phase transformation model, based on Scheil's additive principle, has been applied. The comparison between modeling and experiments shows that the modified phase transformation model coupled with the incubation time provides higher accuracy on the simulation of transformation kinetics history. The robustness of four conditions relevant to tailored hot stamping was investigated: heated tooling (with low and high tool conductance), air cooling, and conventional hot stamping. The results show the high robustness of the conventional hot stamping compared to tailored hot stamping, with respect to the stamped component's final material properties (i.e. phase fraction and hardness). Furthermore, tailored hot stamping showed higher robustness when low conductivity tools are used relative to high conductivity tools.

An analytical model for web-warping in variable width flexible roll forming

The development of ultra/advanced high strength steels (U/AHSS) has challenged traditional forming methods due to their higher strength and reduced formability. An alternative method is flexible roll forming, which allows the manufacture of sheet metal of high strength and limited ductility into complex and weight-optimized components. However, one major problem in flexible roll forming is the web-warping defect, which is the deviation in height of the web over the length of the profile. The authors’ previous work developed an analytical model to predict the magnitude of web-warping. That model was purely geometric and neglected the effect of material properties. This work develops an analytical solution for the prediction of web-warping that considers both geometric and material parameters. The model results were validated by comparison with numerical and experimental results. The impact of this new model will be the ability to provide a rapid initial design assessment before an intensive numerical analysis of flexible roll forming is conducted.

Multi-scale simulation using a hybrid mass-spring system and finite element model

Ply-scale finite element (FE) models are widely used to predict the performance of a composite structure based on material properties of individual plies. When simulating damage, these models neglect microscopic fracture processes which may have a significant effect on how a crack progresses within and between plies of a multidirectional laminate. To overcome this resolution limitation a multi-scale modelling technique is employed to simulate the effect micro-scale damage events have on the macro-scale response of a structure. The current paper discusses the development and validation of a hybrid mass-spring system and finite element modelling technique for multi-scale analysis. The model developed here is limited to elastic deformations; however, it is the first key step towards an efficient multi-scale damage model well suited to simulation of fracture in fibre reinforced composite materials. Various load cases have been simulated using the model developed here which show excellent accuracy compared to analytical and FE results. Future work is discussed, including extension of the model to incorporate damage modelling.

Identifying inorganic material affinity classes for peptide sequences based on context learning

There is a growing interest in identifying inorganic material affinity classes for peptide sequences due to the development of bionanotechnology and its wide applications. In particular, a selective model capable of learning cross-material affinity patterns can help us design peptide sequences with desired binding selectivity for one inorganic material over another. However, as a newly emerging topic, there are several distinct challenges of it that limit the performance of many existing peptide sequence classification algorithms. In this paper, we propose a novel framework to identify affinity classes for peptide sequences across inorganic materials. After enlarging our dataset by simulating peptide sequences, we use a context learning based method to obtain the vector representation of each amino acid and each peptide sequence. By analyzing the structure and affinity class of each peptide sequence, we are able to capture the semantics of amino acids and peptide sequences in a vector space. At the last step we train our classifier based on these vector features and the heuristic rules. The construction of our models gives us the potential to overcome the challenges of this task and the empirical results show the effectiveness of our models.

Reducing the effect of wave dispersion in a timber pole based on transversely isotropic material modelling

Round timbers are used for telecommunication and power distribution networks, jetties, piles, short span bridges etc. To assess the condition of these cylindrical shape timber structures, bulk and elementary wave theory are usually used. Even though guided wave can represents the actual wave behaviour, a great deal complexity exists to model stress wave propagation within an orthotropic media, such as timber. In this paper, timber is modelled as transversely isotropic material without compromising the accuracy to a great extent. Dispersion curves and mode shapes are used to propose an experimental set up in terms of the input frequency and bandwidth of the signal, the orientation of the sensor and the distance between the sensors in order to reduce the effect of the dispersion in the output signal. Some example based on the simulated signal is also discussed to evaluate the proposed experimental set up.

Chemical structure based prediction of PAN and oxidized PAN fiber density through a non-linear mathematical model

The production of carbon fiber, particularly the oxidation/stabilization step, is a complex process. In the present study, a non-linear mathematical model has been developed for the prediction of density of polyacrylonitrile (PAN) and oxidized PAN fiber (OPF), as a key physical property for various applications, such as energy and material optimization, modeling, and design of the stabilization process. The model is based on the available functional groups in PAN and OPF. Expected functional groups, including [Formula presented], [Formula presented], –CH2, [Formula presented], and [Formula presented], were identified and quantified through the full deconvolution analysis of Fourier transform infrared attenuated total reflectance (FT-IR ATR) spectra obtained from fibers. These functional groups form the basis of three stabilization rendering parameters, representing the cyclization, dehydrogenation and oxidation reactions that occur during PAN stabilization, and are used as the independent variables of the non-linear predictive model. The k-fold cross validation approach, with k = 10, has been employed to find the coefficients of the model. This model estimates the density of PAN and OPF independent of operational parameters and can be expanded to all operational parameters. Statistical analysis revealed good agreement between the governing model and experiments. The maximum relative error was less than 1% for the present model.

A model of grain fragmentation based on lattice curvature

A new model is proposed that aims to capture within a single modelling frame all the main microstructural features of a severe plastic deformation process. These are: evolution of the grain size distribution, misorientation distribution, crystallographic texture and the strain-hardening of the material. The model is based on the lattice curvature that develops in all deformed grains. The basic assumption is that lattice rotation within an individual grain is impeded near the grain boundaries by the constraining effects of the neighbouring grains, which gives rise to lattice curvature. On that basis, a fragmentation scheme is developed which is integrated in the Taylor viscoplastic polycrystal model. Dislocation density evolution is traced for each grain, which includes the contribution of geometrically necessary dislocations associated with lattice curvature. The model is applied to equal-channel angular pressing. The role of texture development is shown to be an important element in the grain fragmentation process. Results of this modelling give fairly precise predictions of grain size and grain misorientation distribution. The crystallographic textures are well reproduced and the strength of the material is also reliably predicted based on the modelling of dislocation density evolution coupled with texture development.

Applying bi-directional evolutionary structural optimisation method for tunnel reinforcement design considering nonlinear material behaviour

In the empirical methods for reinforcement design of underground excavations, an even distribution of rock bolts is generally recommended. This work proves that this design is not necessarily optimal and shows how the state-of-the-art reinforcement design could be improved through topology optimisation techniques. The Bidirectional Evolutionary Structural Optimisation (BESO) method has been extended to consider nonlinear material behaviour. An elastic perfectly-plastic Mohr-Coulomb model is utilised for both original rock and reinforced rock. External work along the tunnel wall is considered as the objective function. Various in situ stress conditions with different horizontal stress ratios and different geostatic stress magnitudes are investigated through several examples. The outcomes show that the proposed approach is capable of improving tunnel reinforcement design. Also, significant difference in optimal reinforcement distribution for the cases of linear and nonlinear analysis results proves the importance of the influence of realistic nonlinear material properties on the final outcome.