Predicted Reaction Forces Research Articles

Experimental and numerical investigation of the intra-ply shear behaviour of unidirectional prepreg forming through picture-frame test

Intra-ply shear behaviour of uncured composite plies strongly influences component quality in advanced manufacturing processes such as prepreg compression moulding (PCM) and double diaphragm forming (DDF). This study investigates a straightforward method to characterise the intra-ply shear behaviour of a carbon fibre/epoxy UD prepreg using a specially designed picture-frame rig, by which specimens can be tested without involving inter-ply shear as would normally be observed in cross-plied UD prepreg stacks. Applying the proposed method, it is seen that specimens tend to suffer transverse buckling/wrinkling and local fibre-splitting at large shear strains. 3D digital image correlation (DIC) and a non-contacting video extensometer were utilised to determine the shear strain distribution throughout the test and particularly to determine the onset of out-of-plane deformations such that the trellis shear deformation portion of the test can be identified. The obtained shear stress-strain results show a temperature- and rate-dependent viscoelastic response, with the greatest influence from the temperature. The obtained in-plane shear properties were applied in the numerical simulation of the picture frame test based on a hypoelastic law. Although the predicted reaction forces are greater than experimental results at high strains due several factors including local fibre-splitting, a good agreement overall between physical test data and simulation results is seen for all test conditions. Finally, it is demonstrated that major advantages of the proposed test with respect to the conventional picture-frame test are that only load-extension data are required from the trellis shear experiment to calculate accurately the intra-ply shear stress-strain relationship and that the deformation rate can be easily controlled.

Machine learning-based modeling of the coupling effect of strain rate and temperature on strain hardening for 5182-O aluminum alloy

This research characterizes the dynamic hardening behavior of an aluminum alloy sheet of 5182-O for the coupling effect of strain rate and temperature. Tests are carried out for dogbone specimens at different loading conditions to experimentally characterize the strain rate hardening and thermal softening effect for the alloy. The behaviours are then modeled by the Johnson-Cook, Zerilli-Armstrong and Lim-Huh models. In addition, the FEA-friendly polynomial model and artificial neural network (ANN) model are used to describe the highly non-linearity and coupling of strain hardening. Factors affecting ANN predicting accuracy and numerical computing efficiency are comprehensively studied including network structure, parameter settings and optimization algorithms. All the analytical and ANN models are also implemented into ABAQUS/Explicit to numerically compute the reaction force of tensile tests of dogbone specimens. The strain hardening curves are predicted by the analytical and ANN models for the comparison with experimental measurements to evaluate their performance. The experimental results show that the strain rate is slightly negative at room temperature, while the strain rate effect turns to be positive as temperature rises. The comparison of the flow curves between prediction and experiments reveals that the coupling effect is reasonably illustrated by the proposed polynomial model and the ANN model illustrates the flow curves with the dramatically much better accuracy than all the other models. The numerically predicted reaction forces prove that the ANN model accurately illustrates the load capability with the best agreement among the models studied in this research. The numerical computation also shows that the numerical computation efficiency of the ANN model is slightly reduced compared with analytical models, but the reduction is not much and worthwhile compared with the high accuracy of ANN.

On Choosing Structure for a Machine Learning-based Reaction Force Predictor for Walking Robots

This paper focuses on the topic of contact reaction prediction for walking robots, namely on the analysis of performances on different structures of the machine-learning-based predictors. Predicting reaction forces is important due to the fact that it can allow us to retrieve a simplified model of the contact scenario, as was proposed in the literature preciously. This allows to turn the problem of contact model identification into a data collection and processing problem. In order to do it effectively, both a data compression (feature extraction) and regression strategies might be used. This research provides an analysis of both with respect to the discussed problem.

A 3-D constitutive model for finite element analyses of agarose with a range of gel concentrations

Hydrogels have seen widespread application across biomedical sciences and there is considerable interest in using hydrogels, including agarose, for creating in vitro three-dimensional environments to grow cells and study mechanobiology and mechanotransduction. Recent advances in the preparation of agarose gels enable successful encapsulation of viable cells at gel concentrations as high as 5%. Agarose with a range of gel concentrations can thus serve as an experimental model mimicking changes in the 3-D microenvironment of cells during disease progression and can facilitate experiments aimed at probing the corresponding mechanobiology, e.g. the evolving mechanobiology of chondrocytes during the progression of osteoarthritis. Importantly, whether stresses (forces) or strains (displacements) drive mechanobiology and mechanotransduction is currently unknown. We can use experiments to quantify mechanical properties of hydrogels, and imaging to estimate microstructure and even strains; however, only computational models can estimate intra-gel stresses in cell-seeded agarose constructs because the required in vitro experiments are currently impossible. Finite element modeling is well-established for (computational) mechanical analyses, but accurate constitutive models for modeling the 3-D mechanical environments of cells within high-stiffness agarose with varying gel concentrations are currently unavailable. In this study we aimed to establish a 3-D constitutive model of high-stiffness agarose with a range of gel concentrations. We applied a multi-step, physics-based optimization approach to separately fit subsets of model parameters and help achieve robust convergence. Our constitutive model, fitted to experimental data on progressive stress-relaxations, was able to predict reaction forces determined from independent experiments on cyclical loading. Our model has broad applications in finite element modeling aimed at interpreting mechanical experiments on agarose specimens seeded with cells, particularly in predicting distributions of intra-gel stresses. Our model and fitted parameters enable more accurate finite element simulations of high-stiffness agarose constructs, and thus better understanding of experiments aimed at mechanobiology, mechanotransduction, or other applications in tissue engineering.

Using Neural Networks to Predict the Normal Reactions of a Walking Robot

Purpose of reseach . This work is devoted to solving one of the problems associated with the control of walking robots based on their dynamic mathematical model − the presence in it of obvious mechanical bonds due to reactions of bonds with the supporting surface. To solve this problem, it is proposed to use a fully connected neural network to evaluate the forces of normal reactions between the surface and the feet of a bipedal walking machine during its implementation of one step. Methods. The paper considers two neural network architectures based on fully connected layers with ReLU activation functions. The architecture of the neural network includes five fully connected layers (input, output and three hidden), and an alternative architecture includes a thinning layer after each fully connected layer. The input data for the network are the state of the robot and the required control actions, and the output is the predicted reaction forces. The training sample is generated by modeling a complete dynamic model of the robot. The network is built and trained using machine learning libraries Keras and TensorFlow. Results. The generation of training sample for neural network is described here, and it is carried out the training of two architectures of neural networks. Based on the simulation data, it was established that both trained neural networks are able to accurately predict the values of normal reactions using the values of generalized coordinates and velocities, as well as control actions as input, however, a static prediction error is observed. Conclusion . The results obtained within the framework of the article can be further used to control the movement of bipedal walking machines on various types of surfaces.

Analysis of a Rotary Head for Cutting Vegetation#

This paper provides a dynamic analysis of a rotational cutting head like that used for impact cutting of vegetation. Specifically, the model is used to predict reaction forces generated at the blade-retaining pin, which attaches the cutting blade to the cutting body subassembly, while cutting single stem vegetation. A detailed cutting model, which gives the input force to the blade, is provided. Dynamic simulations show forces generated for both steady-state spinning of the cutting head and spinning when subject to a force input from cutting a stem of vegetation. The model can be used to drive future designs of such devices.

FEM Dynamic Fracture Analysis OF Concrete Beams

A dynamic finite element model, which incorporated a fracture process zone and a maximum tensile stress failure criterion, was used to simulate the results of impact tests on concrete and mortar three-point bend specimens. When the measured impact load was used as an input boundary condition to this dynamic finite element model, it predicted with reasonable accuracy the measured crack propagation history, the mid-span deflection, and the notch tip strain. However, the numerical simulation showed that, in contrast to the modulus of rupture strengths obtained with impact loading, the dynamic tensile strengths appropriate for the fracture process zone model were relatively insensitive to the strain rate variations of 0.1–0.4 sec\u-¹. The predicted reaction forces at the supports, the energy balance, and dynamic energy release rate versus crack velocity results are also presented.

Development of a Pipe Rupture

An approach to the problem of predicting reaction forces that can occur during pipe failure is provided. Use is made of experimental data measuring crack propagation speed to determine the pipe rupture forces. The results of this paper are for pipelines carrying subcooled liquid water, but may be applied to other fluids. The reaction forces during pipe failure are compared with steady-state values.