A novel simulation for the design of a low cycle fatigue experimental testing programme

This paper proposes an innovative concept for the design of an experimental testing programme suitable for causing Low Cycle Fatigue crack initiation in a bespoke complex notched specimen. This technique is referred to as the Reversed Plasticity Domain Method and utilises a novel combination of the Linear Matching Method and the Bree Interaction diagram. This is the first time these techniques have been combined in this way for the calculation of the design loads of industrial components. This investigation displays the capabilities of this technique for an industrial application and demonstrates its key advantages for the design of an experimental testing programme for a highly complex test specimen

Optimizing the Geometric Configuration and Manufacturing Process of High Mast Illumination Poles

This work presents the development of a high-fidelity model that accounts for the cumulative effect of welding and hot- dip galvanizing on the determining the resulting residual stresses and deformations induced during the manufacturing process of high mast illumination poles (HMIPs). This model is meant to elucidate the root causes of weld toe cracks in HMIPs. A TxDOT pole-to-base plate connection detail was used as the reference model in the analysis. Welding was modeled using the plug-in Abaqus Welding Interface (AWI), which automatically implements a series of sequential thermal and mechanical analyses. Then, the welding stress results were used as initial input to the galvanizing analysis. The cumulative stress results were compared against simulations that only considered the galvanizing process. A parametric study was then conducted to quantify the variation in the residual stresses and equivalent plastic strain magnitudes induced during the welding and galvanizing of HMIPs due to changes in welding and galvanizing practices. The results revealed that the cumulative effects of the different processes involved in the manufacturing of HMIPs contribute to the formation of galvanizing cracks in HMIPs. Also, increasing the dipping submersion speed during galvanizing and lowering the torch temperature magnitude during welding results in fewer zones prone to cracking. Altering the angle of inclination effect did not have a significant impact on the results. Performing variations in the manufacturing practices used for the fabrication of HMIPs can contribute to reducing the extensive inspection procedures conducted post-galvanizing to identify cracks

Effect of delamination on the fatigue life of GFRP: A thermographic and numerical study

Delamination is the major failure mechanism in composite laminates and eventually leads to material failure. An early-detection and a better understanding of this phenomenon, through non-destructive assessment, can provide a proper in situ repair and allow a better evaluation of its effects on residual strength of lightweight structural components. Here we adopt a joint numerical-experimental approach to study the effect of delamination on the fatigue life of glass/epoxy composites. To identify and monitor the evolution of the delamination during loading, we carried out stepwise cyclic tests coupled with IR-thermography on both undamaged and artificially-damaged samples. The outcome of the tests shows that IR-thermography is able to identify a threshold stress, named damage stress ?D, which is correlated to the damage initiation and the fatigue performance of the composite. Additionally, we performed FE-simulations, implementing the delamination by cohesive elements. Such models, calibrated on the basis of the experimental fatigue results, can provide a tool to assess the effect of parameters, such as the delamination size and location and composite stacking sequence, on the residual strength and fatigue life of the composite material

A machine learning assisted preliminary design methodology for repetitive design features in complex structures

The current industrial practice used at the preliminary design stage of complex structures involves the use of multifidelity submodelling simulations to predict failure behaviour around geometric and structural design features of interest, such as bolts, fillets, and ply drops. A simplified global model without the design features is first run and the resulting displacement fields are transferred to multiple local models containing the design features of interest. The creation of these high-fidelity local feature models is highly expert dependent, and their subsequent simulation is highly time-consuming. These issues compound as these design features are typically repetitive in complex structures. This leads to long design and development cycles. Application of machine learning to this framework has the potential to capture a structural designer’s modelling knowledge and quickly suggest improved design feature parameters, thereby addressing the current challenges. In this work, we provide a proof of concept for a machine learning assisted preliminary design workflow, see Figure 1, whereby feature-specific surrogate models may be trained offline and used for faster and simpler design iterations. The key challenge is to maximise the prediction accuracy of failure metrics whilst managing the high dimensions required to represent design feature simulation parameters in a minimum training dataset size. These challenges are addressed using: a modified Latin Hypercube Sampling scheme adjusted to improve design of experiment in composite materials; a bi-linear work-equivalent homogenisation scheme to reduce the number of nodal degrees of freedom; a non-local volume-averaged stress-based approach to reduce the number of target features; and linear superposition of stacked bi-directional LSTM neural network models. This methodology is demonstrated in a case study of predicting the stresses of open hole composite laminates in an aerospace C-spar structure. Results highlight the high accuracy (>90%) and time saving benefit (>15x) of this new approach. This methodology may be used to faster correct and iterate the preliminary design of any large or complex structure where there are repetitive localised design features that may contribute to failure, such as in Formula 1 or wind turbines. Combined with exascale computing this methodology may also be applied for predictive virtual testing of digital twins

An Abaqus plugin for efficient damage initiation hotspot identification in large-scale composite structures with repeated features

© 2021 Elsevier Ltd Identifying the hotspots for damage initiation in large-scale composite structure designs presents a significant challenge due to the high modelling cost. For most industrial applications, the finite element (FE) models are often coarsely meshed with shell elements and used to predict the global stiffness and internal loads. Because of the lack of detailed descriptions for the composite materials and 3D stress states, most of the established failure criteria are not applicable. In this work we present an Abaqus plugin tool which implements a framework to identify the hotspots by using a pre-computed database generated for specific, heavily-repeated feature types based on a given structural model. Developed with an object-oriented implementation in Python, this software is split into two main parts, specifically for feature generation and structural analysis. The pre-computed model presents a full 3D description for the considered feature and works as a submodel to the coarse structure model driven by a one-way transfer of the boundary conditions. The presented framework is an analysis tool for efficient sizing of large-scale composite structures, as it enables 3D damage analysis of the structures in critical zones with significant savings of the modelling and computational cost. The results are compared with conventional FE modelling and satisfactory agreement is observed. In addition, the software also enables the pre-computed database to be stored in an HDF5 data file for further reuse on new structures with the same feature

An investigation of composite failure analyses and damage evolution in finite element models

This paper presents a composite conical structure used commonly in flight-qualification testing. This structure’s overall load-displacement behavioral response is characterized. Mixed-mode multidelamination in a layered composite specimen is considered in Abaqus/Explicit through both the Virtual Crack Closure Technique and Cohesive Elements. The Virtual Crack Closure Technique and Cohesive Elements are compared against experimental test results presented in literature. Further, a thorough comparison in which the effects of failure criteria type, through-thickness mesh density, and finite element type on the progressive failure response of this composite assembly is discussed. Lastly, Abaqus/Standard and Helius PFA are compared in order to gain confidence into which analytical model’s failure theories best predicts the different scales of failure, both local/microscale and global/macroscale

Experimental Testing and Numerical Investigation of Materials with Embedded Systems during Indentation and Complex Loading Conditions

In this work, parametric FE (Finite Element) modelling has been developed and used to study the deformation of soft materials with different embedded systems under indentation and more complex conditions. The deformation of a soft material with an embedded stiffer layer under cylindrical flat indenter was investigated through FE modelling. A practical approach in modelling embedded system is evaluated and presented. The FE results are correlated with an analytical solution for homogenous materials and results from a mathematical approach for embedded systems in a half space. The influence of auxeticity on the indentation stiffness ratio and the de-formation of the embedded system under different conditions (indenter size, thickness and embedment depth of the embedded layer) was established and key mechanisms of the Poisson’s ratio effect are highlighted. The results show that the auxeticity of the matrix has a direct influence on the indentation stiffness of the system with an embedded layer. The enhancement of indentation resistance due to embedment increases, as the matrix Poisson’s ratio is decreased to zero and to negative values. The indentation stiffness could be increased by over 30% with a thin inextensible shell on top of a negative Poisson’s ratio matrix. The deformation of the embedded layer is found to be significantly influenced by the auxeticity of the matrix. Selected case studies show that the modelling approach developed is effective in simulating piezoelectrical sensors, and force sensitive resistor, as well as investigating the deformation and embedded auxetic meshes. A full scale parametric FE foot model is developed to simulate the deformation of the human foot under different conditions including soles with embedded shells and negative Poisson’s ratio. The models used a full bone structure and effective embedded structure method to increase the modelling efficiency. A hexahedral dominated meshing scheme was applied on the surface of the foot bones and skin. An explicit solver (Abaqus/Explicit) was used to simulate the transient landing process. Navicular drop tests have been performed and the displacement of the Navicular bone is measured using a 3D image analysing system. The experimental results show a good agreement with the numerical models and published data. The detailed deformation of the Navicular bone and factors affecting the Navicular bone displacement and measurement is discussed. The stress level and rate of stress increase in the Metatarsals and the injury risk in the foot between forefoot strike (FS) and rearfoot (RS) is evaluated and discussed. A detailed full parametric FE foot model is developed and validated. The deformation and internal energy of the foot and stresses in the metatarsals are comparatively investigated. The results for forefoot strike tests showed an overall higher average stress level in the metatarsals during the entire landing cycle than that for rearfoot strike. The increased rate of the metatarsal stress from the 0.5 body weight (BW) to 2 BW load point is 30.76% for forefoot strike and 21.39% for rearfoot strike. The maximum rate of stress increase among the five metatarsals is observed on the 1st metatarsal in both landing modes. The results indicate that high stress level during forefoot landing phase may increase potential of metatarsal injuries. The FE was used to evaluate the effect of embedded shell and auxetic materials on the foot-shoe sole interaction influencing both the contact area and the pressure. The work suggests that application of the auxetic matrix with embedded shell can reinforce the indentation resistance without changing the elastic modulus of the material which can optimise the wearing experience as well as providing enough support for wearers. . Potential approaches of using auxetic structures and randomly distributed 2D inclusion embedded in a soft matrix for footwear application is discussed. The design and modelling of foot prosthetic, which resembles the human foot structure with a rigid structure embedded in soft matrix is also presented and discussed

J-Integral Calculation by Finite Element Processing of Measured Full-Field Surface Displacements

© 2017 The Author(s)A novel method has been developed based on the conjoint use of digital image correlation to measure full field displacements and finite element simulations to extract the strain energy release rate of surface cracks. In this approach, a finite element model with imported full-field displacements measured by DIC is solved and the J-integral is calculated, without knowledge of the specimen geometry and applied loads. This can be done even in a specimen that develops crack tip plasticity, if the elastic and yield behaviour of the material are known. The application of the method is demonstrated in an analysis of a fatigue crack, introduced to an aluminium alloy compact tension specimen (Al 2024, T351 heat condition)