Högskolan i Skövde

The usage of multi material structures in industry, especially in the automotive industry are increasing. To overcome the difficulties in joining these structures, adhesives have several benefits over traditional joining methods. Therefore, accurate simulations of the entire process of fracture including the adhesive layer is crucial. In this paper, material parameters of a previously developed meso mechanical finite element (FE) model of a thin adhesive layer are optimized using the Strength Pareto Evolutionary Algorithm (SPEA2). Objective functions are defined as the error between experimental data and simulation data. The experimental data is provided by previously performed experiments where an adhesive layer was loaded in monotonically increasing peel and shear. Two objective functions are dependent on 9 model parameters (decision variables) in total and are evaluated by running two FEsimulations, one is loading the adhesive layer in peel and the other in shear. The original study converted the two objective functions into one function that resulted in one optimal solution. In this study, however, a Pareto frontis obtained by employing the SPEA2 algorithm. Thus, more insight into the material model, objective functions, optimal solutions and decision space is acquired using the Pareto front. We compare the results and show good agreement with the experimental data.

A meso-mechanical finite element model for a thin adhesive layer is developed. The model is calibrated to experimental results where the adhesive layer is loaded in monotonically increasing peel or shear, cf. Andersson and Stigh [1] and Alfredsson et al. [2], and to an in situ SEM study of the fracture process. The purpose of the meso-mechanical finite element model is to facilitate the development of constitutive laws for adhesive layers.

Ideas developed by Needleman [3], where structural continuum elements are bonded by cohesive elements are used as a basis for the finite element mesh. This thus enables micro cracks to propagate along the finite element boundaries.

The simulations are found to be in good agreement with the experiments. The model is also capable of reproducing realistically the deformation observed in both peel [1] and shear [2] experiments.

Today digital qualification tools are part of many design processes that make them dependent on long and expensive simulations, leading to limited ability in exploring design alternatives. Conventional surrogate modelling techniques depend on the parametric models and come short in addressing radical design changes. Existing data-driven models lack the ability in dealing with the geometrical complexities. Thus, to address the resulting long development lead time problem in the product development processes and to enable parameter-independent surrogate modelling, this paper proposes a method to use images as input for design evaluation. Using a case study on the curtain airbag design process, a database consisting of 60,000 configurations has been created and labelled using a method based on dynamic relaxation instead of finite element methods. The database is made available online for research benchmark purposes. A convolutional neural network with multiple layers is employed to map the input images to the simulation output. It was concluded that the showcased data-driven method could reduce digital testing and qualification time significantly and contribute to real-time analysis in product development. Designers can utilise images of geometrical information to build real-time prediction models with acceptable accuracy in the early conceptual phases for design space exploration purposes.

A methodology for evaluation of transient performance of, and comparison between plate heat exchanger and plate-fin-and-tube heat exchanger was developed and realized, including experiment and 3-D simulation. Heat transfer from water to a gas medium was addressed. The heated gas volume was the same for both heat exchanger designs. This was achieved by placing the plate-fin-and-tube heat exchanger into enclosure. The volume average temperature of the gas as function of time was computed. Estimated material cost for the studied designs was at least seven times lower than for the stainless steel plate heat exchanger. The performance of the selected plate-fin-and-tube heat exchanger design was found comparable to the plate heat exchanger, when both fin and tube materials were set to Al, and the enclosure was a light-weight thermal insulator. Transient behavior of the studied heat exchangers should be of interest for micro-grid applications, but also for thermal management in electronic cabinets and data centers.

Neonatal sepsis is common, lethal, and hard to diagnose. In combination with clinical findings and blood culture, biomarkers are crucial to make the correct diagnose. A Swedish national inquiry indicated that neonatologists were not quite satisfied with the available biomarkers. We assessed the kinetics of 15 biomarkers simultaneously: ferritin, fibrinogen, granulocyte colony-stimulating factor (G-CSF), interferon (IFN)-γ, interleukin (IL)-1β, −6, −8, −10, macrophage inflammatory protein (MIP)-1β, procalcitonin, resistin, serum amyloid A (SAA), tumor necrosis factor (TNF)-α, tissue plasminogen activator-3 and visfatin. The goal was to observe how quickly they rise in response to infection, and for how long they remain elevated. From a neonatal intensive care unit, newborns ≥28 weeks gestational age were recruited. Sixty-eight newborns were recruited to the study group (SG), and fifty-one to the control group (CG). The study group subjects were divided into three subgroups depending on clinical findings: confirmed sepsis (CSG), suspected sepsis (SSG) and no sepsis. CSG and SSG were also merged into an entire sepsis group (ESG) for sub-analysis. Blood samples were collected at three time-points; 0 h, 12–24 h and 48–72 h, in order to mimic a “clinical setting”. At 0 h, visfatin was elevated in SSG compared to CG; G-CSF, IFN-γ, IL-1β, −8 and − 10 were elevated in SSG and ESG compared to CG, whereas IL-6 and SAA were elevated in all groups compared to CG. At 12–24 h, IL-8 was elevated in ESG compared to CG, visfatin was elevated in ESG and SSG compared to CG, and SAA was elevated in all three groups compared to CG. At 48–72 h, fibrinogen was elevated in ESG compared to CG, IFN-γ and IL-1β were elevated in SSG and ESG compared to CG, whereas IL-8 and SAA were elevated in all three groups compared to CG. A function of time-formula is introduced as a tool for theoretical prediction of biomarker levels at any time-point. We conclude that SAA has the most favorable kinetics regarding diagnosing neonatal sepsis, of the biomarkers studied. It is also readily available methodologically, making it a prime candidate for clinical use. 

To advance quality assurance in the welding process, this study presents a deep learning (DL) model that enables the prediction of two critical welds’ key performance characteristics (KPCs): welding depth and average pore volume. In the proposed approach, a wide range of laser welding key input characteristics (KICs) is utilized, including welding beam geometries, welding feed rates, path repetitions for weld beam geometries, and bright light weld ratios for all paths, all of which were obtained from hairpin welding experiments. Two DL networks are employed with multiple hidden dense layers and linear activation functions to investigate the capabilities of deep neural networks in capturing the complex nonlinear relationships between the welding input and output variables (KPCs and KICs). Applying DL networks to the small numerical experimental hairpin welding dataset has shown promising results, achieving mean absolute error values of 0.1079 for predicting welding depth and 0.0641 for average pore volume. This, in turn, promises significant advantages in controlling welding outcomes, moving beyond the current trend of relying only on defect classification in weld monitoring to capture the correlation between the weld parameters and weld geometries.

This study focuses on abrasion resistance of Lamellar Graphite Iron (LGI) using microscratch test under constant and progressive load conditions. The interactions between a semi-spherical abrasive particle, cast iron matrix and graphite lamellas were physically simulated using a sphero-conical indenter. The produced scratches were analysed using LOM and SEM to scrutinise the effect of normal load on resulting scratch depth, width, frictional force, friction coefficient and deformation mechanism of matrix during scratching. Results showed a significant matrix deformation, and change both in frictional force and friction coefficient by increase of scratch load. Furthermore, it was shown how abrasive particles might produce deep scratches with severe matrix deformation which could result in graphite lamella's coverage and thereby deteriorate LGI's abrasion resistance.

In this paper, we propose a stabilized augmented Lagrange multiplier method for the finite element solution of small deformation elastic contact problems. We limit ourselves to friction-free contact with a rigid obstacle, but the formulation is readily extendable to more complex situations. © 2016 Elsevier B.V.

We propose a discontinuous finite element method for small strain elasticity allowing for cohesive zone modeling. The method yields a seamless transition between the discontinuous Galerkin method and classical cohesive zone modeling. Some relevant numerical examples are presented. © 2015 Elsevier B.V.

The purpose of this work is to develop a flexible cohesive law to simulate the constitutive behaviour of an adhesive layer under mixed mode loading. A mixed mode cohesive law that captures the linear elastic and softening behaviour before fracture is presented. This simple model uses a coupled formulation to describe the mixed mode cohesive behaviour. It also allows for different fracture parameters, such as fracture energy, strength and critical separation in different mode mixities. Thus, the fracture process in mode I (peel), in mode II (shear) or in mixed mode (a combination of peel and shear) can be modelled without the usual constraint of a common fracture energy in peel and shear. Examples are given of FE-implementation of the normalised cohesive law, namely for the Unsymmetric Double Cantilever Beam (UDCB) specimen and the Mixed-mode double Cantilever Beam (MCB) specimen. Both specimens are adhesively bonded and loaded in mixed-mode.

This paper aims to present a methodology to predict the anisotropic and non-linear behaviour of glass fibre reinforced plastics using finite element methods. A material model is implemented in order to remedy the need of multiple material definitions, and to control the local plastic behaviour as a function of the fibre orientation. Injection moulding simulations traditionally provide second order orientation tensors, which are considered together with a homogenization scheme to compute local material properties. However, in the present study, fourth order tensors are used in combination with traditional methods to provide more accurate material properties. The elastic and plastic response of the material model is optimized to fit experimental test data, until simulations and experiments overlap. The proposed material model can support design engineers in making more informed decisions, allowing them to create smarter products without the need of excessive safety factors, leading to reduced component weight and environmental impact. 

Cast components generally show a heterogeneous distribution of material properties, caused by variations in the microstructure that forms during solidification. Variations caused by the casting process are not commonly considered in structural analyses, which might result in manufacturing of sub-optimised components with unexpected in-use behaviour. In this paper, we present a methodology which can be used to consider both thermomechanical and thermophysical variations using finite element analyses in cast components. The methodology is based on process simulations including microstructure modelling and correlations between microstructural features and material properties. Local material data are generated from the process simulation results, which are integrated into subsequent structural analyses. In order to demonstrate the methodology, it is applied to a cast iron cylinder head. The heterogeneous distribution of material properties in this component is investigated using experimental methods, demonstrating local variations in both mechanical and physical behaviour. In addition, the strength-differential effect on tensile and compressive behaviour of cast iron is considered in the modelling. The integrated simulation methodology presented in this work is relevant to both design engineers, production engineers as well as material scientists, in order to study and better understand how local variations in microstructure might influence the performance and behaviour of cast components under in-use conditions.

This paper presents a methodology that can be used to consider local variations in thermomechanical and thermophysical material properties, residual stresses, and strength-differential effects in finite element analyses of cast components. The methodology is based on applying process simulations and structural analyses together with experimentally established, or already available literature data, in order to describe element-specific material variations. A cast-iron cylinder head was used in order to evaluate the influence of several simplifications that are commonly performed in computer aided engineering. It is shown that non-trivial errors of a potentially large magnitude are introduced by not considering residual stresses, compressive behaviour, temperature dependence, and process-induced material property variations. By providing design engineers with tools that allow them to consider the complex relationships between these aspects early in the development phase, cast components have the potential to be further optimized with respect to both weight and performance.

In this paper we present a semi-multiscale methodology, where a micrograph is split into multiple independent numerical model subdomains. The purpose of this approach is to enable a controlled reduction in model fidelity at the microscale, while providing more detailed material data for component level- or more advanced finite element models. The effective anisotropic elastic properties of each subdomain are computed using periodic boundary conditions, and are subsequently mapped back to a reduced mesh of the original micrograph. Alternatively, effective isotropic properties are generated using a semi-analytical method, based on averaged Hashin–Shtrikman bounds with fractions determined via pixel summation. The chosen discretization strategy (pixelwise or partially smoothed) is shown to introduce an uncertainty in effective properties lower than 2% for the edge-case of a finite plate containing a circular hole. The methodology is applied to a aluminium alloy micrograph. It is shown that the number of elements in the aluminium model can be reduced by 99.89 % while not deviating from the reference model effective material properties by more than 0.65 % , while also retaining some of the characteristics of the stress-field. The computational time of the semi-analytical method is shown to be several orders of magnitude lower than the numerical one. © 2021, The Author(s).

The strain localization and crack formation in ferritic-pearlitic ductile iron under tension was investigated by in-situ tensile tests. In-situ tensile tests under optical microscope were performed and the onset of the early ferrite-graphite decohesions and micro-cracks inside the matrix were studied. The results revealed that early ferrite-graphite decohesion and micro-cracks inside the ferrite were formed at the stress range of 280–330 MPa, where a kink occurred in the stress-strain response, suggesting the dissipation of energy in both plastic deformation and crack initiation. Some micro-cracks initiated and propagated inside the ferrite but were arrested within the ferrite zone before propagating in the pearlite. Digital Image Correlation (DIC) was used to measure local strains in the deformed micrographs obtained from the in-situ tensile test. Higher strain localization in the microstructure was measured for the areas in which the early ferrite-graphite decohesions occurred or the micro-cracks initiated. © 2017 Elsevier B.V.

This paper presents a study on microstructural deformation of a ferritic-pearlitic ductile iron, utilizing in-situ tensile testing, digital image correlation (DIC) and finite element analysis (FEA). For this purpose, the in-situ tensile test and DIC were used to measure local strain fields in the deformed microstructure. Furthermore, a continuum finite element (FE) model was used to predict the strain maps in the microstructure. Ferrite and pearlite parameters for the FE-model were optimized based on the Ramberg-Osgood relation. The DIC and simulation strain maps were compared qualitatively and quantitatively. Similar strain patterns containing shear bands in identical locations were observed in both strain maps. The average and localized strain values of the DIC and simulation conformed to a large extent. It was found that the Ramberg-Osgood model can be used to capture the main trends of strain localization. The discrepancies between the simulated and DIC results were explained based on the; (i) subsurface effect of the microstructure; (ii) differences in the strain spatial resolutions of the DIC and simulation and (iii) abrupt changes in strain prediction of the continuum FE-model in the interface of the phases due to the sudden changes in the elastic modulus. © 2015 Elsevier B.V.

This paper focuses on local strain distribution in the microstructure of high silicon ductile iron during cyclic loading. In-situ cyclic test was performed on compact-tension (CT) samples inside the scanning electron microscope (SEM) to record the whole deformation and obtain micrographs for microstructural strain measurement by means of digital image correlation (DIC) technique. Focused ion beam (FIB) milling was used to generate speckle patterns necessary for DIC measurement. The equivalent Von Mises strain distribution was measured in the microstructure at the maximum applied load. The results revealed a heterogeneous strain distribution at the microstructural level with higher strain gradients close to the notch of the CT sample and accumulated strain bands between graphite particles. Local strain ahead of the early initiated micro-cracks was quantitatively measured, showing high strain localization, which decreased by moving away from the micro-crack tip. It could be observed that the peak of strain in the field of view was not necessarily located ahead of the micro-cracks tip which could be because of the (i) strain relaxation due to the presence of other micro-cracks and/or (ii) presence of subsurface microstructural features such as graphite particles that influenced the strain concentration on the surface.

The keyhole depth is a key measurement characteristic in the laser welding of busbar to battery tabs in battery packs for electric vehicles (EV), as it directly affects the quality of the weld. In this work, experiments are carried out with controlled and adjusted laser power and feed rate parameters to investigate the influence on the keyhole width, keyhole depth and porosities. A 3D numerical model of laser keyhole welding of an aluminum alloy (A1050) has been developed to describe the porosity formation and the keyhole depth variation. A new integration model of the recoil pressure and the rate of evaporation model is implemented which is closer to the natural phenomena as compared to the conventional methods. Additionally, major physical forces are employed including plume formation, upward vapor pressure and multiple reflection in the keyhole. The results show that keyhole depth is lower at higher feed rate, while lower feed rates result in increased keyhole depth. This study reveals that low energy densities result in an unstable keyhole with high spattering, exacerbated by increased laser power. Mitigating incomplete fusion is achieved by elevating laser energy density. The findings emphasize the critical role of keyhole depth in optimizing laser welding processes for applications like busbar-to-battery tab welding.

Many high-level technical products are associated with changing requirements, drastic design changes, lack of design information, and uncertainties in input variables which makes their design process iterative and simulation-driven. Regression models have been proven to be useful tools during design, altering the resource-intensive finite element simulation models. However, building regression models from computer-aided design (CAD) parameters is associated with challenges such as dealing with too many parameters and their low or coupled impact on studied outputs which ultimately requires a large training dataset. As a solution, extraction of hidden features from CAD is presented on the application of volume simulation of curtain airbags concerning geometric changes in design loops. After creating a prototype that covers all aspects of a real curtain airbag, its CAD parameters have been analyzed to find out the correlation between design parameters and volume as output. Next, using the design of the experiment latin hypercube sampling method, 100 design samples are generated and the corresponding volume for each design sample was assessed. It was shown that selected CAD parameters are not highly correlated with the volume which consequently lowers the accuracy of prediction models. Various geometric entities, such as the medial axis, are used to extract several hidden features (referred to as sleeping parameters). The correlation of the new features and their performance and precision through two regression analyses are studied. The result shows that choosing sleeping parameters as input reduces dimensionality and the need to use advanced regression algorithms, allowing designers to have more accurate predictions (in this case approximately 95%) with a reasonable number of samples. Furthermore, it was concluded that using sleeping parameters in regressionbased tools creates real-time prediction ability in the early development stage of the design process which could contribute to lower development lead time by eliminating design iterations. 

During the casting process and solidification of ductile iron castings, a heterogeneous microstructure is formed throughout the casting. This distribution is highly controlled by process related factors, as chemical composition, local solidification conditions, and the geometry of the casting. Geometrical changes to the geometry of the casting thus alters the local mechanical behaviour, as well as the distribution of stresses and strains when the casting is subjected to load. In order to find an optimal geometry, e.g. with reduced weight and increased load-bearing capacity, this interdependency between geometry and local material behaviour needs to be considered and integrated into the optimization method. In this contribution, recent developments in the multidisciplinary integration of casting process simulation, solidification and microstructure modelling, microstructure-based material characterization, Finite Element Analyses (FEA) with local material behaviour and structural optimization techniques are presented and discussed. The effect and relevance of considering the local material behaviour in shape optimization of ductile iron castings is discussed and evidenced by an industrial application. It is shown that by adopting a multidisciplinary optimization approach by integration of casting simulation and local material behaviour into shape optimization, the potential of the casting process to obtain components with high performance and reliability can be enabled and utilized.

During the casting process and solidification of ductile iron castings, a heterogeneous microstructure is formed throughout the casting. This distribution is strongly influenced by the item geometry and the process related factors, as chemical composition and local solidification conditions. Geometrical changes to the geometry of the casting thus alters the local mechanical behavior and properties, as well as the distribution of stresses and strains when the casting is subjected to load. In order to find an optimal geometry, e.g. with reduced weight and increased load-bearing capacity, this interdependency between geometry and local material behavior needs to be considered and integrated into the optimization method. In this contribution, recent developments in the multidisciplinary integration of casting process simulation, solidification and microstructure modelling, microstructure-based material characterization, finite element structural analyses with local material behavior and structural optimization techniques are presented and discussed. The effect and relevance of considering the local material behavior in shape optimization of ductile iron castings is discussed and evidenced by an industrial application. It is shown that by adopting a multidisciplinary optimization approach by integration of casting simulation and local material behavior into shape optimization, the potential of the casting process to obtain components with high performance and reliability can be enabled and utilized. 

Tailored heterogeneous distributions of microstructural features enable extraordinary material performance in biological and physiological structures such as trees, the aortic arch, human teeth and dinosaur skulls. In ductile iron, a heterogeneous distribution in size and morphology of graphite nodules and variations of the fractions of ferrite and pearlite are created during solidification, and varies as a function of parameters such as local cooling rate, segregation and flow. In the current work, the size distribution as well as the orientation and relation between graphite nodules is obtained by a three-dimensional reconstruction of a ductile iron microstructure from X-ray tomography. The effect of the nodule morphology and clustering on the localization of plastic strains is studied numerically using finite element analysis of the reconstructed microstructure. Real castings have a variation in geometry, solidification conditions and are subjected to variations in loads. A framework for optimized geometry and solidification conditions in order to design and deliver castings with tailored local material performance is proposed.

The local material behaviour of cast metal and injection moulded parts is highly related to the geometrical design of the part as well as to a large number of process parameters. In order to use structural optimization methods to find the geometry that gives the best possible performance, both the geometry and the effect of the production process on the local material behaviour thus has to be considered. In this work, a multidisciplinary methodology to consider local microstructure-based material behaviour in optimizations of the design of engineering structures is presented. By adopting a knowledge based industrial product realisation perspective combined with a previously presented simulation strategy for microstructure-based material behaviour in Finite Element Analyses (FEA), the methodology integrates Computer Aided Design (CAD), casting and injection moulding simulations, FEA, design automation and a multi-objective optimization scheme into a novel structural optimization method for cast metal and injection moulded polymeric parts. The different concepts and modules in the methodology are described, their implementation into a prototype software is outlined, and the application and relevance of the methodology is discussed. 

The mechanical behaviour and performance of a ductile iron component is highly dependent on the local variations in solidification conditions during the casting process. Here we show a framework which combine a previously developed closed chain of simulations for cast components with a micro-scale Finite Element Method (FEM) simulation of the behaviour and performance of the microstructure. A casting process simulation, including modelling of solidification and mechanical material characterization, provides the basis for a macro-scale FEM analysis of the component. A critical region is identified to which the micro-scale FEM simulation of a representative microstructure, generated using X-ray tomography, is applied. The mechanical behaviour of the different microstructural phases are determined using a surrogate model based optimisation routine and experimental data. It is discussed that the approach enables a link between solidification- and microstructure-models and simulations of as well component as microstructural behaviour, and can contribute with new understanding regarding the behaviour and performance of different microstructural phases and morphologies in industrial ductile iron components in service.

Numerical simulations of component behavior and performance is critical to develop optimized and robust load-bearing components. The reliability of these simulations depend on the description of the components material behavior, which for e.g. cast and polymeric materials exhibit component specific local variations depending on geometry and manufacturing parameters. Here an extension of a previously presented strategy, the closed chain of simulations for cast components, to predict and incorporate local material data into Finite Element Method (FEM) simulations on multiple scales is shown. Manufacturing process simulation, solidification modelling, material characterization and representative volume elements (RVE) provides the basis for a microstructure-based FEM analysis of component behavior and a simulation of the mechanical behavior of the local microstructure in a critical region. It is discussed that the strategy is applicable not only to cast materials but also to injection molded polymeric materials, and enables a common integrated computational microstructure-based approach to optimized components.

This thesis is concerned with the modeling, simulation and analysis of adhesive layers. By use of an in situ scanning electron microscopy study it is found that the adhesive studied in the present thesis has a very complex structure with two different compounds, a mineral and an epoxy/thermoplastic blend. A representative volume element (RVE) model is developed to study the behavior of the adhesive layer at the meso-level. It is a continuum model where interface finite elements are implemented at the boundaries of the continuum elements in order to enable crack initiation and propagation of micro cracks. On a structural level, two deformation modes, modes I and II, dominate the behavior of thin adhesive layers. With the RVE it is possible reproduce experimental stress-deformation relations from both modes. However, in a real structure, mixed mode loading usually occur. A range of mode mixes is studied, using the RVE, from an un-loaded state until fracture of the layer. The results indicate that the behavior of the interface elements dominate for mode mixes close to mode I and plasticity in the continuum elements dominates for mode II dominated mode mixes. Furthermore, effects of large root curvatures of the adherends is analyzed numerically by simulating plastically deforming double cantilever beam specimens using the finite element model. The developed RVE is implemented in the models to simulate the behavior of the adhesive layer. By this methodology, virtual experiments can be analyzed with extreme detail. It is shown that in-plane straining of the adhesive layer significantly influences the strength of adhesive joints at large plastic strain of the adherends. There is a never ending need in industries to minimize computational time. To this end, an interphase finite element for structural analyses is developed. The element considers in-plane straining of the adhesive layer due to large curvatures of surrounding substrates.

A thin adhesive layer is analyzed using a representative volume element (RVE). The RVE is comprised by, both continuum and interfacial finite elements. The interface elements allow for crack initiation and crack propagation. To obtain realistic results from the RVE simulation, an in situ scanning electron microscopy (SEM) study is performed. Results from the SEM study show that the adhesive has a very complex structure with two different compounds, a mineral and an epoxy/thermoplastic blend.

The RVE is subjected to two different load cases, peel and shear. An evolutionary algorithm is used to calibrate the numerical model to experimental results. The simulation results are compared to experimental results to verify the numerical model. The simulations show good agreement with the experimental results for both the peel and shear experiments.

A representative volume element is modeled using the finite element method. It is used to analyze mixed mode behavior of a thin adhesive layer. Two sources of dissipation is modeled; plasticity and decohesion. Macroscopic traction–separation laws are extracted from the simulations. The results indicate that a boundary of mode mix exists between a region where major plastic dissipation is present and a region where it is not. Without major plastic dissipation, the fracture energy is low and essentially governed by the cohesive properties. This is the case in peel dominated loading cases. In shear dominated loading cases plastic dissipation gives a substantial contribution to the fracture energy. The results show that pure shear loading gives the largest fracture energy.

A mesomechanical finite element model of a thin adhesive layer is developed. The model is calibrated to previously performed experiments. In these, the adhesive layer is loaded in monotonically increasing peel or shear. An in situ SEM study is also performed and used to guide the modeling and calibration. The purpose of the mesomechanical finite element model is to facilitate the development of constitutive laws for adhesive layers. The modeling is based on Xu and Needleman’s method where all continuum finite elements are surrounded by interface elements that allow for the development of micro cracks. Thus, this enables the modeling of the entire process of degradation and fracture of the adhesive layer. A genetic algorithm is developed for the calibration. The simulations show good agreement with the experiments.

A finite element modle of a double cantilever beam specimen is developed. The adherents are modeled using plane strain elastic continuum elements. Furthermore, the adhesive is modeled using a mesomechanical modeling teqnique wich allows for simulation of initiation and prognationb of micro-cracks. This enables the modelling of entire process of degradation and fracture of the adhesive layer. The purpose of the present study is to compare the stress-deformation behavior in an idealized peel loading to the behavior in a double cantilever beam (DCB) specimen where the adhesive layer is deformed wilt a slight gradient along the layer. Previously performed experiments and simulations of the RVE are used as a compariosn to the simulated results.

A  weighted  potential  methodology  is  developed  by  utilizing  pure  mode  I  and mode  II  energy  release  rate  experiments  to  determine  the  traction-separation  relations  for thin  adhesive  layers.  The  experimentally  measured  energy  release  rates  act  as  boundary conditions  for  developing  a  weighted  potential  function.  Thus,  the  tractions  for  any  mixed mode loading can be established.  Changes of mode mix during an experiment can also be captured  by  the  law  since  every  mixed  mode  variation  is  given  by  the  potential  function. Furthermore,  by  use  of  an  inverse  J-integral  approach  and  damage  type  variables,  the traction-separation  relations  for  any  mode  mix  can  be  approximated  by  use  of  pure  mode experiments.  Numerical  simulations  show  the  applicability  of  the  methodology.  The  results indicate  that  the  methodology  is  promising  when  simulating  the  constitutive  behavior  of adhesive layers.

In this paper, we aim at characterizing three different cast iron alloys and their microstructural features, namely lamellar, compacted and nodular graphite iron. The characterization of microscopic features is essential for the development of methods to optimize the behavior of cast iron alloys; e.g. maximize thermal dissipation and/or maximize ductility while maintaining strength. The variation of these properties is commonly analyzed by metallography on two-dimensional representations of the alloy. However, more precise estimates of the morphologies and material characteristics are obtained by three-dimensional reconstruction of microstructures. The use of X-ray microtomography provides an excellent tool to generate high resolution threedimensional microstructure images. The characteristics of the graphite constituent in the microstructure, including the size, shape and connectivity, were analyzed for the different cast iron alloys. It was observed that the lamellar and compacted graphite iron alloys have relatively large connected graphite morphologies, as opposed to ductile iron where the graphite is present as nodules. The results of the characterization for the different alloys were ultimately used to generate finite element models.

The design and production of light structures in cast iron with high static and fatigue performance is of major interest in e.g. the automotive area. Since the casting process inevitably leads to heterogeneous solidification conditions and variations in microstructural features and material properties, the effects on multiple scale levels needs to be considered in the determination of the local fatigue performance. In the current work, microstructural features of different cast irons are captured by use of micro X-ray tomography, and 3D finite element models generated. The details of the 3D microstructure differ from the commonly used 2D representations in that the actual geometry is captured and that there is not a need to compensate for 3D-effects. The first objective with the present study is to try and highlight certain aspects at the micro scale that might be the underlying cause of fatigue crack initiation, and ultimately crack propagation, under fatigue loading for cast iron alloys. The second objective is to incorporate the gained knowledge about the microstructural behavior into multi-scale simulations at a structural length scale, including the local damage level obtained in the heterogeneous structure subjected to fatigue load.

A special purpose finite element is developed for structural simulations of complex adhesively bonded structures. In the interphase element, the adhesive is explicitly regarded as a material phase between two substrates. The element considers large rotations. Furthermore. it considers in-plane straining of the adhesive due to large curvatures of the bonded shells. This feature appears especially important when considering bonding of thin plastically deforming metallic shell structures. Simulations are made on specimens where the adherends deform both elastically and plastically. The results are in good agreement with previously performed experiments. Copyright (0 2008 John Wiley & Sons, Ltd.

Previously performed experiments to study the mode I behavior of an adhesive layer revealed an apparent increase in the fracture toughness when the adherends deformed plastically. Attempts to simulate the experiments are made; both with elastically and plastically deforming adherends. Thus, effects of the size of the process zone and the deformation of the adherends are revealed. The adhesive layer is modeled using finite elements with different approaches; cohesive elements and representative volume elements. The adherends are modeled with solid elements. With a long process zone, all models give good results as compared to the experiments. However, only the model with representative volume elements gives good agreement for large root curvatures and correspondingly short process zones. The results are interpreted by analyzing the deformation and mechanisms of crack propagation in the representative volume elements. It is shown that with large root curvature of the adherends, the in-plane stretching of the adhesive layer gives a substantial contribution to the fracture energy. A simple formula is derived and shown to give an accurate prediction of the effects of the root curvature. This result indicates the limits of conventional cohesive zone modeling of an adhesive layer of finite thickness.

A detailed model of experiments with the double cantilever beam specimen is set up. Analysis of the model shows that an experimentally deduced apparent increase of fracture energy with severely deforming adherends is due to contributions of in-plane straining of the adhesive layer to the fracture energy. An analysis with the J-integral confirms the result.

A finite element analysis of a complex assembly was made. The material description used was a physically based material model with dislocation density as an internal state variable. This analysis showed the importance of the materials’ behavior in the process as there is discrepancy between the bolt head contact pressure and the internals state of the materials where the assembly process allows for recovery. The end state is governed by both the tightening process and the thermal history and strongly influenced by the thermal expansion of the AZ91D alloy.

An overview of recent development of cohesive modelling is given. Cohesive models are discussed in general and specifically for the modelling of adhesive layers. It is argued that most cohesive models model a material volume and not a surface. Detailed microscopic and mesomechanical studies of the fracture process of an engineering epoxy are discussed. These studies show how plasticity on the mesomechanical length scale contributes to the fracture energy in shear dominated load cases. Methods to measure cohesive laws are presented in a general setting. Conclusions and conjectures based on experimental and mesomechanical studies are presented. The influence of temperature and strain rate on the peak stress and fracture energy of cohesive laws indicates fundamentally different mechanisms responsible for these properties. Experiments and mesomechanical studies show that in-plane straining of an adhesive layer can give large contributions to the registered fracture energy. Finite element formulations including a method to incorporate this influence are discussed.

The manufacturing process gives cast iron castings properties which are dependent on component design, metallurgy and casting method. Factors such as local wall thickness influences the coarseness and type of microstructure and the castings will have local properties depending on the local metallurgical and thermal history. The stress/strain behaviour of cast materials is typically determined by performing a tensile test in a tensile test machine. The deformation behaviour will normally be determined by two mechanisms, namely, elastic and plastic phenomena. The plastic behaviour is based on dislocation movements in the lattice. Commonly, the deformation history of cast iron involves elastic, plastic and crack phases. The cast iron material has a complex microstructure and first order equations cannot be used to predict the deformation during loading. Until methods have been developed, the characterization of complex microstructure materials such as cast iron has to be determined by use of empirical methods. The empirical methods have to couple the internal microstructure and composition of the material with deformation phenomena during loading. The paper will show a method to characterize tensile test curves of cast iron materials which can be used to couple deformation phenomena with for example microstructure. The equations are aimed to make the tensile test curve ready for curve fitting and optimization in two steps. Each stress/strain curve is like a finger print of the material and requires well performed tests and some advices are given. The paper also wants to encourage researchers and people working with tensile testing to get out more of their effort to measure strength of cast iron materials and connect the result to the microstructure of the specimens.