Högskolan i Skövde

Low-austenitizing temperature practices resulted in substantial changes in both microstructure and mechanical properties of the fully ferritic as-cast Compacted Graphite Irons (CGI). The austempering processes were accomplished through first austenitizing at 850 °C for 60 min followed by quenching in a salt-bath at 275, 325, and 375 °C for times ranging from 30, 60, 90, and 120 min. In contrast with the austenitizing performed at 900 °C performed on the same material, the microstructure consisted of a notable volume fraction of proeutectoid ferrite, which was not observed under similar austempering temperature and time conditions. Lowering the austenitizing temperature to 850 °C resulted in decreased untransformed austenite. Depending on the austempering conditions, a notable improvement was achieved in both Brinell and Vickers hardness compared to the as-cast CGI. The ausferrite matrix led to remarkable increases in yield strength (YS), ultimate tensile strength (UTS), and a decrease in total elongation to failure. The highest YS and UTS values were achieved for specimens austempered at 275 °C while increasing the austempering temperature decreased both YS and UTS. Furthermore, the results showed that the austempering temperature had a more significant impact on YS and UTS than the austempering time. All austempered CGI specimens exhibited primarily brittle failure attributes, while ferritic CGIs showed a mixed failure mode.

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.

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.

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.

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. 

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).

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. 

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.

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.

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.