A probabilistic framework is developed utilizing a two-scale modeling approach to efficiently consider the material variability that is typical for composite materials. The modeling integrates a macro-scale model with effective elastic properties extracted from micro-mechanical simulations. Using a weakest link modeling approach for fatigue assessment the combined effects of defects on fatigue strength in a Carbon Fiber Reinforced Polymer (CFRP) material can be investigated. A full fatigue test program is presented and is used to calibrate the probabilistic fatigue model. By including material variability in the numerical model, the calibrated probabilistic model improves the fatigue life prediction.

The study presents a two-scale modeling approach allowing for an efficient fatigue strength evaluation on a macro scale considering a micro-mechanical defect characterization of a Carbon Fiber Reinforced Polymer (CFRP) material. The modeling approach integrates a macro model with the effective elastic properties from micro-mechanical simulations considering voids. This enables the analysis of defects’ influence on material fatigue strength using a probabilistic weakest link approach. A CFRP laminate with a cross-ply layup was investigated. Two simulation case studies demonstrate the effect of void content and size on the characteristic fatigue strength. An experimental investigation was conducted testing the laminates in tension–tension fatigue verifying the predicted numerical behavior. The numerical models identify a difference in the characteristic fatigue strength consistent with the fatigue test results. It is numerically concluded that the investigated CFRP material's fatigue strength is affected by the presence of voids and even with only a slight difference in the global void volume fraction a scatter in fatigue strength is identified.

Our society depends on functional road communication, and Heavy Duty Vehicles (HDVs) offer convenient and limitless possibilities of transport and services. However, HDVs account for a quarter of the European Union's CO2 road emissions. There is a substantial need to reduce the CO2 emissions of HDVs to ensure a low negative environmental impact. To reduce the CO2 emissions of HDVs, their energy usage must be reduced. One way to reduce energy usage is to improve the structural efficiency of the vehicle and use high-performance composite materials such as Carbon Fiber Reinforced Polymers (CFRP). 

HDVs are continuously exposed to road-induced vibrations, and the fatigue loading often sets the design criteria for HDV components. Therefore, flexible simulation frameworks are needed to encourage and simplify the implementation of composite materials in engineering structural designs dimensioned for fatigue. This doctoral thesis proposes a probabilistic modeling framework for fatigue assessment of CFRP. The thesis aims to provide knowledge and insights into the fatigue modeling of composite materials and a better understanding of the proposed modeling framework.

A combination of experimental investigations and numerical modeling is conducted. To carry out fatigue testing, a fatigue testing procedure was established. Fatigue testing of anisotropic material involves accurately selecting process parameters to obtain specimens that fail in the gauge length. The fatigue damage progression of CFRP laminates was monitored throughout the fatigue tests by analyzing the stiffness change, finding that the initial stiffness loss can be related to the damage development of the specimens. 

Composite materials are multi-scale, where constituents and damage are of a much lower order length scale than the laminate and structure. Therefore, the numerical modeling uses a two-scale modeling approach to capture the variability of a composite laminate. First, the micro-scale modeling uses Representative Volume Elements (RVE) to determine the effective macro-mechanical properties of a composite lamina. The RVE models are generated based on experimental data capturing micro-geometrical variations that could affect the composite laminate behavior. Second, macro-scale models, capturing the complexity and variability of composite materials, are used in a probabilistic modeling approach for fatigue assessment. A Weibull distribution in a weakest link formulation is used to consider the combined effect of material variability of a CFRP laminate. 

The work proposes a probabilistic fatigue modeling framework for implementation in an industrial design process. The methodology is highly valuable in the progress of fatigue modeling of composites. It aims to encourage and simplify the implementation of composites in engineering structural designs and components dimensioned for fatigue. The insights and outcomes of this doctoral thesis play a crucial role in the advancement of future resource-efficient vehicles and an optimal selection of materials to design for the right material in the right place.

Vårt samhälle är byggt för en fungerande vägskommunikation och lastbilar erbjuder flexibla lösningar för transporter och tjänster. Lastbilstransporter står emellertid för en fjärdedel av Europeiska Unionens CO2-utsläpp och i arbetet för ett mer hållbart transportsystem finns ett behov av att minska CO2-utsläppen för tunga fordon för att säkerställa en minimal miljöpåverkan. För att minska CO2-utsläppen för lastbilar måste deras energianvändning minskas. Ett sätt att minimera energianvändningen är att effektivisera lastbilens strukturella design och använda högpresterande material som kolfiberarmerade polymerer.

Tunga fordon utsätts kontinuerligt för väginducerade vibrationer och denna typ av utmattningslast sätter ofta designkriterierna för lastbilskomponenter. Därför behövs flexibla simuleringsmetoder för att främja och förenkla användandet av kompositmaterial i komponenter som dimensioneras för utmattning. Denna doktorsavhandling föreslår en probabilistisk modelleringsmetodik för att utvärdera utmattning av kolfiberkompositer. Avhandlingen bidrar till kunskap och insikt om utmattningsmodellering av kompositer samt en fördjupad förståelse av den föreslagna modelleringsmetodiken.

Arbetet består av experimentell provning och numerisk simulering. För att utföra utmattningsprovning etablerades en metodik som hjälper till att noggrant välja de parametrar som behövs för en lyckad provning av anisotropa material. För att bättre förstå skadeprogressionen hos kolfiberlaminat mäts och analyseras styvheten av provstaven under provets gång. Det kan konstateras att den initiala förlusten av styvhet kan relateras till skadeutvecklingen hos provstavarna.

Beståndsdelarna i kompositmaterial är av en mycket mindre längd-skala än laminatet. Därför används en tvåstegsmodelleringsteknik, mikro- och makromodellering, för att fånga de naturliga variationerna i materialet. Mikromodelleringen använder sig av representativa volymselement för att bestämma de effektiva makromekaniska egenskaperna hos ett kompositlaminat. De representativa volymselementen genereras baserat på experimentell data för att ta hänsyn till mikrogeometriska variationer som kan påverka kompositlaminatets beteende. Med avseende på den komplexitet och variation som kompositer uppvisar valdes en probabilistisk modelleringsmetodik för utmattning. En Weibull-fördelning i en Weakest link formulering användes för att utvärdera den kombinerade effekten av materialvariationer hos ett kolfiberlaminat baserat på numeriska makromodeller. 

Doktorsavhandlingen presenterar en probabilistisk utmattningmodelleringsmetodik som ska vara lämplig för en industriell designprocess. Den utvecklade metoden är av stort värde för framsteg inom utmattningsmodellering av kompositer och syftar till att möta behov samt främja användningen av kompositer i komponenter som är dimensionerade för utmattning. Resultaten av denna doktorsavhandling spelar en avgörande roll i utvecklingen av framtida resurseffektiva fordon och för innovativa konstruktioner som använder rätt material på rätt plats.

Global Sensitivity Analysis (GSA) is a well-established approach to support simulation-driven design decisions where the dependency between the simulation's output and the model's input is quantifed. However, classical GSA approaches, such as Sobol' indices based on Monte Carlo Simulations (MCS), are not convenient when computationally expensive simulation models such as Representative Volume Elements (RVE) are used as the model to analyze. A simulation framework is developed with a metamodeling-based GSA to overcome the aforementioned cost of the MCS approaches. The developed framework has been applied in a Multi-Scale Modeling (MSM) framework replacing a micromechanical RVE simulation with three different metamodels for performing GSA. The micromechanical model predicts the stiffness of a Carbon Fiber Reinforced Polymer (CFRP) material and the GSA can quantify how the experimental material parameters affect the material properties. The obtained sensitivity analysis demonstrates that void size is the most influential parameter on the outputs of interest, and the metamodel-based GSA is a computationally convenient approach.

The intensified pursuit for lightweight solutions in the commercial vehicle industry increases the demand for method development of more advanced lightweight materials such as Carbon-Fiber-Reinforced Composites (CFRP). The behavior of these anisotropic materials is challenging to understand and manufacturing defects could dramatically change the mechanical properties. Voids are one of the most common manufacturing defects; they can affect mechanical properties and work as initiation sites for damage. It is essential to know the micromechanical composition of the material to understand the material behavior. Void characterization is commonly conducted using optical microscopy, which is a reliable technique. In the current study, an approach based on optical microscopy, statistically characterizing a CFRP laminate with regard to porosity, is proposed. A neural network is implemented to efficiently segment micrographs and label the constituents: void, matrix, and fiber. A neural network minimizes the manual labor automating the process and shows great potential to be implemented in repetitive tasks in a design process to save time. The constituent fractions are determined and they show that constituent characterization can be performed with high accuracy for a very low number of training images. The extracted data are statistically analyzed. If significant differences are found, they can reveal and explain differences in the material behavior. The global and local void fraction show significant differences for the material used in this study and are good candidates to explain differences in material behavior.

A micromechanical simulation approach in a Multi-Scale Modeling (MSM) framework with the ability to consider manufacturing defects is proposed. The study includes a case study where the framework is implemented exploring a cross-ply laminate. The proposed framework highlights the importance of correct input regarding micromechanical geometry and void characteristics. A Representative Volume Element (RVE) model is developed utilizing true micromechanical geometry extracted from micrographs. Voids, based on statistical experimental data, are implemented in the RVE model, and the effects on the fiber distribution and effective macromechanical properties are evaluated. The RVE algorithm is robust and maintains a good surrounding fiber distribution around the implemented void. The local void fraction, void size, and void shape affect the effective micromechanical properties, and it is important to consider the phenomena of the effective mechanical properties with regard to the overall void fraction of an RVE and the actual laminate. The proposed framework has a good prediction of the macromechanical properties and shows great potential to be used in an industrial implementation. For an industrial implementation, weak spots and critical areas for a laminate on a macro-level are found through combining local RVEs.

Lightweight design is important for Battery Electric Vehicles (BEVs), to minimize the effects from the added weight of the batteries. The study looks at the benefits and  disadvantages of choosing a Carbon Fiber Reinforced Polymer (CFRP) material in comparison to metallic material for a specific battery electric commercial vehicle component. A Life Cycle Energy (LCE) and weight analysis are the basis for the comparison. Other aspects that could be considered important for the industrial implementation, such as cost, are also discussed. The LCE is assessed using a combination of engineering process modelling, available data from industrial partners, and data available in the literature. The analysis is aimed to support a holistic comparison, which means the modelling is performed on an overarching level of detail.

Fatigue testing of a Carbon Fiber Reinforced Polymer (CFRP) in tension-tension loading has been conducted. In-situ surface strain measurements were performed to examine the gradual elongation of the specimen as this relates to stiffness loss and fatigue damage. A methodology capturing the specimen at peak load has been developed, including an automated trigger mechanism that activates the camera at the desired cycle count. The material tested was a Unidirectional (UD) Non-Crimp Fabric (NCF) with carbon fibers and an epoxy matrix. The fatigue test results revealed a wide scatter in the mid-range of the high cycle fatigue region. By studying the strain in the early fatigue loading cycles and the stiffness loss over time, benchmark of the fatigue performance between different material samples could be carried out, explaining the scatter in the fatigue testing. It could be observed that the fatigue limit of the UD CFRP material in the fiber direction is in the magnitude of 80 % of the material’s Ultimate Tensile Strength (UTS).

There is an increase in the use of fiber reinforced polymer materials in the vehicle industry due to the material's significance in designing lightweight vehicle structures and components. For these structures and components structural mechanical properties are important to characterize, particularly the fatigue properties. An approach on setting up a systematical fatigue testing procedure to find an optimal specimen design for the provided lab environment is proposed. It is found that the tab configuration and tab material have a large impact on the test results. The proposed test procedure results in fatigue failure of the CFRP material rather than tab failure. The final method has resulted in successfully testing parallel-side-coupon specimens under tension-tension fatigue testing, with a load ratio of 0.1 and with a frequency of 5 Hz.