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A Statistical Porosity Characterization Approach of Carbon-Fiber-Reinforced Polymer Material Using Optical Microscopy and Neural Network
KTH, School of Engineering Sciences (SCI), Centres, VinnExcellence Center for ECO2 Vehicle design. KTH, School of Engineering Sciences (SCI), Engineering Mechanics. Scania CV AB, SE-15187 Södertälje, Sweden.ORCID iD: 0000-0001-8869-4622
RISE Res Inst Sweden, Mat & Prod Polymers Fibers & Composites, SE-16440 Stockholm, Sweden..ORCID iD: 0000-0001-6729-8604
KTH, School of Engineering Sciences (SCI), Centres, VinnExcellence Center for ECO2 Vehicle design. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics.ORCID iD: 0000-0003-0198-6660
KTH, School of Engineering Sciences (SCI), Engineering Mechanics. KTH, School of Engineering Sciences (SCI), Centres, VinnExcellence Center for ECO2 Vehicle design.ORCID iD: 0000-0003-4180-4710
2022 (English)In: Materials, E-ISSN 1996-1944, Vol. 15, no 19, article id 6540Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI AG , 2022. Vol. 15, no 19, article id 6540
Keywords [en]
Carbon-Fiber-Reinforced Polymer, porosity, Convolutional Neural Network, optical microscopy
National Category
Composite Science and Engineering
Identifiers
URN: urn:nbn:se:kth:diva-320659DOI: 10.3390/ma15196540ISI: 000867957800001PubMedID: 36233894Scopus ID: 2-s2.0-85139979487OAI: oai:DiVA.org:kth-320659DiVA, id: diva2:1707603
Note

QC 20221101

Available from: 2022-11-01 Created: 2022-11-01 Last updated: 2024-07-04Bibliographically approved
In thesis
1. A Framework for Fatigue Analysis of Carbon Fiber Reinforced Polymer Structures
Open this publication in new window or tab >>A Framework for Fatigue Analysis of Carbon Fiber Reinforced Polymer Structures
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Abstract [sv]

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.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2023. p. 73
Series
TRITA-SCI-FOU ; 2023:56
Keywords
Heavy-duty vehicles, Fatigue, Carbon fiber reinforced polymer, Multi-scale modeling, Probabilistic modeling, Tunga fordon, Utmattning, Kolfiberkomposit, Probabilistisk modellering
National Category
Composite Science and Engineering Vehicle Engineering
Research subject
Vehicle and Maritime Engineering
Identifiers
urn:nbn:se:kth:diva-339380 (URN)978-91-8040-749-6 (ISBN)
Public defence
2023-12-12, F3, https://kth-se.zoom.us/j/65952081Pu244, Lindstedtsvägen 26, Stockholm, 10:00 (English)
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Note

QC 231108

Available from: 2023-11-08 Created: 2023-11-08 Last updated: 2024-03-04Bibliographically approved

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Eliasson, SaraHagnell, Mathilda KarlssonWennhage, PerBarsoum, Zuheir

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