Image-based numerical modelling of heterogeneous materials
Doktorsavhandling, 2023
In science there has always been a desire to visualise the invisible. Since the discovery of X-rays in 1895, imaging research has made remarkable progress. Nowadays, state-of-the-art technology allows to visualise the micro-structure of objects in three dimensions.
However, merely visualising the structure is often insufficient. The quantitative information regarding morphology and structure is of great interest. Therefore, in addition to significant advancements in X-ray image acquisition and three-dimensional reconstruction, image analysis has become an active research field in recent years. Modern image analysis methods enable to extract even invisible information from image data.
The heterogeneous micro-structure of composites imposes advanced material characterisation as even for the largest composite structures, such as wind turbine blades or airplane wings, the material properties are dictated on the micro-scale. Image-based modelling offers exceptional capabilities in analysing the micro-structure at the fibre level and numerically predicting material behaviour even at larger scales. However, image-based modelling is a complex process and all work-steps must be in line with the final modelling goal. Therefore, X-ray computed tomography aided engineering has been introduced to emphasise the importance of a holistic point of view on the image-based modelling process.
The developed X-ray computed tomography aided engineering methodology has been developed based on micro X-ray computed tomography scans for non-crimp fabric glass-fibre reinforced composites. It is demonstrated that local fibre orientations and fibre volume fractions can be accurately imaged and transferred onto a finite element model. Thereby, the tensile modulus of the scanned samples can be accurately predicted and possible stress concentration regions detected.
However, conventional micro X-ray computed tomography presents a major drawback. Achieving the required high resolutions to visualise carbon or glass fibres, typically ranging between 5 to 20 μm, limits the scanning field of view, which remains in the millimetre range. This drawback is overcome with new approaches in image-based modelling involving advances in imaging and image analysis. Therefore, targeted approaches for accurate image-based modelling are presented which increase the possible scanning field-of-view of fibrous composites by up to three to six orders of magnitude.
However, merely visualising the structure is often insufficient. The quantitative information regarding morphology and structure is of great interest. Therefore, in addition to significant advancements in X-ray image acquisition and three-dimensional reconstruction, image analysis has become an active research field in recent years. Modern image analysis methods enable to extract even invisible information from image data.
The heterogeneous micro-structure of composites imposes advanced material characterisation as even for the largest composite structures, such as wind turbine blades or airplane wings, the material properties are dictated on the micro-scale. Image-based modelling offers exceptional capabilities in analysing the micro-structure at the fibre level and numerically predicting material behaviour even at larger scales. However, image-based modelling is a complex process and all work-steps must be in line with the final modelling goal. Therefore, X-ray computed tomography aided engineering has been introduced to emphasise the importance of a holistic point of view on the image-based modelling process.
The developed X-ray computed tomography aided engineering methodology has been developed based on micro X-ray computed tomography scans for non-crimp fabric glass-fibre reinforced composites. It is demonstrated that local fibre orientations and fibre volume fractions can be accurately imaged and transferred onto a finite element model. Thereby, the tensile modulus of the scanned samples can be accurately predicted and possible stress concentration regions detected.
However, conventional micro X-ray computed tomography presents a major drawback. Achieving the required high resolutions to visualise carbon or glass fibres, typically ranging between 5 to 20 μm, limits the scanning field of view, which remains in the millimetre range. This drawback is overcome with new approaches in image-based modelling involving advances in imaging and image analysis. Therefore, targeted approaches for accurate image-based modelling are presented which increase the possible scanning field-of-view of fibrous composites by up to three to six orders of magnitude.
Composites
X-ray computed tomography
image analysis
image-based modelling
Kollektorn, Kemivägen 9, Göteborg
Opponent: Prof. Kristofer Gamstedt, Uppsala University, Sweden
Författare
Robert Auenhammer
Chalmers, Industri- och materialvetenskap, Material- och beräkningsmekanik
Automated X-ray computer tomography segmentation method for finite element analysis of non-crimp fabric reinforced composites
Composite Structures,;Vol. 256(2021)
Artikel i vetenskaplig tidskrift
X-ray tomography based numerical analysis of stress concentrations in non-crimp fabric reinforced composites - assessment of segmentation methods
IOP Conference Series: Materials Science and Engineering,;Vol. 942(2020)
Paper i proceeding
X-ray computed tomography data structure tensor orientation mapping for finite element models - STXAE
Software Impacts,;Vol. 11(2022)
Artikel i vetenskaplig tidskrift
Robust numerical analysis of fibrous composites from X-ray computed tomography image data enabling low resolutions
Composites Science and Technology,;Vol. 224(2022)
Artikel i vetenskaplig tidskrift
X-ray based material modelling
Most people in Europe have probably been X-rayed at least once in their life. Since the discovery of X-rays by Conrad Wilhelm Röntgen in 1895 it is the standard image-based medical diagnostic tool. However, X-ray imaging only provides two-dimensional images. Thanks to computed tomography (CT) and advancements in mathematics and computation, it is now possible to scan three-dimensional images.
These three-dimensional images are valuable not only for medical purposes, like examining bone fractures, but also for materials scientists. Understanding materials on nano and micrometre scales enables us to improve for example airplanes, wind turbines, and car batteries. This knowledge leads to longer-lasting, safer, and cheaper designs.
X-ray computed tomography image-based numerical modelling is an advanced scientific technique that combines X-ray imaging and numerical modelling. The process involves several steps:
1. X-ray computed tomography (CT) imaging:
- Detailed two-dimensional X-ray images are taken from various angles, often several hundreds or thousands of pictures. A computer is required then to combine them into a three-dimensional image. The three-dimensional image can be virtually sliced through, and the internal structure of the object becomes visible.
2. Image-based numerical modelling:
- In the three-dimensional image, features or objects can be identified, manually or automatically. We can then run virtual experiments on the computer, exploring how the object behaves under different conditions without touching or destroying the real part.
This powerful combination of X-ray imaging and numerical modelling has revolutionised multiple fields, from medicine to engineering and materials science, providing useful insights and new possibilities to design innovative materials. It is like looking inside things without breaking them and using computers to discover how they work in the real world.
Most people in Europe have probably been X-rayed at least once in their life. Since the discovery of X-rays by Conrad Wilhelm Röntgen in 1895 it is the standard image-based medical diagnostic tool. However, X-ray imaging only provides two-dimensional images. Thanks to computed tomography (CT) and advancements in mathematics and computation, it is now possible to scan three-dimensional images.
These three-dimensional images are valuable not only for medical purposes, like examining bone fractures, but also for materials scientists. Understanding materials on nano and micrometre scales enables us to improve for example airplanes, wind turbines, and car batteries. This knowledge leads to longer-lasting, safer, and cheaper designs.
X-ray computed tomography image-based numerical modelling is an advanced scientific technique that combines X-ray imaging and numerical modelling. The process involves several steps:
1. X-ray computed tomography (CT) imaging:
- Detailed two-dimensional X-ray images are taken from various angles, often several hundreds or thousands of pictures. A computer is required then to combine them into a three-dimensional image. The three-dimensional image can be virtually sliced through, and the internal structure of the object becomes visible.
2. Image-based numerical modelling:
- In the three-dimensional image, features or objects can be identified, manually or automatically. We can then run virtual experiments on the computer, exploring how the object behaves under different conditions without touching or destroying the real part.
This powerful combination of X-ray imaging and numerical modelling has revolutionised multiple fields, from medicine to engineering and materials science, providing useful insights and new possibilities to design innovative materials. It is like looking inside things without breaking them and using computers to discover how they work in the real world.
MUltiscale, Multimodal and Multidimensional imaging for EngineeRING (MUMMERING).
Europeiska kommissionen (EU) (EC/H2020/765604), 2019-01-01 -- 2021-12-31.
UTMOST - Modellering av biokompositer i krockanalys
VINNOVA (2021-05062), 2022-05-02 -- 2024-12-31.
Ämneskategorier
Acceleratorfysik och instrumentering
Teknisk mekanik
Kompositmaterial och -teknik
Styrkeområden
Materialvetenskap
ISBN
978-91-7905-907-1
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5373
Utgivare
Chalmers
Kollektorn, Kemivägen 9, Göteborg
Opponent: Prof. Kristofer Gamstedt, Uppsala University, Sweden