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During their life cycle, composite structures used in aircraft structures can be subjected to high- and low-velocity impact loading. High velocity impact damage is usually easy to detect as it creates visible external damage. Low-velocity impacts are more complex to assess because, although significant damage can be generated internally, there can be little indication of external damage on the impacted surface, leading to the term BVID. Impact damage, especially barely visible impact damage, is therefore of primary concern for design and maintenance of modern aircraft composite structures. As a result there is a concerted research effort to improve the damage resistance and tolerance of these materials.
One of the great strengths of X-ray computed tomography over conventional inspection methods (ultrasound, thermography, radiography) is that it can image damage in 3D. or inter-ply interfaces. An X-ray computed tomography (CT) data processing methodology is developed to extract the through-thickness distribution of damage in curved or deformed composite panels. The method is applied to [(0°/90°)2]s carbon fibre reinforced polymer (CFRP) panels subjected to low velocity impact damage (5 J up to 20 J) providing 3D ply-by-ply damage visualisation and analysis. Our distance transform approach allows slices to be taken that approximately follow the composite curvature allowing the impact damage to be separated, visualised and quantified in 3D on a ply-by-ply basis. In this way the interply delaminations have been mapped, showing characteristic peanut shaped delaminations with the major axis oriented with the fibres in the ply below the interface. This registry to the profile of the panel constitutes a significant improvement in our ability to characterise impact damage in composite laminates and extract relevant measurements from X-ray CT datasets.
One of the great strengths of X-ray computed tomography over conventional inspection methods (ultrasound, thermography, radiography) is that it can image damage in 3D. However for curved or deformed composite panels, it can be difficult to automatically ascribe the damage to specific plies or inter-ply interfaces. An X-ray computed tomography (CT) data processing methodology is developed to extract the through-thickness distribution of damage in curved or deformed composite panels. The method is applied to [(0°/90°)2]s carbon fibre reinforced polymer (CFRP) panels subjected low velocity impact damage (5 J up to 20 J) providing 3D ply-by-ply damage visualisation and analysis. Our distance transform approach allows slices to be taken that approximately follow the composite curvature allowing the impact damage to be separated, visualised and quantified in 3D on a ply-by-ply basis. In this way the interply delaminations have been mapped, showing characteristic peanut shaped delaminations with the major axis oriented with the fibres in the ply below the interface. This registry to the profile of the panel constitutes a significant improvement in our ability to characterise impact damage in composite laminates and extract relevant measurements from X-ray CT datasets.
Improved Data Processing for Accurate Plasma Diagnostics with Implications for Calibration-Free LIBS
(2023)
Many LIBS papers report the determination of plasma temperature using the Boltzmann plot method or the determination of electron density using the Stark line broadening relation. This requires measuring the integrated intensities of the spectral lines and the linewidth under the assumption of optical thinness. It is taken for granted that this can be easily done either by working with the raw spectra or by fitting the appropriate function to the observed spectral lines. However, extracting the necessary information from raw spectral data is not as easy as it might seem. The quality of such extraction will depend to a decisive extent on the type of spectral instrument used. The spectrum emitted by the plasma is distorted by the device; an example is shown in Fig. 1. The elimination of this distortion belongs to the class of inverse problems, the successful solution of which fundamentally depends on the quality of the available information. When it comes to spectroscopy, the quality of information primarily means high spectral resolution and low noise. Not all spectrometers used in LIBS can provide the quality needed to solve the inverse problem; this casts doubt on many published plasma measurements. This communication will be devoted to the general shortcomings of spectral data processing and the inaccuracies in determining the plasma parameters resulting from these shortcomings. The analysis is based on the use of synthetic spectra generated by plasma with known temperature, particle density and electron density. The estimation of errors caused by inadequate processing of spectral data is made by comparing the initial and measured plasma parameters from the spectra. Recommendations will be made for which analytic function best approximates the observed spectral lines, and how data processing errors affect the accuracy of calibration-free LIBS will be discussed. These issues were only partially covered in previously published works, for example [1, 2].
In the LIBS literature, almost every second article reports the determination of the plasma temperature using the Boltzmann plot method or the determination of the electron density using the Stark line broadening relation. The first requires the measurement of the integrated intensities of the spectral lines, and the second requires the measurement of the linewidth, under the same assumption of optical thinness. It is taken for granted that this can be easily done either by working with the raw spectra or by fitting an appropriate function to the observed spectral lines. As a rule, reported data are not verified either by an alternative method (e.g., Thomson scattering) or by computer simulations using synthetic spectra.
However, the question of how to extract the necessary information from the raw spectral data is not as simple as it might seem. The quality of such an extraction will depend critically on the type of spectral instrument used, its resolution, and the noise superimposed on the data. The problem is that we do not see the spectrum emitted by the plasma, but the spectrum distorted by the measurement; an exaggerated example of such a distortion is shown in Fig. 1. The elimination of this distortion belongs to the class of inverse problems, the so-called ill-posed problems, whose successful solution crucially depends on the quality of the information available. When it comes to spectroscopy, quality of information primarily means high spectral resolution and low noise. Not all spectrometers used in LIBS can provide the quality needed to solve the inverse problem; this casts doubt on many published plasma measurements.
The current presentation will be devoted to general shortcomings in the processing of spectral data and inaccuracies in the determination of plasma parameters resulting from these shortcomings. The analysis is based on the use of synthetic spectra produced by plasma with known characteristics, i.e., temperature, species densities, and electron density. The estimation of errors caused by inadequate processing of spectral data is made by comparing the initial and reconstructed plasma parameters. Recipes will be given for which the analytic function best approximates the observed spectral lines, and how data processing errors affect accuracy of calibration-free LIBS will be discussed. These issues were only partially covered in previously published works, for example [1, 2, 3].
The second talk for the Swiss Society for Crystallography (SSCr) workshop on SAXS will highlight the data processing challenges, holistic experimental workflow developments, and the pitfalls. In particular, the following items will be addressed:
- The importance of data processing and estimating uncertainty
- A universal correction pipeline – away with the headaches, at least for this step!
- Experiment planning part 2, some tips and advice to improve your corrected data.
- Sample preparation, background selection, some tips and advice to improve your corrected data.
- Automate for your mental well-being; electronic logbooks, measurement catalogs and workflow management software
- Life on the edge: several pitfalls to avoid…