Sam Yang

Microstructure of a Paint Primer - a Data-Constrained Modeling Analysis

Metallic aerospace components are commonly painted with a primer to improve their corrosion resistance. The primer contains a polymer matrix with embedded corrosion inhibitor and filler particles. Its performance is determined by the microscopic distributions of the particles. Various techniques have been used to quantify such distributions, including X-ray micro-computed tomography (CT). However, its success is sometimes limited by factors such as different particles having similar X-ray CT absorption properties and their size being smaller than the resolution of micro-CT. In this paper, we have performed two X-ray CT measurements on a paint primer sample consisting of SrCrO4 corrosion inhibitor particles and UV-absorbing TiO2 filler particles. Fe and Ti targets were used as X-ray sources with different spectral distributions. The measured CT data sets were used as constraints for a data-constrained microstructure modeling (DCM) prediction of the sample’s microscopic structures. DCM model predictions were compared with experimental elemental surface maps and showed reasonable degree of agreement, suggesting X-ray micro-CT combined with DCM modeling would be a powerful technique for detailing the dynamics of chromate-inhibited primers and other multiphase systems where the components are sensitive to incident X-ray energy.

Data-Constrained Microstructure Characterization with Multispectrum X-Ray Micro-CT

Conventional X-ray microcomputed tomography (micro-CT) is not usually sufficient to determine microscopic compositional distributions as it is limited to measuring the X-ray attenuation of the sample, which for a given dataset can be similar for materials of different composition. In contrast, the present work enables three-dimensional compositional analysis with a data-constrained microstructure (DCM) modeling methodology, which uses two or more CT datasets acquired with different X-ray spectra and incorporates them as model constraints. For providing input data for DCM, we have also developed a method of micro-CT data collection that enables two datasets with different X-ray spectra to be acquired in parallel. Such data are used together with the DCM methodology to predict the distributions of corrosion inhibitor and filler in a polymer matrix. The DCM-predicted compositional microstructures have a reasonable agreement with energy dispersive X-ray images taken on the sample surface.

A Data-Constrained 3D Model for Material Compositional Microstructures

A mathematical model has been developed for predicting material compositional microstructures using measured data as constraints. Examples of measured data include 3-D sets of tomography data, 2-D sets of compositional data on surfaces and sections, and material absorption and interaction properties. The model has been partially implemented as a MS-Windows application. Reasonable agreement has been obtained between the numerical predictions from the software and the simulated data. The predicted microstructures could be used to study various material properties such as porosity distribution, diffusion and corrosion.

SENSOR-BASED WATER PARCEL TRACKING

The increased use of on-line sensors means that a number of physical and chemical parameters are often measured routinely in drinking water distribution systems. These include temperature, pH, turbidity, free and total chlorine, oxygen reduction potential (ORP), electrical conductivity (EC), and fluoride. Laboratory trials showed that real-time monitoring of such parameters following addition of suitable tracers could be used to measure the travel time of water in pipes. Inert tracers gave good estimates of travel time, while reactive tracers such as temperature pulses interacted with the pipe and were delayed. Laminar flow caused increased broadening of tracer pulses, so that turbulent flow was preferred. Inspection of on-line monitoring data also showed that these parameters fluctuate naturally with time giving the water flow a characteristic fingerprint without the need to add a tracer. Field trials showed that these fingerprints were preserved for significant periods and could be successfully detected in a local distribution system after transit times of more than one hour. Visual matching of output peaks was used to measure transit time, and some success has also been achieved using an automatic correlation routine to track water parcel movement. The fingerprint tracking technique is a unique way of following water movement without using added tracer, and it does not require detailed knowledge of network connectivity that is necessary when using a hydraulic modeling approach. This technique has considerable potential in water quality and water security applications, and should enable real-time measurements of water age and chemistry in distribution systems.

Characterization of Darai Limestone Composition and Porosity Using Data-Constrained Modeling and Comparison with Xenon K-Edge Subtraction Imaging.

Data-constrained modeling is a method that enables three-dimensional distribution of mineral phases and porosity in a sample to be modeled based on micro-computed tomography scans acquired at different X-ray energies. Here we describe an alternative method for measuring porosity, synchrotron K-edge subtraction using xenon gas as a contrast agent. Results from both methods applied to the same Darai limestone sample are compared. Reasonable agreement between the two methods and with other porosity measurements is obtained. The possibility of a combination of data-constrained modeling and K-edge subtraction methods for more accurate sample characterization is discussed.

A synchrotron-based local computed tomography combined with data-constrained modelling approach for quantitative analysis of anthracite coal microstructure

A quantitative local computed tomography combined with data-constrained modelling has been developed. The method could improve distinctly the spatial resolution and the composition resolution in a sample larger than the field of view, for quantitative characterization of three-dimensional distributions of material compositions and void.

An Expert System for Plasma Cutting Process Quality Prediction and Optimal Parameter Suggestion

An hybrid expert system, which uses both rule-based reasoning and statistical modelling, is developed for prediction of cut quality and suggestion of optimal process parameter settings for CNC controlled plasma metal-plate cutting machines. A scheme for process parameter classification has been developed. They are classified according to whether they are fixed, operator selectable or adjustable. A scheme for cutting quality definition and practical method of assessment have been developed which incorporate both the quantitative requirements for each quality attribute and their respective relative importance. When process parameter values are entered, the system gives the expected quality outcome. When the quality requirements are specified, the system outputs the optimal parameter setting values. The prototype of the system has been tested and the predictions are consistent with experimental measurements.

Physico-Chemical Characterisation of Protective Coatings and Self Healing Processes

The assessment of repair or self healing for advanced materials and novel coating systems can be performed using electrochemical methods or physicochemical methods. In this chapter the authors review the literature both inside and outside the field of self healing materials on a range of characterisation techniques that can be used for determining the level of self healing. Techniques include direct measurement of healing, vibrational, electron and optical spectroscopies, particle techniques and tomographic techniques. Limitations and pitfalls in the use of these techniques for determining the level of healing are also discussed.

STABILITY PREDICTION OF TITANIUM MILLING WITH DATA DRIVEN RECONSTRUCTION OF PHASE-SPACE

Significant research effort has been carried out in the detection of chatter, which is one of the main barriers against titanium milling. State-of-the-art techniques are unable to satisfy requirements of industry in terms of in-process chatter detection. The present study reports the use of sensor-signal driven reconstructed phase space attractors combined with image correlation as a solution of chatter prediction during milling of titanium in industry. The method uses Poincaré sections of reconstructed phase space attractor as patterns to identify the onset of chatter in the apparently random behavior of vibrations in the milling process. Image correlation of Poincaré sections indicates the onset of chatter in the milling process.

Application of linear regression model on chatter threshold delineation

This research is to parameterize the variation in reconstructed phase space attractor Poincaré sections during the milling process in order to identify the transition of system from stable to unstable condition. Vibration is continuously acquired during the milling process and converted to the form of reconstructed time series. A regression model is developed to compute the trend of changes in the reconstructed phase space attractor Poincaré sections and output a numerical value indicating the level of stability. A threshold value of the trend value defines the boundary of stable and unstable states of milling process. The method monitors the state of system without the necessity to have analytical relationship between cutting parameters and milling dynamics. It can also be used as an online monitoring process to detect the onset of chatter.