Austenite Nucleation and Growth Observed on the Level of Individual Grains by Three-Dimensional X-Ray Diffraction Microscopy

Austenite nucleation and growth is studied during continuous heating using three-dimensional X-ray diffraction (3-D XRD) microscopy at the European Synchrotron Radiation Facility (ESRF) (Grenoble, France). Unique in-situ observations of austenite nucleation and growth kinetics were made for two commercial medium-carbon low-alloy steels (0.21 and 0.35 wt pct carbon with an initial microstructure of ferrite and pearlite). The measured austenite volume fraction as a function of temperature shows a two-step behavior for both steel grades: it starts with a rather fast pearlite-to-austenite transformation, which is followed by a more gradual ferrite-to-austenite transformation. The austenite nucleus density exhibits similar behavior, with a sharp increase during the first stage of the transformation and a more gradual increase in the nucleus density in the second stage for the 0.21 wt pct carbon alloy. For the 0.35 wt pct carbon alloy, no new nuclei form during the second stage. Three different types of growth of austenite grains in the ferrite/pearlite matrix were observed. The combination of detailed separate observations of both nucleation and growth provides unique quantitative information on the phase transformation kinetics during heating, i.e., austenite formation from ferrite and pearlite.     

Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks

This paper describes the development of a neural network (NN) based adaptive flight control system for a high performance aircraft. The main contribution of this work is that the proposed control system is able to compensate the system uncertainties, adapt to the changes in flight conditions, and accommodate the system failures. The underlying study can be considered in two phases. The objective of the first phase is to model the dynamic behavior of a nonlinear F-16 model using NNs. Therefore a NN-based adaptive identification model is developed for three angular rates of the aircraft. An on-line training procedure is developed to adapt the changes in the system dynamics and improve the identification accuracy. In this procedure, a first-in first-out stack is used to store a certain history of the input-output data. The training is performed over the whole data in the stack at every stage. To speed up the convergence rate and enhance the accuracy for achieving the on-line learning, the Levenberg-Marquardt optimization method with a trust region approach is adapted to train the NNs. The objective of the second phase is to develop intelligent flight controllers. A NN-based adaptive PID control scheme that is composed of an emulator NN, an estimator NN, and a discrete time PID controller is developed. The emulator NN is used to calculate the system Jacobian required to train the estimator NN. The estimator NN, which is trained on-line by propagating the output error through the emulator, is used to adjust the PID gains. The NN-based adaptive PID control system is applied to control three angular rates of the nonlinear F-16 model. The body-axis pitch, roll, and yaw rates are fed back via the PID controllers to the elevator, aileron, and rudder actuators, respectively. The resulting control system has learning, adaptation, and fault-tolerant abilities. It avoids the storage and interpolation requirements for the too many controller parameters of a typical flight control system. Performance of the control system is successfully tested by performing several six-degrees-of-freedom nonlinear simulations.     

Discrete state space modeling and control of nonlinear unknown systems

A novel procedure for integrating neural networks (NNs) with conventional techniques is proposed to design industrial modeling and control systems for nonlinear unknown systems. In the proposed approach, a new recurrent NN with a special architecture is constructed to obtain discrete-time state-space representations of nonlinear dynamical systems. It is referred as the discrete state-space neural network (DSSNN). In the DSSNN, the outputs of the hidden layer neurons of the DSSNN represent the system's (pseudo) state. The inputs are fed to output neurons and the delayed outputs of the hidden layer neurons are fed to their inputs via adjustable weights. The discrete state space model of the actual system is directly obtained by training the DSSNN with the input–output data. A training procedure based on the back-propagation through time (BPTT) algorithm is developed. The Levenberg–Marquardt (LM) method with a trust region approach is used to update the DSSNN weights. Linear state space models enable to use well developed conventional analysis and design techniques. Thus, building a linear model of a system has primary importance in industrial applications. Thus, a suitable linearization procedure is proposed to derive the linear state space model from the nonlinear DSSNN representation. The controllability, observability and stability properties are examined. The state feedback controllers are designed with both the linear quadratic regulator (LQR) and the pole placement techniques. The regulator and servo control problems are both addressed. A full order observer is also designed to estimate the state variables. The performance of the proposed procedure is demonstrated by applying for both single-input single-output (SISO) and multiple-input multiple-output (MIMO) nonlinear control problems.     

Regression-based intensity estimation of facial action units

Facial Action Coding System (FACS) is the de facto standard in the analysis of facial expressions. FACS describes expressions in terms of the configuration and strength of atomic units called Action Units: AUs. FACS defines 44 AUs and each AU intensity is defined on a nonlinear scale of five grades. There has been significant progress in the literature on the detection of AUs. However, the companion problem of estimating the AU strengths has not been much investigated. In this work we propose a novel AU intensity estimation scheme applied to 2D luminance and/or 3D surface geometry images. Our scheme is based on regression of selected image features. These features are either non-specific, that is, those inherited from the AU detection algorithm, or are specific in that they are selected for the sole purpose of intensity estimation. For thoroughness, various types of local 3D shape indicators have been considered, such as mean curvature, Gaussian curvature, shape index and curvedness, as well as their fusion. The feature selection from the initial plethora of Gabor moments is instrumented via a regression that optimizes the AU intensity predictions. Our AU intensity estimator is person-independent and when tested on 25 AUs that appear singly or in various combinations, it performs significantly better than the state-of-the-art method which is based on the margins of SVMs designed for AU detection. When evaluated comparatively, one can see that the 2D and 3D modalities have relative merits per upper face and lower face AUs, respectively, and that there is an overall improvement if 2D and 3D intensity estimations are used in fusion.     

Effects of alloy composition and casting speed on structure formation and hot tearing during direct-chill casting of Al-Cu alloys

Effects of casting speed and alloy composition on structure formation and hot tearing during direct-chill (DC) casting of 200-mm round billets from binary Al-Cu alloys are studied. It is experimentally shown that the grain structure, including the occurrence of coarse grains in the central part of the billet, is strongly affected by the casting speed and alloy composition, while the dendritic arm spacing is mostly dependent on the casting speed. The hot cracking pattern reveals the maximum hot-tearing susceptibility in the range of low-copper alloys (1 to 1.5 pct) and at high casting speeds (180 to 200 mm/min). The clear correlation between the amount of nonequilibrium eutectics (representing the reserve of liquid phase in the last stage of solidification) and hot tearing is demonstrated. A casting speed-copper concentration-hot-tearing susceptibility chart is constructed experimentally for real-scale DC casting. Computed dimensions of the solidification region in the billet are used to explain the experimentally observed structure patterns and hot cracking. Thermomechanical finite-element simulation of the solidifying billet was used as a tool for testing the applicability to DC casting of several hot-tearing criteria based on different principles. The results are compared to the experimentally observed hot tearing. It is noted that hot-tearing criteria that account for the dynamics of the process, e.g., strain rate, actual stress-strain situation, feeding rate, and melt flow, can be successfully used for the qualitative prediction of hot tearing.     

Detection of folate binding protein with enhanced sensitivity using a functionalized quartz crystal microbalance sensor

In this report, we describe the development of a quartz crystal microbalance biosensor for detection of folate binding protein (FBP). Using a simple folate-BSA conjugate adsorbed onto a Au-coated quartz sensor, a detection limit of 30 nM was achieved. Binding of FBP to the sensor surface could be blocked at concentrations as high as 1 microM with a 100-fold excess of folic acid, indicating the specificity of the folate-FBP interaction and the absence of nonspecific binding to the functionalized surface. Moreover, capture could be achieved in the presence of blood serum, making the assay amenable to the analysis of bodily fluids. Further signal enhancement based on an anti-FBP antibody and protein-A-coated gold nanosphere sandwich assay extended the detection limit to 50 pM (approximately 3 orders-of-magnitude improvement). Given the overexpression of FBP in certain malignancies and inflammatory disorders, we expect the methodology described here to be useful to detect FBP as a possible biomarker for disease diagnosis.