GD&T-Based Characterization of Short-Range Non-contact 3D Imaging Systems

We present a series of test metrics, artifacts, and procedures for characterizing and verifying the operating limits of a short-range non-contact three-dimensional imaging system. These metrics have been designed to correspond to dimensioning and tolerancing metrics that are widely used in industry (e.g. automotive, aerospace, etc.). We introduce operating limit metrics that correspond with the geometric dimensioning and tolerancing (GD&T) metrics of Form (Flatness and Circularity), Orientation (Angularity), Location (Sphere, Corner, and Hole Position Errors), and Size (Diameter, Sphere-spacing, Plane-spacing and Angle Errors). An example is presented to illustrate how these metrics, artifacts, and associated test procedures can be used in practice.     

Bayesian assessment of the characteristic concrete compressive strength using combined vague-informative priors

Bayesian statistics can be used in order to determine the characteristic value of an unknown distribution based on a limited number of test samples. In cases where no previous test results are available, most often a Bayesian method based on vague prior information is used. The assumption of a vague or uniform prior results in a conservative approach in cases where only a limited number of test results are available. However, in case of concrete, prior information on concrete strength can be found in literature or country-specific prior information can be determined. Therefore, the use of a combined vague-informative prior is of particular interest, more specifically in the form of scaled inverse-χ² distributions that can be used for updating the standard deviation of the strength distribution of concrete. The differences between the use of a vague and a combined vague-informative prior are investigated through Monte Carlo simulations. Because prior information is taken into account, the uncertainty regarding the standard deviation of the predictive strength distribution is significantly reduced.     

Selecting creep models using Bayesian methods

The development of creep prediction models has been a field of extensive research and many different models have already been proposed. This paper presents an evaluation method of the prediction quality of creep models for specific experimental data. Within the scope of this paper, the model according to Bockhold and the model according to Heidolf are examined. First, the parameters of the models are identified with respect to existing experimental data. This is done using a sampling based approach of Bayesian updating developed by Ba?ant and Chern. In extension to the method by Ba?ant and Chern, the uncertainty coming from inaccurate measurement data is taken into account in the definition of the likelihood function within the updating algorithm. The more inaccurate the measurements are, the more uncertain the estimated model parameters and model prognoses become. The identification is performed for different short- and long-term creep tests. The intension is not to validate these models intensively, but to evaluate their prognoses for the individually tested creep behavior. The results show that the identifiability of the models?? parameters is different for both models and consequently the models prognoses differ in their uncertainties. Second, the models are evaluated using two different strategies: the stochastic model selection according to MacKay, Beck and Yuen based on the Ockham factor, and a comparison of the uncertainties taking into account parameter and model uncertainties. The results of the evaluation of the creep models differ for various experimental tests. Model Heidolf is more flexible and gives a better fit to the data, however, it fails to predict reliable long-term creep deformations using only short-term measurements compared to model Bockhold. Comparing the evaluation methods, the analysis of uncertainties of the creep prognosis proofs to be more stable than the evaluation using the stochastic model selection.     

Effect of Thermomechanical Fatigue on Precipitation Microstructure in Two Precipitation-Hardened Cast Aluminum Alloys

Thermal loading induces modifications of the precipitation microstructure of Al–Si–Cu–Mg alloys. This study focuses on the effect of deformation on precipitation microstructure during thermomechanical loadings. Several specimens were thermomechanically cycled while others were exposed to the same thermal cycles without any mechanical loading. The nature and morphological characteristics of the precipitation microstructure of the thermomechanically cycled specimens are compared to those of the thermally aged ones, using transmission electron microscopy (TEM), in order to assess the effect of deformation on the precipitation microstructure and especially on the kinetics of precipitate growth. The absence of any significant effect of superimposed straining during thermal cycling is discussed. Implications for the prevision of yield strength degradation during service operation are briefly presented.

     

Multi-level Modeling of Silica–Template Interactions During Initial Stages of Zeolite Synthesis

Zeolite synthesis is driven by structure-directing agents, such as tetrapropyl ammonium ions (TPA⁺) for Silicalite-1 and ZSM-5. However, the guiding role of these organic templates in the complex assembly to highly ordered frameworks remains unclear, limiting the prospects for advanced material synthesis. In this work, both static ab initio and dynamic classical modeling techniques are employed to provide insight into the interactions between TPA⁺ and Silicalite-1 precursors. We find that as soon as the typical straight 10-ring channel of Silicalite-1 or ZSM-5 is formed from smaller oligomers, the TPA⁺ template is partially squeezed out of the resulting cavity. Partial retention of the template in the cavity is, however, indispensable to prevent collapse of the channel and subsequent hydrolysis.

Introduction to the special issue on IEEE NEWCAS 2017

A contactless detector is presented for evaluating hand tremors caused by exercise-induced fatigue and early Parkinson’s disease. The device consists of a spiral coil, a microcontroller, and an inductive sensor circuitry. Theory shows that the resonant frequency of the circuitry increases when the distance between the hand and the spiral coil decreases, thus small variations of distance from tremor can be detected from the changes of resonant frequencies. A mechanical hand was built for experiments to simulate human hand tremors with repeatability at a fixed frequency. The magnitudes and frequencies of the tremors in the mechanical hand were quantitatively identified using the inductive sensor. Hence, feasibility and accuracy of the contactless hand tremor detector were determined. A triaxial accelerometer was used for comparison. By comparing spectral distributions and magnitudes of the tremors, the inductive sensor performed better than the accelerometer. The detector was applied to evaluate actual hand tremors of three subjects who had undergone exercise to induce tremors. The tremor waveform amplitudes of the subjects were quantitatively analyzed by the standard deviations method. The increased signal energies of exercise-induced tremor within 8–12 Hz were confirmed. Then, a subject with early Parkinson’s disease was evaluated by the proposed hand tremor detector. The tremor magnitudes and frequencies of the patient hand were quantitatively identified within in 4–7 Hz. Therefore, the new contactless hand tremor detector can be developed as a clinical instrument for monitoring the fatigue symptoms of post-exercise and diagnosing the early Parkinson’s disease.     

Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks

In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for image classification. We pose the age estimation problem as a deep classification problem followed by a softmax expected value refinement. The key factors of our solution are: deep learned models from large data, robust face alignment, and expected value formulation for age regression. We validate our methods on standard benchmarks and achieve state-of-the-art results for both real and apparent age estimation.