Refine Your Search

Search Results

Viewing 1 to 7 of 7
Technical Paper

Precision Measurement of Deformation Using a Self-calibrated Digital Speckle Pattern Interferometry (DSPI)

A self-calibrating phase-shifting technique using a Michelson Interferometer is presented to measure phase distribution more accurately in Digital Speckle Pattern Interferometry (DSPI). DSPI is a well-established technique for the determination of whole field deformation via quantitatively measuring the phase distribution of speckle interferograms that use the phase shifting technique. In the phase shifting technique, the phase distribution in a speckle interferogram is quantitatively determined by recording multiple intensity images (usually four images) in which a constant phase shift, e.g. 90 degrees, is introduced between each consecutive image. A precise phase determination is greatly dependent on the accuracy of the phase shift introduced. The popular methods to minimize the error resulting from inaccurate phase shift use various algorithms and need to record five or eight images (rather than four images).
Technical Paper

Modeling Dependence and Assessing the Effect of Uncertainty in Dependence in Probabilistic Analysis and Decision Under Uncertainty

A complete probabilistic model of uncertainty in probabilistic analysis and design problems is the joint probability distribution of the random variables. Often, it is impractical to estimate this joint probability distribution because the mechanism of the dependence of the variables is not completely understood. This paper proposes modeling dependence by using copulas and demonstrates their representational power. It also compares this representation with a Monte-Carlo simulation using dispersive sampling.
Journal Article

A Nonparametric Bootstrap Approach to Variable-size Local-domain Design Optimization and Computer Model Validation

Design optimization often relies on computational models, which are subjected to a validation process to ensure their accuracy. Because validation of computer models in the entire design space can be costly, a recent approach was proposed where design optimization and model validation were concurrently performed using a sequential approach with both fixed and variable-size local domains. The variable-size approach used parametric distributions such as Gaussian to quantify the variability in test data and model predictions, and a maximum likelihood estimation to calibrate the prediction model. Also, a parametric bootstrap method was used to size each local domain. In this article, we generalize the variable-size approach, by not assuming any distribution such as Gaussian. A nonparametric bootstrap methodology is instead used to size the local domains. We expect its generality to be useful in applications where distributional assumptions are difficult to verify, or not met at all.
Technical Paper

Decision-Based Universal Design - Using Copulas to Model Disability

This paper develops a design paradigm for universal products. Universal design is term used for designing products and systems that are equally accessible to and usable by people with and without disabilities. Two common challenges for research in this area are that (1) There is a continuum of disabilities making it hard to optimize product features, and (2) There is no effective benchmark for evaluating such products. To exacerbate these issues, data regarding customer disabilities and their preferences is hard to come by. We propose a copula-based approach for modeling market coverage of a portfolio of universal products. The multiattribute preference of customers to purchase a product is modeled as Frank's Archimedean Copula. The inputs from various disparate sources can be collected and incorporated into a decision system.
Journal Article

Warranty Forecasting of Repairable Systems for Different Production Patterns

Warranty forecasting of repairable systems is very important for manufacturers of mass produced systems. It is desired to predict the Expected Number of Failures (ENF) after a censoring time using collected failure data before the censoring time. Moreover, systems may be produced with a defective component resulting in extensive warranty costs even after the defective component is detected and replaced with a new design. In this paper, we present a forecasting method to predict the ENF of a repairable system using observed data which is used to calibrate a Generalized Renewal Processes (GRP) model. Manufacturing of products may exhibit different production patterns with different failure statistics through time. For example, vehicles produced in different months may have different failure intensities because of supply chain differences or different skills of production workers, for example.
Technical Paper

Rapid Evaluation of Hermetic Seals in Automotive Microelectronic Packages Using Shearography

As the use of electronic devices in automobiles increases, the reliability of such devices is becoming increasingly important. One possible failure is due to leakage resulted from imperfect hermetical seal in mircochips and microelectronic packages. This paper presents an optical technique referred to as shearography for rapid evaluation of hermetics seals. The proposed process of leaking testing is very fast and practical.
Journal Article

A New Metamodeling Approach for Time-Dependent Reliability of Dynamic Systems with Random Parameters Excited by Input Random Processes

We propose a new metamodeling method to characterize the output (response) random process of a dynamic system with random parameters, excited by input random processes. The metamodel can be then used to efficiently estimate the time-dependent reliability of a dynamic system using analytical or simulation-based methods. The metamodel is constructed by decomposing the input random processes using principal components or wavelets and then using a few simulations to estimate the distributions of the decomposition coefficients. A similar decomposition is also performed on the output random process. A kriging model is then established between the input and output decomposition coefficients and subsequently used to quantify the output random process corresponding to a realization of the input random parameters and random processes. What distinguishes our approach from others in metamodeling is that the system input is not deterministic but random.