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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.

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.

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.

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.

Adhesive bonding has many applications in the automotive industry. The single-lapped bonded joint is the most typically used among various bonding types. This paper presents experimental research for determining the strain field of the single-lapped joint under tensile loading. The materials for the joint are epoxy-based structural adhesive and low-carbon electrolytic zinc steel plate. In the study, a DIC (digital image correlation) system was adopted to measure the strain distribution of the bonded joint during a tensile test. The bonded steel coupons in the tensile test were prepared according to the ASTM standard. During the measurement, images of the coupon joint were taken before and after the deformation process. Then the DIC system measured the strain of bonded joint by comparing two consecutive images. The measured data from the DIC was compared to data taken simultaneously from a traditional extensometer.

Styrene-Butadiene Rubber (SBR), a copolymer of butadiene and styrene, is widely used in the automotive industry due to its high durability and resistance to abrasion, oils and oxidation. Some of the common applications include tires, vibration isolators, and gaskets, among others. This paper characterizes the dynamic behavior of SBR and discusses the suitability of a visco-elastic model of elastomers, known as the Kelvin model, from a mathematical and physical point of view. An optimization algorithm is used to estimate the parameters of the Kelvin model. The resulting model was shown to produce reasonable approximations of measured dynamic stiffness. The model was also used to calculate the self heating of the elastomer due to energy dissipation by the viscous damping components in the model. Developing such a predictive capability is essential in understanding the dynamic behavior of elastomers considering that their dynamic stiffness can in general depend on temperature.

This paper presents a novel technique allowing time-dependent displacement of an object to be studied by continuously digitizing the speckle images using a high speed image acquisition system. Instead of generating fringe patterns, the displacement versus time for any point of interest can be studied. Therefore, the technique is equivalent to “many” massless and noncontact displacement sensors, which is particularly useful for vibration measurement.

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.

This paper presents an optical method for evaluating residual stresses. The approach is based on measuring the deformation due to the relief of stress produced by a blind-hole drilling technique. The deformation is rapidly measured by shearography. Unlike the strain gage method, this method does not require mounting strain gages /transducers. A rapid process for detection of residual stress using a micro-indentation technique is also present. The method is practical for employment in a production/field environment.

An optical method for measuring surface deformation of automotive panels is presented. The technique, which is based a 3-D computer vision technique previously reported by the authors, is full-field, noncontact, simple, fast and can be automated. Theory of the technique together with experimental results are presented in the paper.

Shearography is an optical technique developed for full-field measurement of surface deformation. It has since been accepted by industry as a practical nondestructive testing technique for evaluating the structural integrity of components and structures. Qualitatively, shearography reveals flaws from flaw-induced, anomalous deformation in the component under inspection, and quantitatively, shearography assesses the detected flaws through back-calculation from the anomalous deformation. This paper demonstrates that, with the use of multiple-frequency acoustical excitation together with the time-integrated shearographic recording technique, rapid and automated assessment of the integrity of adhesive-bonded composite structures can be realized in the actual plant environment.

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.

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).