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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.

Experimental results on influence of trimming conditions on the shape of the sheared surface are combined with the results of stretching sheared samples after trimming. The objective of the research described in this paper is to study the mechanism of fracture initiation and cracks propagation during half-a-dog bone tensile test representing sheared edge stretching condition. One side of the sample had sheared surface obtained by the trimming process while the other side of the sample had a smooth surface. Significant attention was paid to understanding of fracture sources. An interrupted tensile test approach was employed to track fracture initiation and propagation during stretching of sheared surface. The results of the experimental study indicated that multiple sources of fracture were observed in the burr area for trimming with clearances exceeding 10% of the material thickness.

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.

Experimental results on influence of trimming conditions on the shape of the sheared surface are combined with the results of stretching sheared samples after trimming. The objective of the research described in this paper is to study the mechanism of fracture initiation and cracks propagation during half-a-dog bone tensile test representing sheared edge stretching condition. One side of the sample had sheared surface obtained by the trimming process while the other side of the sample had a smooth surface. Significant attention was paid to understanding of fracture sources. An interrupted tensile test approach was employed to track fracture initiation and propagation during stretching of sheared surface. The results of the experimental study indicated that multiple sources of fracture were observed in the burr area for trimming with clearances exceeding 10% of the material thickness.

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.

The reliability theory of repairable systems is vastly different from that of non-repairable systems. The authors have recently proposed a ‘decision-based’ framework to design and maintain repairable systems for optimal performance and reliability using a set of metrics such as minimum failure free period, number of failures in planning horizon (lifecycle), and cost. The optimal solution includes the initial design, the system maintenance throughout the planning horizon, and the protocol to operate the system. In this work, we extend this idea by incorporating flexibility and demonstrate our approach using a smart charging electric microgrid architecture. The flexibility is realized by allowing the architecture to change with time. Our approach “learns” the working characteristics of the microgrid. We use actual load and supply data over a short time to quantify the load and supply random processes and also establish the correlation between them.

In this paper, experimental study and FEA simulation are performed to investigate the effect of three different methods for joining dissimilar metal coupons in terms of their strength and load transferring capacity. The joining techniques considered include adhesive bonding, bolting and hybrid bolting-and-bonding. Elastic-plastic material model with damage consideration is used for each of the joint components. Traction-separation rule and failure criterion is defined for adhesive. Load transfer capacity and the failure mode are assessed for each type of joining. Joint strength is examined in terms of the effects of adhesive property, bolt preload level, and friction coefficient. Results show that load transferred and failure mechanism vary significantly between samples with different joint methods; preload evolution in bolt changes with friction coefficient; hybrid joint generally has advantage over the other two methods, namely, bolting-only and bonding-only.

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. It also affects the scheduling for preventive maintenance. Reliability usually degrades with time increasing therefore, the lifecycle cost due to more frequent failures which result in increased warranty costs, costly repairs and loss of market share. In a lifecycle cost based design, we must account for product quality and preventive maintenance using time-dependent reliability. Quality is a measure of our confidence that the product conforms to specifications as it leaves the factory. For a repairable system, preventive maintenance is scheduled to avoid failures, unnecessary production loss and safety violations. This article proposes a methodology to obtain the optimal scheduling for preventive maintenance using time-dependent reliability principles.

In this article we present an approach to identify the system topology using simulation for reliability calculations. The system topology provides how all components in a system are functionally connected. Most reliability engineering literature assumes that either the system topology is known and therefore all failure modes can be deduced or if the system topology is not known we are only interested in identifying the dominant failure modes. The authors contend that we should try to extract as much information about the system topology from failure or success information of a system as possible. This will not only identify the dominant failure modes but will also provide an understanding of how the components are functionally connected, allowing for more complicated analyses, if needed. We use an evolutionary approach where system topologies are generated at random and then tested against failure or success data. The topologies evolve based on how consistent they are with test data.

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.

Monte Carlo simulation is a popular tool for reliability assessment because of its robustness and ease of implementation. A major concern with this method is its computational cost; standard Monte Carlo simulation requires quadrupling the number of replications for halving the standard deviation of the estimated failure probability. Efforts to increase efficiency focus on intelligent sampling procedures and methods for efficient calculation of the performance function of a system. This paper proposes a new method to manage cost that views design as a decision among alternatives with uncertain reliabilities. Information from a simulation has value only if it enables the designer to make a better choice among the alternative options. Consequently, the value of information from the simulation is equal to the gain from using this information to improve the decision. A designer can determine the number of replications that are worth performing by using the method.

Reaching a system level reliability target is an inverse problem. Component level reliabilities are determined for a required system level reliability. Because this inverse problem does not have a unique solution, one approach is to tradeoff system reliability with cost and to allow the designer to select a design with a target system reliability, using his/her preferences. In this case, the component reliabilities are readily available from the calculation of the reliability-cost tradeoff. To arrive at the set of solutions to be traded off, one encounters two problems. First, the system reliability calculation is based on repeated system simulations where each system state, indicating which components work and which have failed, is tested to determine if it causes system failure, and second, the task of eliciting and encoding the decision maker's preferences is extremely difficult because of uncertainty in modeling the decision maker's preferences.

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.

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. As time progresses, the product may fail due to time-dependent operating conditions and material properties, component degradation, etc. The reliability degradation with time may increase the lifecycle cost due to potential warranty costs, repairs and loss of market share. Reliability is the probability that the system will perform its intended function successfully for a specified time interval. In this work, we consider the first-passage reliability which accounts for the first time failure of non-repairable systems. Methods are available in the literature, which provide an upper bound to the true reliability which may overestimate the true value considerably. Monte-Carlo simulations are accurate but computationally expensive.

Understanding reliability is critical in design, maintenance and durability analysis of engineering systems. A reliability simulation methodology is presented in this paper for vehicle fleets using limited data. The method can be used to estimate the reliability of non-repairable as well as repairable systems. It can optimally allocate, based on a target system reliability, individual component reliabilities using a multi-objective optimization algorithm. The algorithm establishes a Pareto front that can be used for optimal tradeoff between reliability and the associated cost. The method uses Monte Carlo simulation to estimate the system failure rate and reliability as a function of time. The probability density functions (PDF) of the time between failures for all components of the system are estimated using either limited data or a user-supplied MTBF (mean time between failures) and its coefficient of variation.

Multi-attribute decision making and multi-objective optimization complement each other. Often, while making design decisions involving multiple attributes, a Pareto front is generated using a multi-objective optimizer. The end user then chooses the optimal design from the Pareto front based on his/her preferences. This seemingly simple methodology requires sufficient modification if uncertainty is present. We explore two kinds of uncertainties in this paper: uncertainty in the decision variables which we call inherent design problem (IDP) uncertainty and that in knowledge of the preferences of the decision maker which we refer to as preference assessment (PA) uncertainty. From a purely utility theory perspective a rational decision maker maximizes his or her expected multi attribute utility.

Automotive systems engineering addresses the system throughout its life cycle, including requirement, specification, design, implementation, verification and validation of systems, modeling, simulation, testing, manufacturing, operation and maintenance. This book is the fourth in a series of four volumes on this subject and features 12 papers, published between 2002-2009, that address the challenges and importance of systems approach in system verification and validation, stressing the use of advanced tools and approaches. Topics covered include: Systems integration and verification Software engineering in future automotive systems development Configuration management of the model-based design process Buy the Set and Save!

Automotive systems engineering addresses the system throughout its life cycle, including requirement, specification, design, implementation, verification and validation of systems, modeling, simulation, testing, manufacturing, operation and maintenance. This four-volume set features 49 papers, originally published from 1999 through 2010, that cover the latest research and developments on various aspects of automotive systems engineering. The four-volume set consists of these individual volumes: Automotive Systems Engineering - Overview Automotive Systems Engineering - Requirements and Testing Automotive Systems Engineering - Modeling Automotive Systems Engineering - Approach and Verification

Automotive systems engineering addresses the system throughout its life cycle, including requirement, specification, design, implementation, verification and validation of systems, modeling, simulation, testing, manufacturing, operation and maintenance. This book - the second in a series of four volumes on this subject - features 11 papers, published between 2000-2010, that address the challenges and importance of requirements and testing in systems engineering, stressing the use of advanced tools and approaches. Topics covered include: Creating correct requirements Requirement analysis Document management Development Management Architecture for military vehicles Buy the Set and Save! Automotive Systems Engineering The four-volume set consists of these individual volumes: Automotive Systems Engineering - Overview Automotive Systems Engineering - Requirements and Testing Automotive Systems Engineering - Modeling Automotive Systems Engineering - Approach and Verification

Automotive systems engineering addresses the system throughout its life cycle, including requirement, specification, design, implementation, verification and validation of systems, modeling, simulation, testing, manufacturing, operation and maintenance. This book - the third in a series of four volumes on this subject - features 11 papers, published between 1999-2010, that address the challenges and importance of systems modeling, stressing the use of advanced tools and approaches. Topics covered include: Automotive systems modeling Model-based design culture Applications Buy the Set and Save! Automotive Systems Engineering The four-volume set consists of these individual volumes: Automotive Systems Engineering - Overview Automotive Systems Engineering - Requirements and Testing Automotive Systems Engineering - Modeling Automotive Systems Engineering - Approach and Verification