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A simulation-based, system reliability-based design optimization (RBDO) method is presented that can handle problems with multiple failure regions and correlated random variables. Copulas are used to represent the correlation. The method uses a Probabilistic Re-Analysis (PRRA) approach in conjunction with a trust-region optimization approach and local metamodels covering each trust region. PRRA calculates very efficiently the system reliability of a design by performing a single Monte Carlo (MC) simulation per trust region. Although PRRA is based on MC simulation, it calculates “smooth” sensitivity derivatives, allowing therefore, the use of a gradient-based optimizer. The PRRA method is based on importance sampling. It provides accurate results, if the support of the sampling PDF contains the support of the joint PDF of the input random variables. The sequential, trust-region optimization approach satisfies this requirement.

Piston design is a challenging engineering problem which involves complex physics and requires satisfying multiple performance objectives. Uncertainty in piston operating conditions and variability in piston design variables are inevitable and must be accounted for. The piston assembly can be a major source of engine mechanical friction and cold start noise, if not designed properly. In this paper, an analytical piston model is used in a deterministic and probabilistic (reliability-based) multi-objective design optimization process to obtain an optimal piston design. The model predicts piston performance in terms of scuffing, friction and noise, In order to keep the computational cost low, efficient and accurate metamodels of the piston performance metrics are used. The Pareto set of all optimal solutions is calculated allowing the designer to choose the “best” solution according to trade-offs among the multiple objectives.

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.

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.

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.

For designing new products or developing new specifications, the reliability performance of systems and components experienced by the customer provides invaluable information for the engineer. This information, not only provides for the visibility of reliability requirements, but also an awareness of potential degradation of the systems and components during its life cycle. In this paper, a method is presented for predicting vehicle system and component reliability from vehicle fleet repair data. This method combines sampling stratification, computer data analysis and statistical modeling techniques into a reliability analysis procedure to provide reliability prediction. Specifically, published vehicle fleet data was used to provide the basis for predicting the vehicle system and component reliability at any mileage level.

This paper discusses the feasibility and issues associated with integrating a consumer off-the shelf product into a vehicle. For this evaluation, we selected a handheld personal computer (HPC), cellular telephone and modem to integrate with the vehicle audio, climate and system controls. Connectivity between the HPC and the vehicle is established by the use of the standard infrared serial data link that comes with the HPC. Connectivity outside the vehicle uses a cellular telephone for voice and a cellular digital packet data (CDPD) modem for data. This system is built into the Dodge ESX-2 hybrid powered concept vehicle for demonstration.

The rapid growth of information technology has the potential to affect many of the reasons why people drive. The Internet is arguably the most significant recent milestone in the growth of information technology. This paper examines the ways Internet communication might affect the travel experience by a) eliminating traditional reasons for personal travel, b) providing new reasons, c) changing the balance between personal and freight travel, and d) changing trip length distribution. Changes of the types listed could affect the product demand "mix" for electric, hybrid-electric and fuel cell vehicles being developed.

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.

The automobile industry has an increasing demand for lightweight components, improve product performance, efficiency and increase safety. For optimization of design and manufacturing of these components the detail measurement of critical material properties such as strain limit, strength coefficients, anisotropy coefficients et al, are required. The most commonly used method for finding the material properties is tensile test with extensometer. But this system only provides an average strain over the specimen gauge length and is not applicable to post diffuse necking. Digital Image Correlation (DIC) technique is the latest state of art technique. Because of its capability of fast data acquisition, this technique is suited well for characterization of material properties both in the elastic and plastic ranges. It also has advantages of full field, non-contact, and considerately high precision for displacement and strain measurements.

Tapered roller bearings have proven successful in a number of high-volume automatic transaxle designs. Typically, tapered roller bearings are required to carry high loads generated by helical and hypoid gears. To meet the demands of a successful design, a number of factors must be considered in the selection and application of tapered roller bearings. This paper presents a discussion of these factors as well as results from Chrysler's transaxle testing. Selection of tapered roller bearings is based on the transmission duty cycle developed using load and speed histograms, gear data, size constraints, and life requirements. A bearing life analysis considering the total transaxle system is conducted using a sophisticated computer program. Various system effects are analyzed including the load/speed cycle, housing and shaft rigidity, lubrication, bearing setting, thermal effects, and bearing internal design.

The reliability of an electronic sensor in the automotive applications is assessed using data from Fleet Test and proving ground Vehicle Endurance test. These nonfailure data are multiply censored at different mileage. Reliability analysis of data with no failure is rarely discussed in most reliability literature. This paper applies the Weibull maximum likelihood analysis based on known values of the Weibull shape parameter to extract useful reliability information. The well-known Weibayes and Weibest methods are subsets of the discussed approach. The sensitivity of the change of reliability levels over a range of Weibull shape parameter values is also examined in our case. The Huang-Porter (1991) approach of obtaining a reliability lower bound regardless of the Weibull shape parameter values is also applied and its potential of practical application is discussed. Practical limitations of all methods are discussed.

Higher horsepower per liter engines have put more demand on the crankshaft, often requiring the use of forged steel. This paper examines cost reduction opportunities to offset the penalties associated with forged steel, with raw material and machinability being the primary factors evaluated. A cost model for crankshaft processing is utilized in this paper as a design tool to select the lowest cost material grade. This model is supported by fatigue and machinability data for various steel grades. Materials considered are medium carbon, low alloy, and microalloy steels; the effects of sulfur as a machining enhancer is also studied.

The automobile industry has been using high-density polyethylene (HDPE) as a material to fabricate fuel tanks. Because untreated HDPE is permeable to the primary constituents of gasoline, these fuel tanks are now being produced with various barrier technologies that significantly reduce this permeation rate. Four currently available barrier technologies are fluorination, sulfonation, coextrusion, and the laminar barrier technology. These technologies have successfully proven to decrease the permeation rate of pure gasoline. However, it is suspected that their effectiveness may be reduced when alcohols are introduced into the fuel blend. In this work, we determine the permeation rates of gasoline-alcohol fuel blends through HDPE by conducting tests on 22-gallon HDPE fuel tanks and on small HDPE bottles fabricated with and without these barrier technologies. The goal of this study is to provide a comprehensive evaluation of these four barrier technologies.

Seats play an important role in determining customer satisfaction and safety. They also represent three to five percent of the overall vehicle cost and weight. Therefore, automotive manufacturers are continuously seeking ways to improve the areas of comfort, safety, reliability, cost and weight within the seat system. The purpose of this paper is to review the development of an automotive front seat constructed of injection molded nylon frames and metal mechanisms. This development program was initiated for the purpose of reducing vehicle weight while increasing the reliability and safety of the front seats. This paper will review the material and process selection decision, a design overview, the performance criteria and the results of tests performed on the injection molded front seats.

An OEM Natural Gas Vehicle (NGV) has been developed to address recently enacted Clean-Fuel Vehicle legislation. The NGV incorporates advanced fuel storage and fuel metering technologies to produce very low emissions and to provide superior customer value compared to aftermarket conversion units.

A new, 1-D analytical engine thermal management tool was developed to model piston, oil and coolant temperatures in the Ford 3.5L engine family. The model includes: a detailed lubrication system, including piston oil-squirters, which accurately represents oil flow rates, pressure drops and component heat transfer rates under non-isothermal conditions; a detailed coolant system, which accurately represents coolant flow rates, pressure drops and component heat transfer rates; a turbocharger model, which includes thermal interactions with coolant, oil, intake air and exhaust gases (modeled as air), and heat transfer to the surroundings; and lumped thermal models for engine components such as block, heads, pistons, turbochargers, oil cooler and cooling tower. The model was preliminarily calibrated for the 3.5L EcoBoost™ engine, across the speed range from 1500 to 5500 rpm, using wide-open-throttle data taken from an early heat rejection study.

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.