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Tensile and Fatigue Behaviors of Two Thermoplastics Including Strain Rate, Temperature, and Mean Stress Effects

Univ. of Toledo-Ali Fatemi, Steve Mellot
General Motors Co.-Abolhassan Khosrovaneh, Charles Buehler
Published 2014-04-01 by SAE International in United States
An experimental investigation was conducted to evaluate tensile and fatigue behaviors of two thermoplastics, a neat impact polypropylene and a mineral and elastomer reinforced polyolefin. Tensile tests were performed at various strain rates at room, −40°C, and 85°C temperatures with specimens cut parallel and perpendicular to the mold flow direction. Tensile properties were determined from these tests and mathematical relations were developed to represent tensile properties as a function of strain rate and temperature. For fatigue behavior, the effects considered include mold flow direction, mean stress, and temperature. Tension-compression as well as tension-tension load-controlled fatigue tests were performed at room temperature, −40°C and 85°C. The effect of mean stress was modeled using the Walker mean stress model and a simple model with a mean stress sensitivity factor.
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Fatigue Life Prediction of an Automobile Cradle Mount

SAE International Journal of Passenger Cars - Mechanical Systems

Univ. of Toledo-Touhid Zarrin-Ghalami, Ali Fatemi
Chrysler Group LLC-Yung-Li Lee
  • Journal Article
  • 2013-01-1009
Published 2013-04-08 by SAE International in United States
Elastomers have large reversible elastic deformation, good damping and high energy absorption capabilities. Due to these characteristics along with low cost of manufacturing, elastomeric components are widely used in many industries and applications, including in automobiles. These components are typically subjected to complex multiaxial and variable amplitude cyclic loads during their service life. Therefore, fatigue failure and life prediction are important issues in the design and analyses of these components. Availability of an effective CAE technique to evaluate fatigue damage and to predict fatigue life under complex loading conditions is a valuable tool for such analysis. This paper discusses a general CAE analytical technique for durability analysis and life prediction of elastomeric components. The methodology is then illustrated and verified by using experimental fatigue test results from an automobile cradle mount. The developed methodology involves constitutive behavior and fatigue behavior of the material, finite element analysis of the component, and fatigue damage quantification for life predictions. The commonly used Rainflow cycle counting method and Miner linear cumulative damage rule for metals are also evaluated for…
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Efficient Random Vibration Analysis Using Markov Chain Monte Carlo Simulation

SAE International Journal of Materials and Manufacturing

Univ. of Toledo-Mahdi Norouzi, Efstratios Nikolaidis
  • Journal Article
  • 2012-01-0067
Published 2012-04-16 by SAE International in United States
Reliability assessment of dynamic systems with low failure probability can be very expensive. This paper presents and demonstrates a method that uses the Metropolis-Hastings algorithm to sample from an optimal probability density function (PDF) of the random variables. This function is the true PDF truncated over the failure region. For a system subjected to time varying excitation, Shinozuka's method is employed to generate time histories of the excitation. Random values of the frequencies and the phase angles of the excitation are drawn from the optimal PDF. It is shown that running the subset simulation by the proposed approach, which uses Shinozuka's method, is more efficient than the original subset simulation. The main reasons are that the approach involves only 10 to 20 random variables, and it takes advantage of the symmetry of the expression of the displacement as a function of the inputs. The paper demonstrates the method on two examples.
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System Failure Identification using Linear Algebra: Application to Cost-Reliability Tradeoffs under Uncertain Preferences

Univ. of Toledo-Efstratios Nikolaidis
Oakland Univ.-Vijitashwa Pandey, Zissimos Mourelatos
Published 2012-04-16 by SAE International in United States
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. Establishing if a system state leads to system failure is a cumbersome process using existing techniques. The contribution of this paper is in developing a method to streamline this process, using…
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Estimation of High-Cycle Fatigue Life by using Re-analysis

SAE International Journal of Materials and Manufacturing

Univ. of Toledo-Mahdi Norouzi, Efstratios Nikolaidis
  • Journal Article
  • 2012-01-0066
Published 2012-04-16 by SAE International in United States
In design of real-life systems, such as the suspension of a car, an offshore platform or a wind turbine, there are significant uncertainties in the model of the inputs. For example, scarcity of data leads to inaccuracies in the power spectral density function of the waves and the probability distribution of the wind speed. Therefore, it is necessary to evaluate the performance and safety of a system for different probability distributions. This is computationally expensive or even impractical. This paper presents a methodology to assess efficiently the fatigue life of structures for different power spectra of the applied loads. We accomplish that by reweighting the incremental damage calculated in one simulation. We demonstrate the accuracy and efficiency of the proposed method on an example which involves a nonlinear quarter car under a random dynamic load. The fatigue life of the suspension spring under loads generated by a sampling spectrum is calculated. Then, the fatigue life for different spectra is estimated by using re-analysis. We compare the results with those from simulation to validate the method.
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Managing the Computational Cost in a Monte Carlo Simulation by Considering the Value of Information

Univ. of Toledo-Efstratios Nikolaidis
Oakland Univ.-Vijitashwa Pandey, Zissimos Mourelatos
Published 2012-04-16 by SAE International in United States
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. The study in this paper suggests that one may need much fewer replications than one expects in order to make an informed design decision.
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Efficient Probabilistic Reanalysis and Optimization of a Discrete Event System

SAE International Journal of Materials and Manufacturing

Yibo Li
Univ. of Toledo-Efstratios Nikolaidis
  • Journal Article
  • 2011-01-1081
Published 2011-04-12 by SAE International in United States
This paper presents a methodology to evaluate and optimize discrete event systems, such as an assembly line or a call center. First, the methodology estimates the performance of a system for a single probability distribution of the inputs. Probabilistic Reanalysis (PRRA) uses this information to evaluate the effect of changes in the system configuration on its performance. PRRA is integrated with a program to optimize the system. The proposed methodology is dramatically more efficient than one requiring a new Monte Carlo simulation each time we change the system. We demonstrate the approach on a drilling center and an electronic parts factory.
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Time-Dependent Reliability of Random Dynamic Systems Using Time-Series Modeling and Importance Sampling

SAE International Journal of Materials and Manufacturing

Univ. of Toledo-Efstratios Nikolaidis
Oakland Univ.-Amandeep Singh, Zissimos Mourelatos
  • Journal Article
  • 2011-01-0728
Published 2011-04-12 by SAE International in United States
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. A computationally efficient importance sampling technique is presented to calculate the cumulative probability of failure for random dynamic systems excited by a stationary input random process. Time series modeling is used to characterize the input random process from only one sample function of the random process. Examples are presented to demonstrate the accuracy and efficiency of the…
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Surface Finish Effects on Fatigue Behavior of Forgings

SAE International Journal of Materials and Manufacturing

Univ. of Toledo-Sean Mckelvey, Ali Fatemi
  • Journal Article
  • 2011-01-0488
Published 2011-04-12 by SAE International in United States
Fatigue fractures are the most common type of mechanical failures of components and structures. It is widely recognized that surface finish has a significant effect on fatigue behavior. Forgings can be accompanied by significant surface roughness and decarburization. The correction factors used in many mechanical design textbooks to correct for the as-forged surface condition are typically based on data published in the 1940's. It has been found by several investigators that the existing data for as-forged surface condition is too conservative. Such conservative values often result in over-engineered designs of many forged parts, leading not only to increased cost, but also inefficiencies associated with increased weight, such as increased fuel consumption in the automotive industry. In addition, this can reduce forging competitiveness as a manufacturing process in terms of cost and performance prediction in the early design stage, compared to alternative manufacturing processes. In this paper two surface conditions are evaluated, a smooth-polished surface finish to be used as the reference surface and a hot-forged surface finish, in order to quantify forged surface finish effect…
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Multi-Objective Decision Making under Uncertainty and Incomplete Knowledge of Designer Preferences

SAE International Journal of Materials and Manufacturing

Univ. of Toledo-Efstratios Nikolaidis
Oakland Univ.-Vijitashwa Pandey, Zissimos Mourelatos
  • Journal Article
  • 2011-01-1080
Published 2011-04-12 by SAE International in United States
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. We show how this is inherently inconsistent with providing the decision maker with alternatives on the Pareto Front unless the decision maker trades off attributes or some function thereof linearly. In this paper we propose a methodology, rooted in a set of axioms that can be used in conjunction with a modified Pareto Front to select the best design. We present our…
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