Probabilistic optimization design offers tools for making reliable decisions with the consideration of uncertainty associated with design variables/parameters and simulation models. In a probabilistic design, such as reliability-based design and robust design, the design feasibility is formulated probabilistically such that the probability of the constraint satisfaction (reliability) exceeds the desired limit. The reliability assessment for probabilistic constraints often involves an iterative procedure; therefore, two loops are involved in a probabilistic optimization. Due to the double-loop procedure, the computational demand is extremely high. To improve the efficiency of a probabilistic design, a novel method – sequential optimization and reliability assessment (SORA) is developed in this paper. The SORA method employs a single-loop strategy where a serial of cycles of optimization and reliability assessment is employed. In each cycle optimization and reliability assessment are decoupled from each other; no reliability assessment is required within optimization and the reliability assessment is only conducted after the optimization. The key concept of the proposed method is to shift the boundaries of violated deterministic constraints (with low reliability) to the feasible direction based on the reliability information obtained in the previous cycle. Hence the design is quickly improved from cycle to cycle and the computational efficiency is improved significantly. Two engineering applications, the reliability-based design for vehicle crashworthiness of side impact and the integrated reliability and robust design of a speed reducer, are presented to demonstrate the effectiveness of the SORA method.Copyright © 2002 by ASME

Sequential Optimization and Reliability Assessment Method for Efficient Probabilistic Design

Reliability is the ability of a product or system to perform as intended (i.e., without failure and within specified performance limits) for a specified time, in its life-cycle environment. Commonly used electronics reliability prediction methods (e.g., Mil-HDBK-217, 217-PLUS, PRISM, Telcordia, FIDES) based on handbook methods have been shown to be misleading and provide erroneous life predictions. The use of stress and damage models permits a far superior accounting of the reliability and the physics of failure (PoF); however, sufficient knowledge of the actual operating and environmental application conditions of the product is still required.



This article presents a PoF-based prognostics and health management approach for effective reliability prediction. PoF is an approach that utilizes knowledge of a product's life-cycle loading and failure mechanisms to perform reliability modeling, design, and assessment. This method permits the assessment of the reliability of a system under its actual application conditions. It integrates sensor data with models that enable in situ assessment of the deviation or degradation of a product from an expected normal operating condition and the prediction of the future state of reliability. This article presents a formal implementation procedure, which includes failure modes, mechanisms, and effects analysis, data reduction and feature extraction from the life-cycle loads, damage accumulation, and assessment of uncertainty. Applications of PoF-based prognostics and health management are also discussed.


Keywords:

reliability;
prognostics;
physics of failure;
design-for-reliability;
reliability prediction

Prognostics and Health Management of Electronics

In robust design, it is important not only to achieve robust design objectives but also to maintain the robustness of design feasibility under the effect of variations (or uncertainties). However, the evaluation of feasibility robustness is often a computationally intensive process. Simplified approaches in existing robust design applications may lead to either over-conservative or infeasible design solutions. In this paper, several feasibility-modeling techniques for robust optimization are examined. These methods are classified into two categories: methods that require probability and statistical analyses and methods that do not. Using illustrative examples, the effectiveness of each method is compared in terms of its efficiency and accuracy. Constructive recommendations are made to employ different techniques under different circumstances. Under the framework of probabilistic optimization, we propose to use a most probable point (MPP) based importance sampling method, a method rooted in the field of reliability analysis, for evaluating the feasibility robustness. The advantages of this approach are discussed. Though our discussions are centered on robust design, the principles presented are also applicable for general probabilistic optimization problems. The practical significance of this work also lies in the development of efficient feasibility evaluation methods that can support quality engineering practice, such as the Six Sigma approach that is being widely used in American industry.

Towards a Better Understanding of Modeling Feasibility Robustness in Engineering Design

In robust design, associated with each quality characteristic, the design objective often involves multiple aspects such as bringing the mean of performance on target and minimizing the variations. Current ways of handling these multiple aspects using either the Taguchi's signal-to-noise ratio or the weighted-sum method are not adequate. In this paper, we solve bi-objective robust design problems from a utility perspective by following upon the recent developments on relating utility function optimization to a Compromise Programming (CP) method. A robust design procedure is developed to allow a designer to express his/her preference structure of multiple aspects of robust design. The CP approach, i.e., the Tchebycheff method, is then used to determine the robust design solution which is guaranteed to belong to the set of efficient solutions (Pareto points). The quality utility at the candidate solution is represented by means of a quadratic function in a certain sense equivalent to the weighted Tchebycheff metric. The obtained utility function can be used to explore the set of efficient solutions in a neighborhood of the candidate solution. The iterative nature of our proposed procedure will assist decision making in quality engineering and the applications of robust design.

Quality utility : a Compromise Programming approach to robust design

A review : On the development of low melting temperature Pb-free solders

TAGUCHI ON ROBUST TECHNOLOGY DEVELOPMENT BRINGING QUALITY ENGINEERING UPSTREAM ASME PRESS SERIES ON INTERNATIONAL PDF Are you looking for taguchi on robust technology development bringing quality engineering upstream asme press series on international Books? Now, you will be happy that at this time taguchi on robust technology development bringing quality engineering upstream asme press series on international PDF is available at our online library. With our complete resources, you could find taguchi on robust technology development bringing quality engineering upstream asme press series on international PDF or just found any kind of Books for your readings everyday.

Taguchi on Robust Technology Development: Bringing Quality Engineering Upstream

Random events discrete random variables continuous random variables systems of random variables functions of random variables entropy and information random processes - correlation theory random processes - spectral theory extreme value distributions reliability Markovian processes random fatigue geometric tolerance random loads and responses in some engineering systems processing of experimental data.

/pdf/applied-probability-for-engineers-and-scientists-23yxz567l1.pdf

Applied Probability for Engineers and Scientists

PRODUCT EFFECTIVENESS AND WORTHProduct AttributesProgrammatic FactorsProduct Effectiveness FactorsOperational AvailabilityDesign, Use, and Logistic EffectivenessDesign Effectiveness.. Use Effectiveness. Logistic EffectivenessReliabilityMission Reliability. Logistic ReliabilityRestorationMaintainabilityTime Elements and Product EffectivenessRelationships among Time IntervalsAssigning ResponsibilityIntegrated Product and Process DevelopmentManaging Product EffectivenessSummaryPROBABILITY CONCEPTSRandom EventsDefinitions of ProbabilityBasic Theorems of ProbabilityConditional Probability and Multiplication Rule. Statistical Independence. Total Probability Theorem. Bayes' TheoremRandom Variables and Their DistributionsRandom VariablesProbability DistributionMain Descriptors of a Random VariableMean. Variance and Standard Deviation. Markov Inequality. Chebyshev Inequality. Skewness. Quantiles and PercentilesBrief Review of DistributionsDiscrete Distributions. Continuous DistributionsMultiple Random VariablesJoint Probability. Conditional Probability DistributionsCovariance and CorrelationFunctions of Random VariablesProbability DistributionsMain Descriptors of Random FunctionsRandom ProcessesDefinition of a Random ProcessMain Descriptors of Random ProcessMean Value. VarianceStationary Random ProcessesErgodicity of Random ProcessesCounting ProcessesRecurrent Point ProcessesMarkov ProcessSome Limit Results in Probability and Stochastic ProcessesLimit Theorems in Probability TheoryThe Central Limit Theorem. The Poisson Theorem. A Random Number of Random Variables in a SumStochastic ProcessesCrossing a "High Level" by Continuous Process. Thinning a Point ProcessThe Superposition of Point ProcessesSTATISTICAL INFERENCE CONCEPTSStatistical EstimationPoint EstimationMethod of Moments. Method of Maximum LikelihoodInterval EstimationHypothesis TestingFrequency HistogramGoodness-of-Fit TestsThe Chi-Square Test. The Kolmogorov-Smirnov Test. Sample ComparisonReliability Regression Model FittingGauss-Markov Theorem and Linear RegressionRegression Analysis. The Gauss-Markov Theorem. Multiple Linear RegressionProportional Hazard (PH) and Accelerated Life (AL) ModelsAccelerated Life (AL) Model. Proportional Hazard (PH) ModelAccelerated Life Regression for Constant StressAccelerated Life Regression for Time-Dependent StressPRACTICAL RELIABILITY CONCEPTSReliability MeasuresTime-to-Failure Distribution and Reliability FunctionMean Time to Failure and Percentile LifeFailure Rate and Cumulative Hazard FunctionLife Distributions as Reliability ModelsGeometric DistributionThe Binomial DistributionExponential DistributionClasses of Distribution Functions Based on AgingFailure Rate and the Notion of Aging. Bounds on Reliability for Aging Distributions. Inequality for Coefficient of Variation. Cumulative Damage ModelThe Weibu

Product Reliability, Maintainability, and Supportability Handbook

Modern Solder Technology for Competitive Electronics Manufacturing

Plastic Packaging Materials Manufacturing Processes Assembly Onto Printed Wiring Boards Packing and Handling Failure Mechanisms, Sites, and Modes Quality Assurance Qualification and Accelerated Testing Defect Analysis Techniques Trends and Challenges Glossary Index.

Anthony J. Rafanelli

Papers

Taguchi on Robust Technology Development: Bringing Quality Engineering Upstream

Applied Probability for Engineers and Scientists

Product Reliability, Maintainability, and Supportability Handbook

Modern Solder Technology for Competitive Electronics Manufacturing

Plastic Encapsulated Microelectronics; Materials, Processes, Quality, Reliability, and Application