Two-point constraint approximation in structural optimization

The use of constraint approximations is recognized as a primary means of achieving computational efficiency in structural optimization. Existing approximation methods are based upon the value of the constraint function and its derivatives at a single point. The present paper explores the use of approximations based upon the value of the constraint and its derivative at two points. Several candidate approximations are suggested and tested for randomly generated rational constraint functions. Several of the approximations prove to be superior to the single-point approximations.     

Convenient construction of t-tests for quality evaluation

An available numerical approximation to the Student's t distribution is described and its use in the construction of t-tests is presented. The result is a convenient and accurate computer based approach to the selection of the sample size and critical value to be used in a t-test. Computational logic for implementation of the approach is provided and the method is illustrated by an example.     

Coordinated warranty and burn-in strategies

In product reliability assurance, the warranty and burn-in (W&BI) strategies are usually selected separately, despite the fact that both depend on the early-life failure behavior of the product. This paper treats W&BI strategies together in order to examine the possible benefits of coordinated strategies for product performance management. As these strategies are meaningful only for decreasing hazard-rate systems, a Weibull life distribution is assumed for each system component. A net-profit model that includes an increase in product price as a function of warranty duration is constructed. The model shows how a coordinated W&BI strategy can be selected. The model is quite general and its extension to other cases is explained. A central point that is treated thoroughly is the renewal analysis necessary to determine replacement costs during burn-in and during the warranty period. As part of the analysis, a useful approximation is defined, and efficient optimization routines are identified. An example illustrates the use of analytical methods. The analysis and discussion of the example show that there are advantages in coordinating the selection of W&BI strategies     

Evaluating and Implementing 3-Level Control Charts

In some situations, an appropriate quality measure uses three or more discrete levels (rather than an attributes or a variables measure) to classify a product characteristic. In previous work, we refer to such a classification scheme as a multi-level product quality measure, and present a general methodology for the evaluation and implementation of 3-level (conforming, marginal, nonconforming) acceptance sampling plans. In this paper, we apply this classification scheme and methodology for the purpose of evaluating and implementing 3-level control charts. The OC and ARL functions are formulated and integrated into an approximate parameter selection method. Because of the approximation, a manufacturer must overstate their ARL expectations in order to obtain reasonable control chart performance.     

Comments on `Diagnostic-strategy selection for a series system' byJ.A. Nachlas et al

The authors comment on the work of J.A. Nachlas et al. (see ibid., vol.39, no.3, p.273-80, 1990) that presents diagnostic strategies for series systems with both perfect and imperfect testing. Their comment concerns the results given for the perfect-test case. They hold that the proof of the theorem which provides a simple optimal test sequence is incomplete and give a necessary criterion for an optimal strategy     

Diagnostic-strategy selection for series systems

The selection of efficient testing strategies for repairable systems composed of components arranged in series is considered. Two cost models (for perfect and imperfect testing) represent the consequences of possible test realizations. The probability that any particular component is responsible for the failure is derived and used as a basis for the two models. The model for perfect testing is solved exactly. In the optimal perfect-test sequence the components are tested in decreasing order of the ratio of: [probability that the component is responsible for the system failure] to [component test cost]. For imperfect testing, possible diagnostic errors are included in a model for which two heuristic solution strategies are provided. The model represents the consequences of both false-positive and false-negative component-test outcomes. The heuristic strategies yield efficient test sequences. Under reasonable assumptions, the second heuristic strategy is guaranteed to locate the optimal test sequence. The model can quantitatively evaluate the benefits of test-accuracy enhancement plans. These models and algorithms provide convenient methods for selecting efficient test-sequences. This is illustrated by representative examples     

Evaluating and Implementing 3-Level Acceptance Sampling Plans

There are many situations in which product quality can be described by classifying a product using three or more discrete levels. For example, a food product may be classified as good, marginal, or bad depending on the concentration of harmful microorganisms in the product. In this paper, a generic framework is defined for establishing 3-level acceptance sampling plans. These plans utilize what we refer to as quality value functions. The Operating Characteristic function for these plans is constructed and used to develop an approximate parameter selection method based on the Central Limit Theorem. The results of testing this method using numerical examples are presented. The problem of quality value function selection is also addressed. A detailed example is presented, which includes the implementation of both the parameter and quality value function selection methods.     

A general model for age acceleration during thermal cycling

Conventional practice in evaluating the effect of elevated temperature upon electronic devices in either accelerated testing or stress screening is the use of the Arrhenius reaction rate equation. This equation fails to represent several key aspects of the stress. A general model that includes the reaction rate effects during heating and cooling, the mechanical effects of heating and cooling, and non-constant activation energies is provided here. The model is based upon a general representation of a thermal cycle. Once the general model is constructed, it is shown to accommodate as special cases, acceleration when one or more of the features is assumed absent. An example illustrates this point. Then several uses for the model beyond the computation of the acceleration factor are discussed. It is suggested that the general model can be used to support test design and equipment selection decisions. The model therefore provides a more realistic portrayal of the effects of a thermal cycle and increased decision flexibility in defining thermal stress regimens.     

Reliability analysis of fault tolerant memory arrays

A model is developed to represent computer memory module reliability as a function of memory array reliability under a fault tolerant design. The fault tolerance feature of the array actually results from a revision in the use of the array so that with respect to some failure modes, the array becomes a K out of N rather than a series system. The model is used to determine array reliability under fault tolerance. The ratio of module reliability under fault tolerance to that without this feature is used as a measure of the benefits of revising array use. A key feature of the analysis is the fact that not all faults can be tolerated. The elemental memory devices examined conform to a decreasing Weibull hazard model. Consequently, evaluation of the general model for the K out of N system realized must be done numerically. However, for the special case in which K=N-1, a closed form expression for the performance measure is obtained. This special case occurs for the application of interest and it is shown that the performance measure always exceeds one and depends directly upon the proportion of faults that can be tolerated. Thus the value of fault tolerance is shown to depend upon the extent to which the array will tolerate faults. This provides a basis for deciding whether or not fault tolerance should be implemented.