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A computer model can be used for predicting an output only after specifying the values of some unknown physical constants known as calibration parameters. The unknown calibration parameters can be estimated from real data by conducting physical experiments. This paper presents an approach to optimally design such a physical experiment. The problem of optimally designing physical experiment, using a computer model, is similar to the problem of finding optimal design for fitting nonlinear models. However, the problem is more challenging than the existing work on nonlinear optimal design because of the possibility of model discrepancy, that is, the computer model may not be an accurate representation of the true underlying model. Therefore, we propose an optimal design approach that is robust to potential model discrepancies. We show that our designs are better than the commonly used physical experimental designs that do not make use of the information contained in the computer model and other nonlinear optimal designs that ignore potential model discrepancies. We illustrate our approach using a toy example and a real example from industry., 25 pages, 10 figures

A manufacturing system collects big and heterogeneous data for tasks such as product quality modeling and data-driven decision-making. However, as the size of data grows, timely and effective data utilization becomes challenging. We propose an unsupervised data filtering method to reduce manufacturing big data sets with multi-variate continuous variables into informative small data sets. Furthermore, to determine the appropriate proportion of data to be filtered, we propose a filtering information criterion (FIC) to balance the tradeoff between the filtered data size and the information preserved. The case study of a babycare manufacturing and a simulation study have shown the effectiveness of the proposed method.

The use of lower cut-off values/concentration limits for the calculation of mixture classification in UN GHS/EU CLP versus the previous regulatory scheme (EU Dangerous Preparations Directive, DPD), has resulted in an increased number of classifications in the highest eye hazard category. Herein, a semi-quantitative categorisation of severity of eye effects, following accidental human exposures to detergents, was compared to the classification category of the products. Three schemes were evaluated: EU DPD; EU CLP (based on all available data and information, including weight of evidence); and EU CLP (based entirely on the calculation method). As reported by four EU Poison Centres, the vast majority of exposures had caused minor or no symptoms. Classification was a poor predictor of effects in man subjected to accidental exposure. Note however that this is also because effects are not only driven by the intrinsic hazard (as reflected in the classification), but also by the exposure conditions and mitigation (i.e. rinsing). EU CLP classification using all available data and information was more predictive of medically relevant symptoms than the EU CLP calculation method. The latter led to a poorer differentiation between irritating products versus products potentially causing serious eye damage.

In multistage manufacturing systems, modeling multiple quality indices based on the process sensing variables is important. However, the classic modeling technique predicts each quality variable one at a time, which fails to consider the correlation within or between stages. We propose a deep multistage multi-task learning framework to jointly predict all output sensing variables in a unified end-to-end learning framework according to the sequential system architecture in the MMS. Our numerical studies and real case study have shown that the new model has a superior performance compared to many benchmark methods as well as great interpretability through developed variable selection techniques., Comment: Accepted by Journal of Quality Technology

The purpose of model calibration is to make the model predictions closer to reality. The classical Kennedy-O'Hagan approach is widely used for model calibration, which can account for the inadequacy of the computer model while simultaneously estimating the unknown calibration parameters. In many applications, the phenomenon of censoring occurs when the exact outcome of the physical experiment is not observed, but is only known to fall within a certain region. In such cases, the Kennedy-O'Hagan approach cannot be used directly, and we propose a method to incorporate the censoring information when performing model calibration. The method is applied to study the compression phenomenon of liquid inside a bottle. The results show significant improvement over the traditional calibration methods, especially when the number of censored observations is large.

Sliced Latin hypercube designs (SLHDs) have important applications in designing computer experiments with continuous and categorical factors. However, a randomly generated SLHD can be poor in terms of space-filling, and based on the existing construction method that generates the SLHD column by column using sliced permutation matrices, it is also difficult to search for the optimal SLHD. In this article, we develop a new construction approach that first generates the small Latin hypercube design in each slice and then arranges them together to form the SLHD. The new approach is intuitive and can be easily adapted to generate orthogonal SLHDs and orthogonal array-based SLHDs. More importantly, it enables us to develop general algorithms that can search for the optimal SLHD efficiently.

For consumer protection, many governments perform random inspections on goods sold by weight or volume to ensure consistency between actual and labeled net contents. To pass inspection, random samples must jointly comply with restrictions placed on the individual sampled items and on the sample average. In this article, we consider the current United States National Institute of Standards and Technology joint acceptance criteria. Motivated by a problem from a real manufacturing process, we provide an approximation for the probability of sample acceptance that is applicable for processes with one or more known sources of variation via a random effects model. This approach also allows the assessment of the sampling scheme of the items. We use examples and simulations to assess the quality and accuracy of the approximation and illustrate how the methodology can be used to fine-tune process parameters for a prespecified probability of sample acceptance. Simulations are also used for estimating variance compon...

A high-level business need was addressed via the development of a solution for setting appropriate targets for product filling processes.

In mixture-of-mixtures experiments major components are defined as the components which themselves are mixtures of some other components, called minor components. Sometimes components are divided into different categories where each category is called a major component and the components within a major component become minor components. The special structure of the mixture-of-mixtures experiment makes the design and modeling approaches different from a typical mixture experiment. In this article we propose a new model called the major–minor model to overcome some of the limitations of the commonly used multiple-Scheffe model. We also provide a strategy for designing experiments that are much smaller in size than those based on the existing methods. We then apply the proposed design and modeling approach to a mixture-of-mixtures experiment conducted to formulate a new potato crisp. This article has supplementary material online.

The upper confidence bound for a product defect rate is a very important index for evaluating the production process in industry. In this paper, we provide a bootstrap methodology to construct a (1−α)100% upper confidence bound for the overall defect rate of a product whose quality assessment involves multiple pass/fail binary data and multiple continuous data. When only the pass/fail data are included we propose using a bootstrap method which is consistent with the Clopper–Pearson one-sided confidence interval. When only the continuous data are included the BCa bootstrap method is recommended. These two methods are combined to provide an upper confidence bound for the overall defect rate of the product when multiple pass/fail binary data and multiple continuous data are present. All methods are clearly stated in algorithmic form, investigated through simulation and demonstrated using example data sets. In the simulation studies and examples the proposed algorithms show great advantages in both coverage probability and computational efficiency. Copyright © 2010 John Wiley & Sons, Ltd.

There have been many linear regression models proposed to analyze mixture experiments including the Scheffé model, the slack-variable model, and the Kronecker model. The use of the slack-variable model is somewhat controversial within the mixture experiment research community. However, in situations that the slack-variable ingredient is used to fill in the formulation and the remaining ingredients have constraints such that they can be chosen independently of one another, the slack-variable model is extremely popular by practitioners mainly due to the ease of interpretation. In this article, we advocate that for some mixture experiments the slack-variable model has appealing properties including numerical stability and better prediction accuracy when model-term selection is performed. We also explain how the effects of the slack-variable model components should be interpreted and how easy it is for practitioners to understand the components effects. We also investigate how to choose the slack-variable component, what transformation should be used to reduce collinearity, and under what circumstances the slack-variable model should be preferred. Both simulation and practical examples are provided to support the conclusions.

When categorical noise variables are present in the Robust Parameter Design (RPD) context, it is possible to reduce process variance by not only manipulating the levels of the control factors but also by adjusting the proportions associated with the levels of the categorical noise factor(s). When no adjustment factors exist or when the adjustment factors are unable to bring the process mean close to target, a popular approach for determining optimal operating conditions is to find the levels of the control factors that minimize the estimated mean squared error of the response. Although this approach is effective, engineers may have a difficult time translating mean squared error into quality. We propose the use of a parts per million defective objective function. Furthermore, we point out that in many situations the levels of the control factors are not equally desirable due to cost and/or time issues. We have termed these types factors non-uniform control factors. We propose the use of desirability functions to determine optimal operating conditions when non-uniform control factors are present and illustrate this methodology with an example from industry. Copyright © 2006 John Wiley & Sons, Ltd.

Robust parameter design (RPD) as used in Industrial experiments is usually applied assuming a linear model under standard assumptions, but has recently also been considered in a generalized linear model (GLM) setting. The application of a general dual-r..

Robust parameter design has been extensively studied and applied in industrial experiments over the past twenty years. The purpose of robust parameter design is to design a process or product that ...

There has been considerable interest recently in the use of statistically designed experiments to identify both location and dispersion effects for quality improvement. Analysis of dispersion effects usually requires replications that can be expensive or time consuming. Several recent articles have considered identification of both location and dispersion effects from unreplicated fractional factorial experiments. In this article, we provide a systematic study of various methods that are commonly used or have been proposed recently. Both theoretical and simulation results are used to characterize the properties. Although all methods suffer from some degree of bias, some have serious problems when the bias remains large even as the design run size increases to infinity. Based on these analyses, we propose some iterative strategies for model selection and estimation of the dispersion effects. A real example and simulations as well are used to illustrate the results.

We believe that the current practice in HAI reporting of using estimation approaches rather than quality control approaches to data supply chain validation might be supportable for research. However, we believe that it should not be the standard recommended practice for program managers who need to ensure that their data supply chain produces data of sustainably reliable quality over time to ensure maximum protection of the public’s health. Therefore, we strongly recommend an immediate, synergistic alignment of the state-of-the-art validation acceptance-sampling methods and science currently used widely in industry with the needs and logistics of the tasks involved in collecting, sampling, evaluating, and reporting HAI data. We believe that the American public deserves to have its best technology and its “A Team” on the field in this effort and that involving the ASQ, its Healthcare Division, its quality scientists, and its intellectual capital resources could dramatically improve the HAI quality reporting system. Our people and our healthcare system deserve no less!

The commentator discusses in general the importance of simulation studies and how they can solve very difficult problems quickly.

While split-plot designs have received considerable attention in the literature over the past decade, there seems to be a general lack of intuitive understanding of the error structure of these des...

There has been considerable interest in the use of unreplicated designed experiments to identify both location and dispersion effects. This article takes a look at the two-step approach which incorporates a noniterative identification step followed by an iterative final estimation step. This two-step approach eliminates some of the bias present in the noniterative methods. Keywords: location-dispersion model; parameter design; quality improvement; restricted maximum likelihood (REML); robust design

The ASQ Statistics Division is pleased to announce that nominations are now open for its William G. Hunter Award for 2013.

The mechanism of prestrain embrittlement in wrought metals was examined in three alloy systems: Armco iron, 7075-T651 aluminum, and a copper- SiO2 alloy. Specimens were compressively prestrained, then tested in tension in and transverse to the direction of compression. Prestrain embrittlement was observed in all three alloys, depending on test conditions. In the copper-base alloy, which was in the form of thin sheet, the retained tensile ductility was reduced linearly by inplane compressive prestrain. Fracture was initiated by crystallographically-oriented voids generated at particle-matrix interfaces during compression. The behavior of Armco iron was similar in that particle- matrix void nucleation during compression provides fracture initiation sites that lead to brittle cleavage failure at room temperature during subsequent notch tensile testing. Prestrain had little influence on smooth bar tensile test results. Crystallographic texture development was shown to play a part in the embrittlement but tests under high pressure that suppressed void nucleation showed the role of texture to be secondary. Prestrain embrittlement in the aluminum alloy results from the interaction of intense shear bands, formed by sufficient compressive prestrain, with the grain boundaries to give a partially intergranular fracture.