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The process capability index (PCI), C pk , one of the widely used tools for assessing the capability of a manufacturing process, expresses the deviation of the process mean from the midpoint of the specification limits. The C pk is known to perform well under the general assumption that the experimental data are normally distributed without contamination. Under this assumption, the sample mean and sample standard deviation are used for the estimation of the PCI. However, the sample mean and sample standard deviation are quite sensitive to data contamination and this will result in underperformance of C pk . Therefore, in this article, we propose alternatives to the conventional method by replacing the sample mean and sample standard deviation with robust location and scale estimators. We also propose a method for constructing a robust PCI C pk confidence interval which lends itself to robust statistical hypothesis testing. The robust hypothesis testing methods based on this confidence interval are shown to be quite efficient when the data are normally distributed yet also outperform the conventional method when data contamination exists. [ABSTRACT FROM AUTHOR]

Integrated parameter design and tolerance design (IPTD) is an effective way to improve product quality and reduce manufacturing cost in micro-manufacturing processes. However, the current modeling techniques rarely analyze the influence of model uncertainty on the optimal machining parameters. It may not obtain the robust optimal machining parameters due to model uncertainty. This paper proposes a novel economically integrated design method which considers the correlations among quality characteristics, the variability of manufacturing process, and uncertainty in the model predictions. First, a new rework and scrap cost functions are established via Monte Carlo simulation. Meanwhile, an integrative expected quality loss function is constructed based on interval analysis theory for quantifying model uncertainty. Second, to make the proposed method closer to the practical micro-manufacturing problem, we consider the trade-offs among cost, time, and success rate in the modeling process. Finally, a total cost model is proposed to take into account the quality loss, tolerance cost, unit manufacturing cost, and scrap cost. The effectiveness of the proposed modeling method is verified by a laser beam micro-drilling manufacturing. The results illustrate that the proposed method can achieve better robustness property and economically than the traditional method that does not consider model uncertainty. [ABSTRACT FROM AUTHOR]

Various creative multi-response optimisation approaches have been developed in the literature. Most of these researches are based on the normality assumption of the response distribution. However, this assumption does not necessarily hold in some real cases, such as non-normal multiple responses. Also, the reproducibility of optimisation results does not hold in some practical applications due to the variability of predicted responses associated with model uncertainty. In this paper, a novel approach is proposed to address the issues for non-normal multi-response optimisation. The proposed method not only identifies significant effects for each response by incorporating factorial effect principles into the framework of the Bayesian generalised linear models (GLMs) but also takes into account the model uncertainty and the variability of predicted responses by using the Bayesian sampling technique and Pareto optimal strategy. Furthermore, the optimal parameter settings are found by using grey incidence analysis (GIA). Besides, two examples are used to illustrate the effectiveness of the proposed method. The results show that the proposed method not only effectively identify significant factors but also find more satisfactory parameter settings when the reliability and reproducibility of optimisation results are considered simultaneously. [ABSTRACT FROM AUTHOR]

Many industrial process optimisation methods rely on empirical models that relate output responses to a set of design variables. One of the most crucial problems in process optimisation is how to efficiently implement model selection and model estimation. This paper presents a Bayesian hierarchical modelling approach to process optimisation based on the seemingly unrelated regression (SUR) models. This approach can estimate a set of predictors to be included in a model based on a Bayesian hierarchical procedure (i.e. model selection) and then give model prediction based on a Bayesian SUR model (i.e. model estimation). Meanwhile, a two-stage optimisation strategy considering practitioners' preference information is proposed in process optimisation, which initially finds a set of non-dominated input settings and then determines the best one based on the similarity to an ideal solution method. The performance and effectiveness of the proposed method are illustrated with both simulation studies and a case study. The comparison results demonstrate that the proposed method can be a good alternative to existing process optimisation methods. [ABSTRACT FROM AUTHOR]

As a significant analytical tool in reliability management, FMEA has been extensively used in various fields. Nevertheless, conventional FMEA has been criticized for some defects. To compensate this situation, this article proposes an improved FMEA method under the environment of probabilistic linguistic terms. The multiformity and indeterminacy of experts' assessment information is depicted by applying probabilistic linguistic term sets, and then evaluation information is fused based on information quality and Dempster-Shafer evidence theory. The different action priority is adopted to determine the sequence of failure modes. Finally, a case study is presented to verify the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Metamodels are widely used as fast surrogates to facilitate the optimization of simulation models. Stochastic kriging (SK) is an effective metamodeling tool for a mean response surface implied by stochastic simulation. In SK, it is usually assumed that the experimental data are normally distributed and uncontaminated. However, these assumptions can be easily violated in many practical applications. This paper proposes a new type of SK for simulation models that may have non-Gaussian responses; this new SK uses robust estimators of location (or central tendency) and scale (or variability) that are well-known in the literature on robust statistics. Statistical properties of the robust estimators used in this paper are briefly analyzed and the performances of the proposed methods are compared through numerical examples of different features. The comparison results show that the proposed robust SK with the robust estimators is quite efficient, no matter whether the standard assumptions hold or not. [ABSTRACT FROM AUTHOR]