Search

Due to the nature of the quality characteristics, or there is no instrument available to measure the characteristics of interest, ordered data, e.g. 1 (poor), 2 (satisfactory), 3 (good), and 4 (excellent), often appears in industrial processes. Methods commonly used for continuous or categorical quality characteristics are not appropriate for modelling and optimising such quality characteristics. This motivated us to develop a more useful approach to address the variable selection, model construction, and process optimisation for the ordered response. Specifically, Bayesian Lasso is incorporated into the framework of the response surface model to simultaneously perform variable selection and model estimation. The relationship between the probability of the response falls into a specific category and significant factor effects are established by introducing a latent variable. The desirability function, which is commonly used for multi-objective optimisation for quantitative responses, is extended to process optimisation for the ordered response. A numerical example and an industrial case are used to validate the effectiveness of the proposed method. The performance studies of the proposed method show that our method is more competitive than existing methods. [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]