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Computer experiments often have inputs that are proportions/fractions of components in a mixture. In these mixture computer experiments, it can be of interest to perform robust and tolerance design on the mixture proportions since the proportions are subjected to noise variations. Traditionally, manufacturing of mixture products is controlled via interval tolerances for mixture amounts. In this paper, an optimal tolerance region for proportions, which gives optimal quality cost among all possible tolerance regions for mixture proportions with the same acceptance probability, is proposed for integrated parameter and tolerance design in mixture computer experiments. Real examples are given to demonstrate the improvements that can be achieved with the optimal tolerance region.

Failure mode and effects analysis (FMEA) is an effective risk assessment tool for detecting and reducing possible risks during a manufacturing process. However, traditional FMEA has some shortcomings when used in the real world. In recent years, improved FMEA approaches have been proposed to eliminate the inherent shortcomings of FMEA, but the risk ranking result obtained from those FMEA approaches may be inconsistent. Therefore, this paper integrates six FMEA approaches by using an ensemble learning technique to obtain comprehensive and reliable rankings for failure modes. Data from the assembly process of spark plugs are used to check the performance of the proposed method. Meanwhile, a comparation is designed to illustrate that the proposed FMEA method can not only obtain reliable results, but also provide meaningful management insights for practitioners.

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Transportation systems need more accurate predictions to further optimize traffic network design with the development and application of autonomous driving technology. In this article, we focus on highway traffic flow systems that are often simulated by the modified Greenshields model. However, this model can not perfectly match the true traffic flow due to its underlying simplifications and assumptions, implying that it is inexact. Specifically, some parameters affect the simulation accuracy of the modified Greenshields model, while tuning these parameters to improve the model’s accuracy is called model calibration. The parameters obtained using the L2 calibration have the advantages of high accuracy and small variance for an inexact model. However, the method is calculation intensive, requiring optimization of the integral loss function. Since traffic flow data are often massive, this paper proposes a fast L2 calibration framework to calibrate the modified Greenshields model. Specifically, the suggested method selects a sub-design containing more information on the calibration parameters, and then the empirical loss function obtained from the optimal sub-design is utilized to approximate the integral loss function. A case study highlights that the proposed method preserves the advantages of L2 calibration and significantly reduces the running time.

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