Online robust parameter design considering observable noise factors
Online robust parameter design considering observable noise factors
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DOI：
10.1080/0305215x.2020.1770744
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发表时间：
2020-06
影响因子：
2.7
通讯作者：
Shijuan Yang;Jianjun Wang;Yan Ma
中科院分区：
文献类型：
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作者：
Shijuan Yang;Jianjun Wang;Yan Ma
变异是产品质量问题的根本原因。噪声因素引起的变化往往会影响产品在制造过程中的质量，导致产品不能满足客户的要求。针对噪声因素可观测的参数设计问题，提出了一种贝叶斯在线鲁棒参数设计方法。该方法不仅利用时间序列模型考虑了噪声因素的在线观测，而且利用响应面模型和期望损失函数，在真实的时间内调整控制因素的最优设置，以减小噪声因素变化对产品质量的影响。数值算例和算例分析表明了该方法的优越性。结果表明，该方法比现有方法能给出更合理的结果。
Variation is the root cause of product quality problems. Variations caused by noise factors often affect the quality of the product during manufacturing, resulting in products that fail to meet customer requirements. In this article, a Bayesian online robust parameter design method is proposed to address the parameter design problem with observable noise factors. The proposed method not only takes into account the online observations of noise factors through using a time-series model, but also adjusts the optimal settings of the control factors in real time to reduce the impact of the variations from noise factors on product quality by using a response surface model and an expected loss function. The advantages of the proposed method are illustrated by a numerical example and a case study. The results show that the proposed method can give more reasonable results than the existing methods.