非均衡失效成本条件下的统计过程调整技术研究

Statistical process adjustment technique on condition of asymmetric off-target cost

为解决统计过程调整问题,建立了统计过程调整问题模型,采用公差设计和参数设计中的非均衡失效成本作为过程调整的评价指标,构建了过程方差参数未知和已知条件下的过程参数估计贝叶斯模型,进而构建了相应的质量特性值的预测模型。根据非均衡失效成本函数和质量特性值的预测模型,确定了过程每一步的最优控制目标,并根据控制目标完成过程调整。最后,在非均衡失效成本条件下,通过与格鲁布斯基本准则调整方法和马尔可夫链蒙特卡罗调整方法的对比,验证了本文中调整方法的性能优势。

After deeply studying the problem of Statistical Process Adjustment （SPA）, a process adjustment model was set up. Asymmetric off-target cost was used as the performance evaluation index of adjustment methods which was used in tolerance design and parameters design but not in SPA yet. Different Bayesian models were constructed with known and unknown information in the process variance, and then the prediction model of the quality characteristica was constructed. According to the Bayesian models and prediction model, the optimal control targets for each step were defined and the target adjustments were also performed. Finally, by comparing with Grubbs basic rule adjustment approach and Markov Chain Monte Carlo （MCMC） adiustment approach on condition of asymmetric offtarget cost, the performance advantages of the proposed approach were confirmed.