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A novel approach for measuring the maximum process-loss information in multiple production line conditions

机译：一种用于测量多条生产线条件下最大工艺损失信息的新颖方法

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摘要

The process-loss index L_e, the expected value of the ratio of the quadratic loss function to the square value of half specification width, proposed by Johnson (1992, The relationship of C_(pm) to squared error loss. Journal of Quality Technology, 24 (4), 211-215), has been widely used in a variety of industries to provide a numerical loss measure for assessing the performance of their production processes. However, the sampling distribution of its uniformly minimum variance unbiased estimator (UMVUE), obtained from traditional approaches involving unknown parameters, is able neither to form classical confidence intervals (CCIs) nor to provide justifiable process-loss information in practice. To tackle this disadvantage, in this paper a novel approach known as generalised confidence intervals (GCIs) is adopted. Instead of Monte Carlo simulations that were popularly utilised in implementing the GCIs method for assessing production process performance, we theoretically derive analytical forms of upper confidence bounds (L_e-GUCB) for L_e and program them to provide the maximum process-loss information for the manufacturing processes. Two common manufacturing scenarios are presented in order to work out: (1) whether the underlying production process loss is capable (or whether products received from one supplier are acceptable); and (2) whether the maximum process-loss information existing in multiple production line conditions is acceptable (or whether products received from several suppliers are acceptable). The applicability of the results is demonstrated by two examples.
机译：Johnson（1992，C_（pm）与平方误差损失的关系）提出了过程损失指数L_e，即二次损失函数与规格一半宽度平方的比值的期望值。 24（4），211-215），已广泛用于各种行业，以提供用于评估其生产过程性能的数值损失度量。然而，从涉及未知参数的传统方法获得的其均匀最小方差无偏估计量（UMVUE）的采样分布既不能形成经典的置信区间（CCI），也不能在实践中提供合理的过程损失信息。为了解决这个缺点，本文采用一种称为广义置信区间（GCI）的新方法。我们从理论上推导了L_e的上限置信界限（L_e-GUCB）的分析形式，并对它们进行编程以提供最大的制造过程损失信息，而不是在实施GCIs方法来评估生产过程性能时普遍使用的蒙特卡洛模拟流程。为了解决该问题，提出了两种常见的制造方案：（1）基本的生产过程损失是否能够承受（或者从一个供应商处收到的产品是否可以接受）； （2）在多条生产线条件下存在的最大工艺损失信息是否可以接受（或者从几个供应商处收到的产品是否可以接受）。结果的适用性通过两个例子证明。

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