利用投影到潜在结构模型建立来料的多变量规格区域:直接映射和模型反演的比较

Adéline Paris, C. Duchesne, É. Poulin
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引用次数: 5

摘要

增加原材料的可变性对许多行业来说都是一项挑战,因为它会对最终产品质量产生不利影响。建立用于选择来料批次的多变量规格区域是缓解这一问题的关键解决方案。文献中出现了两种数据驱动的方法,用于在投影到潜在结构(PLS)模型的潜在空间中定义这些规范。第一种是基于对潜在空间中优质最终产品和相关大量原材料的直接映射,然后选择最小化或最佳平衡I型和II型误差的边界。第二种方法通过反转位于最终产品验收极限上的每个点的PLS模型来定义规范区域。本文的目的是比较这两种方法,以确定它们的优缺点,并在质量属性之间存在不同程度相关性的情况下评估它们的分类性能。使用模拟原材料和在产品质量属性具有不同程度共线的多个场景下生成的产品质量数据进行比较分析。首先,提出了一个简单的例子,使用一个质量属性来说明方法。然后,研究了共线的影响。研究表明,在大多数情况下,质量变量之间的相关性似乎不会影响分类性能,除非变量之间具有高度相关性。对这两种方法的主要优点和缺点进行了总结,以指导为给定应用程序选择最合适的方法来建立多元规范区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Establishing Multivariate Specification Regions for Incoming Raw Materials Using Projection to Latent Structure Models: Comparison Between Direct Mapping and Model Inversion
Increasing raw material variability is challenging for many industries since it adversely impacts final product quality. Establishing multivariate specification regions for selecting incoming lot of raw materials is a key solution to mitigate this issue. Two data-driven approaches emerge from the literature for defining these specifications in the latent space of Projection to Latent Structure (PLS) models. The first is based on a direct mapping of good quality final product and associated lots of raw materials in the latent space, followed by selection of boundaries that minimize or best balance type I and II errors. The second rather defines specification regions by inverting the PLS model for each point lying on final product acceptance limits. The objective of this paper is to compare both methods to determine their advantages and drawbacks, and to assess their classification performance in presence of different levels of correlation between the quality attributes. The comparative analysis is performed using simulated raw materials and product quality data generated under multiple scenarios where product quality attributes have different degrees of collinearity. First, a simple case is proposed using one quality attribute to illustrate the methods. Then, the impact of collinearity is studied. It is shown that in most cases, correlation between the quality variable does not seem to influence classification performance except when the variables are highly correlated. A summary of the main advantages and disadvantages of both approaches is provided to guide the selection of the most appropriate approach for establishing multivariate specification regions for a given application.
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