了解混合成分和工艺变量之间的相互作用

IF 1.3 4区 工程技术 Q4 ENGINEERING, INDUSTRIAL
R. Snee, R. Hoerl
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引用次数: 0

摘要

自scheff的工作以来,对存在过程变量的混合组分效应的研究一直引起人们的兴趣。一般来说,设计实验的一个关键优势是能够估计和解释相互作用。混合工艺实验的一个独特特征是混合组分和工艺变量之间可能存在相互作用。解释这些问题的经典方法是使用等高线图并评估舍夫勒混合过程模型中的个体相互作用系数。研究沿Cox分量轴的相互作用,大大提高了从等高线图中获得的对这些相互作用性质的认识。此外,我们提出了一种替代分析,产生混合过程模型中过程变量主效应的估计。给出了图解法和解析法。这种方法提供了主要影响和相互作用的总体视图,这与如何在仅使用过程变量的析因和响应面实验中评估这些术语是一致的。识别并讨论了经典方法的局限性。本文包括三个示例来说明这种方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding interactions between mixture components and process variables
Abstract The study of mixture component effects in the presence of process variables has been of interest since the work of Scheffé. A key advantage of designed experiments in general is the ability to estimate and interpret interactions. A unique feature of mixture-process experiments is the potential presence of interactions between the mixture components and the process variables. The classic approach to interpret these has been to use contour plots and evaluate individual interaction coefficients in Scheffé mixture-process models. It is proposed to study the interactions along the Cox component axes, which greatly enhances the insight into the nature of these interactions that can be obtained from contour plots. Further, we propose an alternative analysis that produces estimates of the process variable main effects in mixture-process models. Both graphical and analytical methods are presented. This approach provides an overall view of the main effects and interactions that is consistent with how these terms are evaluated in factorial and response surface experiments with only process variables. Limitations of the classic approach are identified and discussed. Three examples are included to illustrate the approach.
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来源期刊
Quality Engineering
Quality Engineering ENGINEERING, INDUSTRIAL-STATISTICS & PROBABILITY
CiteScore
3.90
自引率
10.00%
发文量
52
审稿时长
>12 weeks
期刊介绍: Quality Engineering aims to promote a rich exchange among the quality engineering community by publishing papers that describe new engineering methods ready for immediate industrial application or examples of techniques uniquely employed. You are invited to submit manuscripts and application experiences that explore: Experimental engineering design and analysis Measurement system analysis in engineering Engineering process modelling Product and process optimization in engineering Quality control and process monitoring in engineering Engineering regression Reliability in engineering Response surface methodology in engineering Robust engineering parameter design Six Sigma method enhancement in engineering Statistical engineering Engineering test and evaluation techniques.
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