Confidence Interval based Quality Improvement for Non-normal Responses

Year 2019, Volume: 15 Issue: 2, 199 - 204, 30.06.2019

Melis Zeybek

https://doi.org/10.18466/cbayarfbe.518736

Abstract

Robust parameter design is an effective tool to determine the optimal
operating conditions of a system. Because of its practicability and usefulness,
the widespread applications of robust design techniques provide major quality
improvements. The usual assumptions of robust parameter design are that
normally distributed experimental data and no contamination due to outliers. Optimizing
an objective function under the normality assumption for a skewed data in
dual-response modeling may result in misleading fit and operating conditions
located far from the optimal values. This creates a chain of degradation in the
production phase, e.g., poor quality products. This paper focuses on skewed
experimental data. The proposed approach is constructed on the confidence
interval of the process mean which makes the system median unbiased for the
mean using the skewness information of the data.  The response modeling of the midpoint of the
interval is proposed as a location performance response. The main advantages of
the proposed approach are that it gives a robust solution due to the skewed
structure of the experimental data distribution and does not need any
transformation which causes any loss of information in estimation of the mean
response. The procedure and the validity of the proposed approach are
illustrated on a popular example, the printing process study

Keywords

Confidence interval, Dual response surface, Non-normal data, Robust parameter design

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