Browsing by Author "Hung, H. N."

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An effective powerful test for one-sided supplier selection problem
(2011)
Pearn, W. L.
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Hung, H. N.
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Chuang, Y. S.
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Su, R. H.
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統計學研究所
In this paper, we consider the supplier selection problem, which deals with comparing two one-sided processes and selecting a better one that has a higher capability. We first review two existing approximation approaches, and an exact approach proposed which we refer to as the division method. We then develop a new exact approach called the subtraction method. We compare the two exact methods on the selection power. The results show that the proposed subtraction method is indeed more powerful than the division method. A two-phase selecting procedure is then developed based on the subtraction method for practical applications. Some computational results are tabulated for practitioners' convenience.
An Extension of the Product Acceptance Determination for One-Sided Process with Multiple Characteristics
(2013-03-01)
Pearn, W. L.
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Wu, C. H.
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Hung, H. N.
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Kao, C. M.
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統計學研究所
In statistical quality control, product acceptance determination is an important problem for producer and consumer. In practical, particularly more than one quality characteristic must be simultaneously considered to improve the product quality because of the product design. In this article, we investigate the lot sentencing problem for normally distributed process with one-sided specification and multiple characteristics. We not only provide a simple procedure to help practitioners make reliable decision easily but also tabulate the required sample size and the corresponding critical acceptance value for various producer's and consumer's risks with the capability requirements AQL (acceptable quality level) and LTPD (lot tolerance percent defective). Copyright (c) 2012 John Wiley & Sons, Ltd.
Process Capability Evaluation for Square Bumps with Mean Shift
(2011-09-01)
Pearn, W. L.
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Hung, H. N.
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Tai, Y. T.
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Hou, H. H.
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統計學研究所
Process capability indices have been widely used in manufacturing industries to provide numerical measures of process potential and performance. In in-plant applications, some inevitable process mean shift may be undetected when the statistical process control charts are applied, particularly, in the bumping process for square bumps. Bothe in 2002 provided a statistical reason for considering such a shift in the process mean to accommodate the undetected mean shifts for normally distributed processes when evaluating process capability. In this paper, we consider a bumping process for square bumps in which the data can be formulated as a non-central chi-square distribution, a class of non-normal distributions. To evaluate the popular yield-based process capability C(pk) more accurately, we accommodate the magnitudes of undetected mean shifts using the modified capability evaluation formula and tabulate the detection powers under various subgroup sizes and non-central chi-square parameters. Based on the modified process capability evaluation, we can provide a more reliable process capability evaluation of capability index C(pk) for square bumps and make more correct decisions. For illustration purpose, a real application in a bumping factory which is located on the Science-based Industrial Park in Hsinchu, Taiwan, is presented.
Testing process precision for truncated normal distributions
(2007-12-01)
Pearn, W. L.
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Hung, H. N.
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Peng, N. F.
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Huang, C. Y.
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統計學研究所
Process precision index C, has been widely used in the manufacturing industry to provide numerical measures of process precision, which essentially reflects product quality consistency. Precision measures using C, for normal processes, contaminated normal processes, have been investigated extensively, but are neglected for truncated normal processes. Truncated normal processes are common in the manufacturing industry, particularly, for factories equipped with automatic machines handling fully inspections, and scrap/rework products falling outside the specification limits. If the processes follow the normal distribution, then the inspected products (processes) must follow the truncated normal distribution. In this note, we consider the precision measure for truncated normal processes. We investigate the analytically intractable sampling distribution of the estimated C, and obtain a rather accurate approximation. Using the results, we develop a practical testing procedure for practitioners to use in their in-plant applications. (C) 2007 Elsevier Ltd. All rights reserved.