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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
computer science
A combined synthetic and np scheme for detecting increases in fraction nonconforming
Computers and Industrial Engineering, Volume 62, No. 4, Year 2012
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Description
The applications of attribute control charts cover a wide variety of manufacturing processes in which quality characteristics cannot be measured on a continuous numerical scale or even a quantitative scale. The np control chart is an attribute chart used to monitor the fraction nonconforming p of a process. This chart is effective for detecting large process shifts in p. The attribute synthetic chart is also proposed to detect p shifts. It utilizes the information about the time interval or the Conforming Run Length (CRL) between two nonconforming samples. During the implementation of a synthetic chart, a sample is classified as nonconforming if the number d of nonconforming units falls beyond a warning limit. Unlike the np chart, the synthetic chart is more powerful to detect small and moderate p shifts. This article proposes a new scheme, the Syn-np chart, which comprises a synthetic chart and an np chart. Since the Syn-np chart has both the strength of the synthetic chart for quickly detecting small p shifts and the advantage of the np chart of being sensitive to large p shifts, it has a better and more uniform overall performance. Specifically, it is more effective than the np chart and synthetic chart by 73% and 31%, respectively, in terms of Weighted Average of Average Time to Signal (WAATS) over a wide range of p shifts under different conditions. © 2011 Elsevier Ltd. All rights reserved.
Authors & Co-Authors
Haridy, Salah
Singapore, Singapore City
Nanyang Technological University
Wu, Zhang
Singapore, Singapore City
Nanyang Technological University
Khoo, M. B.C.
Malaysia, Minden
Universiti Sains Malaysia
Yu, Fongjung
Taiwan, Dacun
Da-yeh University
Statistics
Citations: 41
Authors: 4
Affiliations: 3
Identifiers
Doi:
10.1016/j.cie.2011.12.024
ISSN:
03608352
Study Approach
Quantitative