Dimensional SPC for low-volume production /

The topic of this study is approaches for implementing Statistical Process Control (SPC) in a low-volume manufacturing environment which has difficulty in applying conventional SPC techniques centered around high-volume, repetitive processes. To make up for the weaknesses of SPC in low-volume produc...

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Bibliographic Details
Main Author: Chin, Chang-Ho
Format: Thesis eBook
Language:English
Published: [Place of publication not identified] : [publisher not identified] ; 1999.
Subjects:
Online Access:Link to OAKTrust copy

MARC

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099 |a 1999  |a Thesis  |a C457 
100 1 |a Chin, Chang-Ho. 
245 1 0 |a Dimensional SPC for low-volume production /  |c by Chang-Ho Chin. 
246 3 |a Dimensional Statistical Process Control for low volume production 
264 1 |a [Place of publication not identified] :  |b [publisher not identified] ;  |c 1999. 
300 |a xi, 75 leaves :  |b illustrations ;  |c 28 cm. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
500 |a "Major subject: Industrial Engineering". 
500 |a Vita. 
502 |b M.S.  |c Texas A&M University  |d 1999. 
504 |a Includes bibliographical references (leaves 65-68). 
520 |a The topic of this study is approaches for implementing Statistical Process Control (SPC) in a low-volume manufacturing environment which has difficulty in applying conventional SPC techniques centered around high-volume, repetitive processes. To make up for the weaknesses of SPC in low-volume production, a new method to test data independence, a new plot to describe the similarity between two charts, and two modified graphical tools are proposed. In low-volume production, it is frequently hard to analyze processes due to the lack of data. This study focuses on an environment in which there are not enough time-related data to apply conventional SPC techniques, but a large number of data from the same tyke of processes in a part or product are available for monitoring and diagnosis. The proposed test for data independence is useful in terms of the descriptive statistics analysis. The similarity plot and graphical tools are designed to make up for these difficulties of low-volume production by visual representation methodology. The graphical tools are color-coded for easy recognition and focus on presenting the trend and distribution of defect occurrence within the same part or product. 
530 |a Also available online. 
530 |a Issued also on microfiche from Lange Micrographics. 
650 4 |a Major industrial engineering. 
856 4 1 |u https://hdl.handle.net/1969.1/ETD-TAMU-1999-THESIS-C457  |z Link to OAKTrust copy  |t 0 
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