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Methods for detection of small process shiftsJamnarnwej, Panisuan 05 1900 (has links)
No description available.
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Simulation for tests on the validity of the assumption that the underlying distribution of life is exponentialThoppil, Anjo January 2010 (has links)
Typescript (photocopy). / Digitized by Kansas Correctional Industries
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Assembly tolerance analysis in geometric dimensioning and tolerancingTangkoonsombati, Choowong 25 August 1994 (has links)
Tolerance analysis is a major link between design and
manufacturing. An assembly or a part should be designed
based on its functions, manufacturing processes, desired
product quality, and manufacturing cost. Assembly tolerance
analysis performed at the design stage can reduce potential
manufacturing and assembly problems. Several commonly used
assembly tolerance analysis models and their limitations are
reviewed in this research. Also, a new assembly tolerance
analysis model is developed to improve the limitations of the
existing assembly tolerance analysis models. The new model
elucidates the impact of the flatness symbol (one of the
Geometric Dimensioning and Tolerancing (GD&T) specification
symbols) and reduces design variables into simple
mathematical equations. The new model is based on beta
distribution of part dimensions. In addition, a group of
manufacturing variables, including quality factor, process
tolerance, and mean shift, is integrated in the new assembly
tolerance analysis model.
A computer integrated system has been developed to
handle four support systems for the performance of tolerance
analysis in a single computer application. These support
systems are: 1) the CAD drawing system, 2) the Geometric
Dimensioning and Tolerancing (GD&T) specification system, 3)
the assembly tolerance analysis model, and 4) the tolerance
database operating under the Windows environment. Dynamic
Data Exchange (DDE) is applied to exchange the data between
two different window applications, resulting in improvement
of information transfer between the support systems. In this
way, the user is able to use this integrated system to select
a GD&T specification, determine a critical assembly dimension
and tolerance, and access the tolerance database during the
design stage simultaneously. Examples are presented to
illustrate the application of the integrated tolerance
analysis system. / Graduation date: 1995
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Performance of quality control procedures when monitoring correlated processesBarr, Tina Jordan 05 1900 (has links)
No description available.
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On monitoring the attributes of a processMarcucci, Mark O. January 1982 (has links)
Two prominent monitoring procedures in statistical quality control are the p-chart for the proportion of items defective, and the c-chart, for the number of defects per item. These procedures are reconsidered, and some extensions are examined for monitoring processes with multiple attributes.
Some relevant distribution theory is reviewed, and some new results are given. The distributions considered are multivariate versions of the binomial, Poisson, and chi-squared distributions, plus univariate and multivariate generalized Poisson distributions. All of these distributions prove useful in the discussion of attribute control charts.
When quality standards are known, p-charts and c-charts are shown to have certain optimal properties. Generalized p-charts, for monitoring multinomial processes, and generalized c-charts are introduced. Their properties are shown to depend upon multivariate chi-squared and generalized Poisson distributions, respectively.
Various techniques are considered for monitoring multivariate Bernoulli, Poisson, multinomial, and generalized Poisson processes. Omnibus procedures are given, and some of their asymptotic properties are derived. Also examined are diagnostic procedures based upon both small- and large-sample. / Ph. D.
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A systematic, experimental methodology for design optimizationRitchie, Paul Andrew, 1960- January 1988 (has links)
Much attention has been directed at off-line quality control techniques in recent literature. This study is a refinement of and an enhancement to one technique, the Taguchi Method, for determining the optimum setting of design parameters in a product or process. In place of the signal-to-noise ratio, the mean square error (MSE) for each quality characteristic of interest is used. Polynomial models describing mean response and variance are fit to the observed data using statistical methods. The settings for the design parameters are determined by minimizing a statistical model. The model uses a multicriterion objective consisting of the MSE for each quality characteristic of interest. Minimum bias central composite designs are used during the data collection step to determine the settings of the parameters where observations are to be taken. Included is the development of minimum bias designs for various cases. A detailed example is given.
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A unified approach to the economic aspects of statistical quality control and improvementGhebretensae Manna, Zerai 12 1900 (has links)
Assignment (MSc)--Stellenbosch University, 2004. / ENGLISH ABSTRACT: The design of control charts refers to the selection of the parameters implied, including the
sample size n, control limit width parameter k, and the sampling interval h. The design of the
X -control chart that is based on economic as well as statistical considerations is presently one of
the more popular subjects of research. Two assumptions are considered in the development and
use of the economic or economic statistical models. These assumptions are potentially critical. It
is assumed that the time between process shifts can be modelled by means of the exponential
distribution. It is further assumed that there is only one assignable cause. Based on these
assumptions, economic or economic statistical models are derived using a total cost function per
unit time as proposed by a unified approach of the Lorenzen and Vance model (1986). In this
approach the relationship between the three control chart parameters as well as the three types of
costs are expressed in the total cost function. The optimal parameters are usually obtained by the
minimization of the expected total cost per unit time. Nevertheless, few practitioners have tried
to optimize the design of their X -control charts. One reason for this is that the cost models and
their associated optimization techniques are often too complex and difficult for practitioners to
understand and apply. However, a user-friendly Excel program has been developed in this paper
and the numerical examples illustrated are executed on this program. The optimization procedure
is easy-to-use, easy-to-understand, and easy-to-access. Moreover, the proposed procedure also
obtains exact optimal design values in contrast to the approximate designs developed by Duncan
(1956) and other subsequent researchers.
Numerical examples are presented of both the economic and the economic statistical designs of
the X -control chart in order to illustrate the working of the proposed Excel optimal procedure.
Based on the Excel optimization procedure, the results of the economic statistical design are
compared to those of a pure economic model. It is shown that the economic statistical designs
lead to wider control limits and smaller sampling intervals than the economic designs.
Furthermore, even if they are more costly than the economic design they do guarantee output of
better quality, while keeping the number of false alarm searches at a minimum. It also leads to
low process variability. These properties are the direct result of the requirement that the
economic statistical design must assure a satisfactory statistical performance.
Additionally, extensive sensitivity studies are performed on the economic and economic
statistical designs to investigate the effect of the input parameters and the effects of varying the bounds on, a, 1-f3 , the average time-to-signal, ATS as well as the expected shift size t5 on
the minimum expected cost loss as well as the three control chart decision variables. The
analyses show that cost is relatively insensitive to improvement in the type I and type II error
rates, but highly sensitive to changes in smaller bounds on ATS as well as extremely sensitive
for smaller shift levels, t5 .
Note: expressions like economic design, economic statistical design, loss cost and assignable
cause may seen linguistically and syntactically strange, but are borrowed from and used
according the known literature on the subject. / AFRIKAANSE OPSOMMING: Die ontwerp van kontrolekaarte verwys na die seleksie van die parameters geïmpliseer,
insluitende die steekproefgrootte n , kontrole limiete interval parameter k , en die
steekproefmterval h. Die ontwerp van die X -kontrolekaart, gebaseer op ekonomiese sowel as
statistiese oorwegings, is tans een van die meer populêre onderwerpe van navorsing. Twee
aannames word in ag geneem in die ontwikkeling en gebruik van die ekonomiese en ekonomies
statistiese modelle. Hierdie aannames is potensieel krities. Dit word aanvaar dat die tyd tussen
prosesverskuiwings deur die eksponensiaalverdeling gemodelleer kan word. Daar word ook
verder aangeneem dat daar slegs een oorsaak kan wees vir 'n verskuiwing, of te wel 'n
aanwysbare oorsaak (assignable cause). Gebaseer op hierdie aannames word ekonomies en
ekonomies statistiese modelle afgelei deur gebruik te maak van 'n totale kostefunksie per
tydseenheid soos voorgestel deur deur 'n verenigende (unified) benadering van die Lorenzen en
Vance-model (1986). In hierdie benadering word die verband tussen die drie kontrole
parameters sowel as die drie tipes koste in die totale kostefunksie uiteengesit. Die optimale
parameters word gewoonlik gevind deur die minirnering van die verwagte totale koste per
tydseenheid. Desnieteenstaande het slegs 'n minderheid van praktisyns tot nou toe probeer om
die ontwerp van hulle X -kontrolekaarte te optimeer. Een rede hiervoor is dat die kosternodelle
en hulle geassosieerde optimeringstegnieke té kompleks en moeilik is vir die praktisyns om te
verstaan en toe te pas. 'n Gebruikersvriendelike Excelprogram is egter hier ontwikkel en die
numeriese voorbeelde wat vir illustrasie doeleindes getoon word, is op hierdie program
uitgevoer. Die optimeringsprosedure is maklik om te gebruik, maklik om te verstaan en die
sagteware is geredelik beskikbaar. Wat meer is, is dat die voorgestelde prosedure eksakte
optimale ontwerp waardes bereken in teenstelling tot die benaderde ontwerpe van Duncan (1956)
en navorsers na hom.
Numeriese voorbeelde word verskaf van beide die ekonomiese en ekonomies statistiese
ontwerpe vir die X -kontrolekaart om die werking van die voorgestelde Excel optimale
prosedure te illustreer. Die resultate van die ekonomies statistiese ontwerp word vergelyk met
dié van die suiwer ekomomiese model met behulp van die Excel optimerings-prosedure. Daar
word aangetoon dat die ekonomiese statistiese ontwerpe tot wyer kontrole limiete en kleiner
steekproefmtervalle lei as die ekonomiese ontwerpe. Al lei die ekonomies statistiese ontwerp tot
ietwat hoër koste as die ekonomiese ontwerpe se oplossings, waarborg dit beter kwaliteit terwyl
dit die aantal vals seine tot 'n minimum beperk. Hierbenewens lei dit ook tot kleiner prosesvartasie. Hierdie eienskappe is die direkte resultaat van die vereiste dat die ekonomies
statistiese ontwerp aan sekere statistiese vereistes moet voldoen.
Verder is uitgebreide sensitiwiteitsondersoeke op die ekonomies en ekonomies statistiese
ontwerpe gedoen om die effek van die inset parameters sowel as van variërende grense op a,
1- f3 , die gemiddelde tyd-tot-sein, ATS sowel as die verskuiwingsgrootte 8 op die minimum
verwagte kosteverlies sowel as die drie kontrolekaart besluitnemingsveranderlikes te bepaal. Die
analises toon dat die totale koste relatief onsensitief is tot verbeterings in die tipe I en die tipe II
fout koerse, maar dat dit hoogs sensitief is vir wysigings in die onderste grens op ATS sowel as
besonder sensitief vir klein verskuiwingsvlakke, 8.
Let op: Die uitdrukkings ekonomiese ontwerp (economic design), ekonomies statistiese ontwerp
(economic statistical design), verlies kostefunksie (loss cost function) en aanwysbare oorsaak
(assignable cause) mag taalkundig en sintakties vreemd voordoen, maar is geleen uit, en word so
gebruik in die bekende literatuur oor hierdie onderwerp.
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Multivariate control charts for nonconformitiesChattinnawat, Wichai 05 September 2003 (has links)
When the nonconformities are independent, a multivariate control chart for
nonconformities called a demerit control chart using a distribution approximation
technique called an Edgeworth Expansion, is proposed. For a demerit control chart,
an exact control limit can be obtained in special cases, but not in general. A proposed
demerit control chart uses an Edgeworth Expansion to approximate the distribution of
the demerit statistic and to compute the demerit control limits. A simulation study
shows that the proposed method yields reasonably accurate results in determining the
distribution of the demerit statistic and hence the control limits, even for small sample
sizes. The simulation also shows that the performances of the demerit control chart
constructed using the proposed method is very close to the advertised for all sample sizes.
Since the demerit control chart statistic is a weighted sum of the
nonconformities, naturally the performance of the demerit control chart will depend on
the weights assigned to the nonconformities. The method of how to select weights
that give the best performance for the demerit control chart has not yet been addressed
in the literature. A methodology is proposed to select the weights for a one-sided
demerit control chart with and upper control limit using an asymptotic technique. The
asymptotic technique does not restrict the nature of the types and classification scheme
for the nonconformities and provides an optimal and explicit solution for the weights.
In the case presented so far, we assumed that the nonconformities are
independent. When the nonconformities are correlated, a multivariate Poisson
lognormal probability distribution is used to model the nonconformities. This
distribution is able to model both positive and negative correlations among the
nonconformities. A different type of multivariate control chart for correlated
nonconformities is proposed. The proposed control chart can be applied to
nonconformities that have any multivariate distributions whether they be discrete or
continuous or something that has characteristics of both, e.g., non-Poisson correlated
random variables. The proposed method evaluates the deviation of the observed
sample means from pre-defined targets in terms of the density function value of the
sample means. The distribution of the control chart test statistic is derived using an
approximation technique called a multivariate Edgeworth expansion. For small
sample sizes, results show that the proposed control chart is robust to inaccuracies in
assumptions about the distribution of the correlated nonconformities. / Graduation date: 2004
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Data-driven approach for control performance monitoring and fault diagnosisYu, Jie 28 August 2008 (has links)
Not available / text
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Data-driven approach for control performance monitoring and fault diagnosisYu, Jie, 1977- 23 August 2011 (has links)
Not available / text
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