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Contributions to multivariate process capability indices
University West, Department of Engineering Science, Division of Natural Sciences and Electrical and Surveying Engineering.
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Luelå, 2012. , p. 36
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Mathematics
Research subject
ENGINEERING, Manufacturing and materials engineering
Identifiers
URN: urn:nbn:se:hv:diva-4456ISBN: 9789174393989 (print)OAI: oai:DiVA.org:hv-4456DiVA, id: diva2:537206
Available from: 2012-06-28 Created: 2012-06-26 Last updated: 2012-06-28Bibliographically approved
List of papers
1. Relationships between Coating Microstructure and Thermal Conductivity in Thermal Barrier Coatings – A modelling Approach
Open this publication in new window or tab >>Relationships between Coating Microstructure and Thermal Conductivity in Thermal Barrier Coatings – A modelling Approach
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2010 (English)In: International Thermal Spray Conference and Exposition, ITCS Singapore 2010: 3-5 May 2010,  Singapore, Düsseldorft: DVS Media , 2010, p. 66-72Conference paper, Published paper (Refereed)
Abstract [en]

Fundamental understanding of relationships between coating microstructure and thermal conductivity is important to be able to understand the influence of coating defects, such as delaminations and pores, on heat insulation in thermal barrier coatings. Object-Oriented Finite element analysis (OOF) has recently been shown as an effective tool for evaluating thermo-mechanical material behaviour, because of this method's capability to incorporate the inherent material microstructure as an input to the model. In this work, this method was combined with multi-variate statistical modelling. The statistical model was used for screening and tentative relationship building and the finite element model was thereafter used for verification of the statistical modelling results. Characterisation of the coatings included microstructure, porosity and crack content and thermal conductivity measurements. A range of coating architectures was investigated including High purity Yttria stabilised Zirconia, Dysprosia stabilised Zirconia and Dysprosia stabilised Zirconia with porosity former. Evaluation of the thermal conductivity was conducted using the Laser Flash Technique. The microstructures were examined both on as-sprayed samples as well as on heat treated samples. The feasibility of the combined two modelling approaches, including their capability to establish relationships between coating microstructure and thermal conductivity, is discussed.

Place, publisher, year, edition, pages
Düsseldorft: DVS Media, 2010
Series
DVS-Reports ; Volume 264
National Category
Other Engineering and Technologies not elsewhere specified Manufacturing, Surface and Joining Technology
Research subject
ENGINEERING, Manufacturing and materials engineering
Identifiers
urn:nbn:se:hv:diva-3076 (URN)978-3-87155-590-9 (ISBN)
Conference
Thermal Spray 2010: Global Solutions for Future Applications
Available from: 2011-01-26 Created: 2011-01-26 Last updated: 2016-08-16Bibliographically approved
2. Comparing Confidence Intervals for Multivariate Process capability Indices
Open this publication in new window or tab >>Comparing Confidence Intervals for Multivariate Process capability Indices
2012 (English)In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 28, no 4, p. 481-495Article in journal (Refereed) Published
Abstract [en]

Multivariate process capability indices (MPCIs) are needed for process capability analysis when the quality of a process is determined by several univariate quality characteristics that are correlated. There are several different MPCIs described in the literature, but confidence intervals have been derived for only a handful of these. In practice, the conclusion about process capability must be drawn from a random sample. Hence, confidence intervals or tests for MPCIs are important. With a case study as a start and under the assumption of multivariate normality, we review and compare four different available methods for calculating confidence intervals of MPCIs that generalize the univariate index Cp. Two of the methods are based on the ratio of a tolerance region to a process region, and two are based on the principal component analysis. For two of the methods, we derive approximate confidence intervals, which are easy to calculate and can be used for moderate sample sizes. We discuss issues that need to be solved before the studied methods can be applied more generally in practice. For instance, three of the methods have approximate confidence levels only, but no investigation has been carried out on how good these approximations are. Furthermore, we highlight the problem with the correspondence between the index value and the probability of nonconformance. We also elucidate a major drawback with the existing MPCIs on the basis of the principal component analysis. Our investigation shows the need for more research to obtain an MPCI with confidence interval such that conclusions about the process capability can be drawn at a known confidence level and that a stated value of the MPCI limits the probability of nonconformance in a known way. 

Place, publisher, year, edition, pages
Wiley online library, 2012
Keywords
multivariate process capability index lower confidence bound multivariate normal distribution
National Category
Probability Theory and Statistics
Research subject
ENGINEERING, Mathematics; Production Technology
Identifiers
urn:nbn:se:hv:diva-3775 (URN)10.1002/qre.1250 (DOI)000304152600011 ()
Available from: 2011-10-12 Created: 2011-10-12 Last updated: 2018-04-10Bibliographically approved
3. A multivariate process capability index based on the first principal component only
Open this publication in new window or tab >>A multivariate process capability index based on the first principal component only
2013 (English)In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 29, no 7, p. 987-1003Article in journal (Refereed) Published
Keywords
multivariate process capability index, multivariate normal distribution, transformed variable, principal component analysis, significance lever, power, Work-integrated Learning, WIL, AIL
National Category
Mathematical Analysis
Research subject
ENGINEERING, Manufacturing and materials engineering; PROFILE AREAS, Work Integrated Learning
Identifiers
urn:nbn:se:hv:diva-4455 (URN)10.1002/qre.1451 (DOI)000333581600005 ()2-s2.0-84887118097 (Scopus ID)
Note

Epub ahead of print

Available from: 2012-06-26 Created: 2012-06-26 Last updated: 2017-12-07Bibliographically approved

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