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Statistical Analysis for High-Temperature Alloy Design: Integration of Sparse PCA and Zero-One Inflated Beta Regression
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
2024 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis investigates the impact of various elements on the mechanical properties of

alloys at high temperature, with a specific focus on hot ductility as measured by the

Reduction in Area. Using a methods of K-means clustering, data points are separated

into distinct clusters. Afterwards, combination of Sparse Principal Component Analysis,

and Zero-One Inflated Beta Regression are applied to analyze the relationships between

hot ductility measured by reduction in area and alloy compositions across different

clusters. Our results reveal that aluminum and tin generally showed a negative impact

on reduction in area. Cu is found to have a minor effect on reduction in area at high

temperature compared to it have at low temperature.

Abstract [sv]

Detta examensarbete undersöker inverkan av olika element på de mekaniska egenskaperna hos legeringar vid hög temperatur, med ett specifikt fokus på varm duktilitet mätt med Reduction in Area. Genom att använda en metod för K-means-klustring separeras datapunkter i distinkta kluster. Därefter tillämpas en kombination av Sparse Principal Component Analysis och Zero-One Inflated Beta-regression för att analysera sambanden mellan varm duktilitet mätt genom reduktion i area och legeringssammansättningar över olika kluster. Våra resultat visar att aluminium och tenn generellt sett hade en negativ inverkan på areaminskningen. Cu har visat sig ha en mindre effekt på minskning av arean vid hög temperatur jämfört med den har vid låg temperatur.

Place, publisher, year, edition, pages
2024. , p. 49
Keywords [en]
Statistical analysis, Beta regression, Bayesian learning, Sparse PCA
National Category
Mathematics
Identifiers
URN: urn:nbn:se:umu:diva-233812OAI: oai:DiVA.org:umu-233812DiVA, id: diva2:1925530
Presentation
2024-08-22, MIT.A.378, UNIVERSITETSTORGET 4, Umeå, 11:00 (English)
Supervisors
Examiners
Available from: 2025-01-22 Created: 2025-01-08 Last updated: 2025-01-22Bibliographically approved

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