The fatigue life estimation of the porous AlSi9Cu3 alloy based on the classification of pore geometry using multivariate probability distribution density
A method for assessing the fatigue life of porous structures using the strain-life approach (εa-N) is presented. Cellular voids and other forms of porosity in products introduce nonlinear geometric anomalies that result in greater scatter in fatigue life. With the aim of predicting the fatigue life...
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Elsevier
2025-05-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785425012311 |
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| author | Dejan Tomažinčič Branislav Panić Marko Nagode Jernej Klemenc |
| author_facet | Dejan Tomažinčič Branislav Panić Marko Nagode Jernej Klemenc |
| author_sort | Dejan Tomažinčič |
| collection | DOAJ |
| description | A method for assessing the fatigue life of porous structures using the strain-life approach (εa-N) is presented. Cellular voids and other forms of porosity in products introduce nonlinear geometric anomalies that result in greater scatter in fatigue life. With the aim of predicting the fatigue life of such products, specimens with different porosity topologies were first classified by considering the typical shape of the probability density distribution of porosity parameters. Each pore was first described with five geometric parameters. Then, for each test specimen, a mixture of multivariate Gaussian functions was estimated, whereby the interdependence of geometric porosity parameters was statistically modeled. With such statistical processing of data, samples with certain geometric characteristics of pores can be placed in a specific fatigue life group. This is an important step in estimating the fatigue life, as it enables easier predicting of fatigue life for a certain porosity topology. The presented method was successfully tested on experimental samples made of AlSi9Cu3 material. The proposed method offers a significant improvement in the accuracy of modeling fatigue life curves with respect to the spatial topology of porosity because it is leveraging μ-CT imaging for quantitative macrostructural characterization with statistical analysis. This approach enables robust data extraction and is suited for integration into industrial-scale manufacturing and quality control workflows. |
| format | Article |
| id | doaj-art-b154ca05e838486dbbca51c4700fe864 |
| institution | OA Journals |
| issn | 2238-7854 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj-art-b154ca05e838486dbbca51c4700fe8642025-08-20T01:59:14ZengElsevierJournal of Materials Research and Technology2238-78542025-05-0136100391005410.1016/j.jmrt.2025.05.076The fatigue life estimation of the porous AlSi9Cu3 alloy based on the classification of pore geometry using multivariate probability distribution densityDejan Tomažinčič0Branislav Panić1Marko Nagode2Jernej Klemenc3Corresponding author.; University of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, 1000, Ljubljana, SloveniaUniversity of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, 1000, Ljubljana, SloveniaUniversity of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, 1000, Ljubljana, SloveniaUniversity of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, 1000, Ljubljana, SloveniaA method for assessing the fatigue life of porous structures using the strain-life approach (εa-N) is presented. Cellular voids and other forms of porosity in products introduce nonlinear geometric anomalies that result in greater scatter in fatigue life. With the aim of predicting the fatigue life of such products, specimens with different porosity topologies were first classified by considering the typical shape of the probability density distribution of porosity parameters. Each pore was first described with five geometric parameters. Then, for each test specimen, a mixture of multivariate Gaussian functions was estimated, whereby the interdependence of geometric porosity parameters was statistically modeled. With such statistical processing of data, samples with certain geometric characteristics of pores can be placed in a specific fatigue life group. This is an important step in estimating the fatigue life, as it enables easier predicting of fatigue life for a certain porosity topology. The presented method was successfully tested on experimental samples made of AlSi9Cu3 material. The proposed method offers a significant improvement in the accuracy of modeling fatigue life curves with respect to the spatial topology of porosity because it is leveraging μ-CT imaging for quantitative macrostructural characterization with statistical analysis. This approach enables robust data extraction and is suited for integration into industrial-scale manufacturing and quality control workflows.http://www.sciencedirect.com/science/article/pii/S2238785425012311Aluminium alloyCastingPore topologyFatigue lifeMultivariate normal mixtureREBMIX |
| spellingShingle | Dejan Tomažinčič Branislav Panić Marko Nagode Jernej Klemenc The fatigue life estimation of the porous AlSi9Cu3 alloy based on the classification of pore geometry using multivariate probability distribution density Journal of Materials Research and Technology Aluminium alloy Casting Pore topology Fatigue life Multivariate normal mixture REBMIX |
| title | The fatigue life estimation of the porous AlSi9Cu3 alloy based on the classification of pore geometry using multivariate probability distribution density |
| title_full | The fatigue life estimation of the porous AlSi9Cu3 alloy based on the classification of pore geometry using multivariate probability distribution density |
| title_fullStr | The fatigue life estimation of the porous AlSi9Cu3 alloy based on the classification of pore geometry using multivariate probability distribution density |
| title_full_unstemmed | The fatigue life estimation of the porous AlSi9Cu3 alloy based on the classification of pore geometry using multivariate probability distribution density |
| title_short | The fatigue life estimation of the porous AlSi9Cu3 alloy based on the classification of pore geometry using multivariate probability distribution density |
| title_sort | fatigue life estimation of the porous alsi9cu3 alloy based on the classification of pore geometry using multivariate probability distribution density |
| topic | Aluminium alloy Casting Pore topology Fatigue life Multivariate normal mixture REBMIX |
| url | http://www.sciencedirect.com/science/article/pii/S2238785425012311 |
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