Multi-method characterization approach to facilitate a strategy to design mechanical and electrical properties of sintered copper

Wijaya, Andi Rahadiyan; Eichinger, Barbara; Chamasemani, Fereshteh Falah; Sartory, Bernhard; Hammer, René; Maier–Kiener, Verena; Kiener, Daniel; Mischitz, Martin; Brunner, Roland

doi:10.1016/j.matdes.2020.109188

Cited by 17 publications

(7 citation statements)

References 40 publications

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“…In principle, single grains or grain boundaries are hardly detected through lab-scale µ-XCT. Nevertheless, a similar approach was reported by Wijaya et al [ 51 ], where the local crystal orientation of the microstructure was neglected and good correlation of experimental and numerical results of elastic properties was achieved. However, in case of solder joints, the local crystal anisotropy becomes a determining factor for solder joint reliability [ 52 ].…”

Section: Discussionmentioning

confidence: 78%

Analysis of Sn-Bi Solders: X-ray Micro Computed Tomography Imaging and Microstructure Characterization in Relation to Properties and Liquid Phase Healing Potential

et al. 2020

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This work provides an analysis of X-ray micro computed tomography data of Sn-xBi solders with x = 20, 30, 35, 47, 58 wt.% Bi. The eutectic thickness, fraction of eutectic and primary phase are analyzed. Furthermore, the 3D data is evaluated by means of morphology parameters, such as, shape complexity, flatness, elongation and mean intercept length tensor. The investigated alloys are categorized in three groups based on their morphology, which are described as “complex dominant”, “complex- equiaxed” and “mixed”. The mechanical behavior of Sn-Bi alloys in the semi-solid configuration and the correlation with microstructural parameters are discussed. A varying degree of geometric anisotropy of the investigated alloys is found through the mean intercept length tensor. Representative volume element models for finite element simulations (RVE-FEM) are created from tomography data of each alloy to analyze a correlation of geometric and elastic anisotropy. The simulations reveal an elastic isotropic behavior due to the small difference of elastic constants of primary and eutectic phase. A discussion of properties in the semi-solid state and liquid phase healing is provided.

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Section: Discussionmentioning

confidence: 78%

Analysis of Sn-Bi Solders: X-ray Micro Computed Tomography Imaging and Microstructure Characterization in Relation to Properties and Liquid Phase Healing Potential

et al. 2020

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“…The voxel size is 18.6 x 18.6 x 25 nm 3 . The curtaining and shadowing artefacts of the obtained tomography image data are reduced with FFT-lter 67 and histogram shifting methods 37,40 RMSE and R 2 are calculated by using mean_squared_error and r2_square modules. We set the number of features to be equal or more than two, so that different models with various number of features are generated and calculated.…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence Driven Material Design for Porous Materials

Wijaya,

Wagner,

Sartory

et al. 2023

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In general, material properties and the underlaying microstructure are linked to each other. It is a frontier challenge to understand the associated structure-property relationship, which displays an essential ingredient for accelerated material design. Herein, we approach this issue with a unique machine learning assisted material design workflow, suitable to tailor the electrical conductivity based on the 3D microstructure or vice versa, in porous copper. Specifically, we integrate a multi-variable linear regression model for the targeted prediction and utilize a U-Net deep learning architecture to accurately classify the collected 3D image data. The evaluated 3D microstructure features and the electrical conductivity are used as an input for the prediction model. We show that the prediction reaches a maximum r-squared value of about 0.94. Our results highlight the importance of accurately retrieving a set of physical scrutinized microstructure features with statistical confidence, a key to conclude about the microstructure-property relationship.

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“…In general, the material microstructure influences the material characteristics and a profound knowledge of the underlying structure-property relationships is crucial for an improved understanding as shown in e.g. 21 , 22 , 25 . The microstructure of the thin films cannot be neglected to understand the material behaviour 26 – 29 .…”

Section: Introductionmentioning

confidence: 99%

Probing the composition dependence of residual stress distribution in tungsten-titanium nanocrystalline thin films

et al. 2023

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Nanocrystalline alloy thin films offer a variety of attractive properties, such as high hardness, strength and wear resistance. A disadvantage is the large residual stresses that result from their fabrication by deposition, and subsequent susceptibility to defects. Here, we use experimental and modelling methods to understand the impact of minority element concentration on residual stresses that emerge after deposition in a tungsten-titanium film with different titanium concentrations. We perform local residual stress measurements using micro-cantilever samples and employ machine learning for data extraction and stress prediction. The results are correlated with accompanying microstructure and elemental analysis as well as atomistic modelling. We discuss how titanium enrichment significantly affects the stress stored in the nanocrystalline thin film. These findings may be useful for designing stable nanocrystalline thin films.

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Multi-method characterization approach to facilitate a strategy to design mechanical and electrical properties of sintered copper

Cited by 17 publications

References 40 publications

Analysis of Sn-Bi Solders: X-ray Micro Computed Tomography Imaging and Microstructure Characterization in Relation to Properties and Liquid Phase Healing Potential

Analysis of Sn-Bi Solders: X-ray Micro Computed Tomography Imaging and Microstructure Characterization in Relation to Properties and Liquid Phase Healing Potential

Artificial Intelligence Driven Material Design for Porous Materials

Probing the composition dependence of residual stress distribution in tungsten-titanium nanocrystalline thin films

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