Accessing pore microstructure–property relationships for additively manufactured materials

Abstract

Understanding structure-property (SP) relationships is essential for accelerating materials innovation. Still being in the state of ongoing research and development, this is especially true for additive manufacturing (AM) in which process-induced imperfections like pores and microstructural variations significantly influence the material's properties. That is why, the present work aims at proposing an approach for accessing pore SP relationships for AM materials. For this purpose, crystal plasticity (CP) simulations on reconstructed domains based on experimental measurements are employed to allow for a microstructure-sensitive investigation. For the considered Ti-6Al-4V specimen manufactured by laser powder bed fusion, the microstructure and pore characteristics are obtained by utilizing light microscopy and X-ray computed tomography at the microscale. Employing suitable statistical analysis and reconstruction, statistical volume elements with reconstructed pore distributions are created. Using them, microscale CP simulations are performed to obtain fatigue indicating parameters. Employing a further statistical analysis, fatigue ranking parameters are derived for a comparison of different microstructures. Additionally, a comparison with the empirical Murakami's square root area concept is made. Results from first numerical studies underline the potential of the approach for understanding and improving AM materials.

Descriptor-based reconstruction of three-dimensional microstructures through gradient-based optimization

“…The descriptorbased nature of the algorithm enables to create large data bases of synthetic 3D microstructure from few observations through interpolation in descriptor space. This exploration of feasible microstructures leads to valuable insight and computationally-obtained process-structure-property linkages like [21] work unit only. TensorFlow [1] is used for just-in-time compilation in order to generate efficient GPU code from a simple Python definition of the descriptors and the loss function.…”

Influence of CT image processing on the predicted impact of pores on fatigue of additively manufactured Ti6Al4V and AlSi10Mg

“…The relationship between fatigue strength and defect size can be written as $$$$ \Delta {\sigma}_{\mathrm{w}}=\Delta {\sigma}_{\mathrm{w}0}\sqrt{\frac{a_0}{a+{a}_0}} $$$$ with $$$ \Delta {\sigma}_{\mathrm{w}} $$$ as the fatigue limit, $$$ \Delta {\sigma}_{\mathrm{w}0} $$$ as the fatigue limit of the material without defects and $$$ a=\sqrt{area}=\sqrt{\pi \cdotp b\cdotp c} $$$ the square root of the ellipse fitted to the largest projected area of the largest defect with the major half‐axis $$$ b $$$ and the second largest half‐axis $$$ c $$$ [30,38]. The El‐Haddad constant $$$$ {a}_0=\sqrt{are{a}_0}=\frac{1}{\pi }{\left(\frac{\Delta {K}_{\mathrm{th},\mathrm{LC}}}{F_{\mathrm{w}}\cdotp \Delta {\sigma}_{\mathrm{w}0}}\right)}^2, $$$$ is calculated with the crack propagation threshold $$$ \Delta {K}_{\mat...$…”

“…with Δ𝜎 w as the fatigue limit, Δ𝜎 w0 as the fatigue limit of the material without defects and a = √ area = √ 𝜋 ⋅ b ⋅ c the square root of the ellipse fitted to the largest projected area of the largest defect with the major half-axis b and the second largest half-axis c [30,38]. The El-Haddad constant…”

“…Numerical and empirical material models are developed based on data from experimental analysis to simulate the fatigue behavior of AM materials. Raßloff et al [38] use light microscopy and CT data as input for microscale crystal plasticity simulations to obtain fatigue indicating parameters for Ti6Al4V manufactured by LPBF. The empirical $$$ \sqrt{\mathrm{area}} $$$‐parameter model developed by Murakami [34] allows for a fast estimation of the criticality of defects in AM components.…”