Uncertainty quantification of microstructure variability and mechanical behaviour of additively manufactured lattice structures
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Abstract (arXiv)
Process-induced defects are the leading cause of discrepancies between as-designed and as-manufactured additive manufacturing (AM) product behavior. Especially for metal lattices, the variations in the printed geometry cannot be neglected. Therefore, the evaluation of the influence of microstructural variability on their mechanical behavior is crucial for the quality assessment of the produced structures. Commonly, the as-manufactured geometry can be obtained by computed tomography (CT). However, to incorporate all process-induced defects into the numerical analysis is often computationally demanding. Thus, commonly this task is limited to a predefined set of considered variations, such as strut size or strut diameter. In this work, a CT-based binary random field is proposed to generate statistically equivalent geometries of periodic metal lattices. The proposed random field model in combination with the Finite Cell Method (FCM), an immersed boundary method, allows to efficiently evaluate the influence of the underlying microstructure on the variability of the mechanical behavior of AM products. Numerical analysis of two lattices manufactured at different scales shows an excellent agreement with experimental data. Furthermore, it provides a unique insight into the effects of the process on the occurring geometrical variations and final mechanical behavior.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Mar 2021.
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.
- 2024Guided Diffusion for Fast Inverse Design of Density-based Mechanical Metamaterialscited by 7
- 2025A Dynamic Time Warping-Transfer Learning Approach to Transferring Knowledge in Stress-strain Behaviors from Polymers to Metals: An Affordable and Generalizable Additive Manufacturing Part Qualification Frameworkcited by 2
- 2025GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Datacited by 0
- 2025A Dynamic Time Warping-Transfer Learning Approach to Transferring Knowledge in Stress-strain Behaviors from Polymers to Metals: An Affordable and Generalizable Additive Manufacturing Part Qualification Framework9 Dec 2025
- 2025GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data28 May 2025
- 2024Guided Diffusion for Fast Inverse Design of Density-based Mechanical Metamaterials24 Jan 2024