Machine learning for characterizing uncertain elastic properties of fused filament fabricated materials for topology optimization applications

Zahra Kazemi; Craig A. Steeves

Home/Archive/arXiv

arXiv · 2408.05850

Machine learning for characterizing uncertain elastic properties of fused filament fabricated materials for topology optimization applications

Zahra Kazemi·Craig A. Steeves

View original source ↗Read source document ↗Export metadata ↓

Abstract

The layering approach used in fused filament fabrication (FFF) enables creation of complex designs generated by topology optimization. Defects associated with the layer-by-layer process, introduce considerable random variability to the local elastic modulus of the print. The elastic modulus along the fusion layers connecting bulk materials differs from that of the bulk areas. Accurate quantitative measurements of variations in both areas are essential to achieve robust optimized designs. This study aims to quantify the parameters of the random distributions given the surface strain field of the print measured by digital image correlation (DIC). Two statistical properties, mean and standard deviation, are sufficient to characterize the stochastic elastic modulus fields in each region. An efficient neural network model is developed to estimate spatial variations in the local elastic modulus within both bulk and fusion layers. This model is trained on a dataset of synthetic strain fields with known distributions in the elastic modulus fields. It performs well in correlating the elastic modulus with the input strain, as long as the standard deviation is below 60% of the mean of the random field. The predictive accuracy of the model on testing data, measured by the R2 score, is 0.99 and 0.95 for mean and standard deviation in the fusion material. The scores for the bulk material are 0.97 each. The trained model is implemented to predict the elastic modulus distribution of an FFF-printed material at a print speed of 30 mm/s and an extrusion temperature of 493.15 K, based on its DIC-measured surface strain data. The model predicts a mean and standard deviation of 1.2 GPa and 1 GPa for the bulk material and 400 MPa and 430 MPa for the fusion region. Validation of predictions confirms the reliability of this approach in measuring uncertainty in the local properties of the prints.

Explore related subjects

cond-mat.mtrl-sci cond-mat.dis-nn

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Zahra Kazemi, Craig A. Steeves. 2024-08-31. Machine learning for characterizing uncertain elastic properties of fused filament fabricated materials for topology optimization applications. https://arxiv.org/abs/2408.05850

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Incommensurate structural and magnetic modulations in potassium-rich cryptomelane, K$_x$Mn$_8$O$_{16}$ ($x\approx1.45$)

Cryptomelane is a hollandite-like material consisting of K$^+$ cations in an $α$-MnO$_2$ tunnel-like crystallographic motif. Cryptomelane with stoichiometry K$_x$Mn$_8$O$_{16}$ ($x\approx1.45$) has been synthesized and its magnetic properties investigated using variable-temperature magnetic susceptibility, heat capacity, and neutron powder diffraction. Three distinct transitions at $T_1=184$\,K, $T_2=54.5$\,K, and $T_3=24$\,K are observed. At $T_1$ there is a subtle tetragonal$\rightarrow$monoclinic transition associated with emergence of a set of non-magnetic superstructure peaks indexable to a $\vec{k}_\mathrm{struc}\approx0.74\vec{c^*}$ incommensurate modulation parallel to the $α$-MnO$_2$ tunnels. Our findings are consistent with a relation previously reported in titanate hollandites, that $x\approx2|\vec{k}_\mathrm{struc}|$. Magnetic Bragg peaks emerge below $T_2=54.5$\,K, and their positions indicate an incommensurate modulated magnetic structure. The model consistent with the data is a dual-$\vec{k}_\mathrm{mag}$ structure with a ferromagnetic $|\vec{k}_\mathrm{mag}|=0$ component and an incommensurate $\vec{k}_\mathrm{mag}\approx0.37\vec{c^*}$, with the latter most likely to be helical. The period of oscillation of the incommensurate magnetic component is in line with predictions based on a Heisenberg spin Hamiltonian [Mandal \textit{et al}. Phys. Rev. B 90, 104420 (2014)]. Below $T_3=24$\,K, there is a magnetic transition, which gives rise to a different set of magnetic Bragg peaks indicative of a highly complex magnetic structure.

cond-mat.mtrl-sci↗

arXiv2026

An anisotropic functional for two-dimensional material systems

Density function theory is the workhorse of modern electronic structure theory. However, its accuracy in practical calculations is limited by the choice of the exchange-correlation potential. In this respect, two-dimensional materials pose a special challenge, as all these materials and their heterostructures have a crucial similarity. The underlying atomic structures are strongly spatially inhomogeneous, implying that current exchange-correlation functionals, that in almost all cases are isotropic, are ill-prepared for an accurate description. We present an anisotropic screened-exchange potential, that remedies this problem and reproduces the band-gap of 2D materials as well as the piecewise linearity of the total energy with fractional occupation number.

cond-mat.mtrl-sci↗

arXiv2026

Thermally-driven reorientation of the Néel vector in altermagnetic MnTe

Altermagnets are novel magnetic systems that possess a spin-polarized electronic band structure without a net magnetic moment, making them promising for device applications. Hexagonal MnTe, a prototypical altermagnet, arguably exhibits the most properties consistent with theoretical predictions, including an anomalous Hall effect despite a vanishing net magnetization, and altermagnetinduced electronic band splitting. However, fundamental questions remain, including why some effects only appear significantly below the magnetic ordering temperature. Here, we resolve this discrepancy by revealing a reorientation of the Néel vector in single-crystalline MnTe. The Néel vector points 30° from the a-axis at low $T$, before aligning directly with the a-axis around $T\simeq 260$ K. We attribute this to single-ion anisotropy, which depends on temperature-dependent lattice parameters. We obtained these results using muon-spin spectroscopy, magnetization measurements, and X-ray diffraction; we show that the findings are consistent with neutron diffraction. Manipulating this effect, for example through strain, could unlock sensitive electronic detection schemes for external stimuli, paving the way for functional altermagnetic devices.

cond-mat.mtrl-sci↗