A comparative study of mechanical properties of table tennis balls - Hiemer, Stefan; Fey, Tobias; Kaschta, Joachim; Schubert, Dirk W. (54th Nordic Polymer Days, 2017)

A novel study on the sandwich-structure strain sensor using ethylene-vinyl acetate-based hot-melt adhesive mesh web: Fabrication, properties, and modeling - Qu, Muchao; Fang, Jiaqiang; Mu, Chenzhong; Li, Yanfeng; Huang, Shaojuan; Han, Lei; Hiemer, Stefan; Xu, Wei; Qin, Yijing (Journal of Applied Polymer Science, 2022)
In this study, a flexible strain sensor consisting of carbon nanotubes (CNTs) and ethylene-vinyl acetate (EVA)-based hot-melt adhesive web (HAW) is proposed with the advantages of low cost and easy fabrication. Six different weight fractions of the CNTs were employed in producing the three-layer sandwich-structure composite membrane. The morphology and internal particle structure of the cross section of the EVA/CNTs composite membranes was studied using scanning electron microscopy. The mechanical properties as well as the mechanical-electrical behavior of the EVA/CNTs sensors were investigated, showing a gauge factor up to 477. In addition, two mathematical equations to establish the relationship between the output electrical signal and the input strain signal during the monotonic and cyclic measurements were proposed, respectively. Overall, this study provides a solvent-free one-step approach for the preparation of flexible strain sensors with innovative and broad application prospects, and offers some insight into the mathematical prediction of the strain sensing behavior.
CRIS-Team WoS Importer:2022-10-21

A novel study on the sandwich‐structure strain sensor using ethylene‐vinyl acetate‐based hot‐melt adhesive mesh web: Fabrication, properties, and modeling - Qu, Muchao; Fang, Jiaqiang; Mu, Chenzhong; Li, Yanfeng; Huang, Shaojuan; Han, Lei; Hiemer, Stefan; Xu, Wei; Qin, Yijing (Journal of Applied Polymer Science, 2022)
In this study, a flexible strain sensor consisting of carbon nanotubes (CNTs) and ethylene-vinyl acetate (EVA)-based hot-melt adhesive web (HAW) is proposed with the advantages of low cost and easy fabrication. Six different weight fractions of the CNTs were employed in producing the three-layer sandwich-structure composite membrane. The morphology and internal particle structure of the cross section of the EVA/CNTs composite membranes was studied using scanning electron microscopy. The mechanical properties as well as the mechanical–electrical behavior of the EVA/CNTs sensors were investigated, showing a gauge factor up to 477. In addition, two mathematical equations to establish the relationship between the output electrical signal and the input strain signal during the monotonic and cyclic measurements were proposed, respectively. Overall, this study provides a solvent-free one-step approach for the preparation of flexible strain sensors with innovative and broad application prospects, and offers some insiht into the mathematical prediction of the strain sensing behavior.<br

From mechanism-based to data-driven approaches in materials science - Hiemer, Stefan; Zapperi, Stefano (Materials Theory, 2021)
A time-honored approach in theoretical materials science revolves around the search for basic mechanisms that should incorporate key feature of the phenomenon under investigation. Recent years have witnessed an explosion across areas of science of a data-driven approach fueled by recent advances in machine learning. Here we provide a brief perspective on the strengths and weaknesses of mechanism based and data-driven approaches in the context of the mechanics of materials. We discuss recent literature on dislocation dynamics, atomistic plasticity in glasses focusing on the empirical discovery of governing equations through artificial intelligence. We conclude highlighting the main open issues and suggesting possible improvements and future trajectories in the fields.
Keywords: Machine learning; Perspective; Dislocation dynamics; Plasticity in glasses; Equation discovery

Implementation of shear thinning behavior in the fused filament fabrication melting model: Analytical solution and experimental validation - Colón Quintana, José Luis; Hiemer, Stefan; Granda Duarte, Nancy; Osswald, Tim (Additive Manufacturing, 2020)
Fused Filament Fabrication (FFF) has been on the forefront of Additive Manufacturing (AM) due to low costs, ability to manufacture complex geometries, and broad availability of AM machines in the market. Various theoretical models have been proposed to explain the melting behavior of the polymer filament in the FFF process. The most prevalent model involves the formation of a pool of polymer melt at the nozzle that is pushed out of the orifice by the solid filament acting as a piston (Bellini et al., 2004) [1]. An alternative model that suggests the formation of a melt film, with thickness varying as a function of the force exerted by the solid filament, is used as basis for this work. This model, known as the "Fused Filament Fabrication melting model", was proposed by Osswald et al. (2018) [2] and assumes a Newtonian fluid, which is a simplification of the real rheological behavior of polymeric melts. This work aims to improve this model by implementing the shear thinning behavior of the material into the equations. The analytical solution of the shear thinning model is compared to models found in literature using experimental data at different printing conditions. By comparing these models, it was shown that the implementation of shear thinning effects enhances the prediction of the melting behavior of the filament material at both low and high printing temperatures using the corresponding assumptions.
CRIS-Team Scopus Importer:2020-12-04

Predicting creep failure by machine learning - which features matter? - Hiemer, Stefan; Moretti, Paolo; Zapperi, Stefano; Zaiser, Michael (Forces in Mechanics, 2022)
Spatial and temporal features are studied with respect to their predictive value for failure time prediction in subcritical failure with machine learning (ML). Data are generated from simulations of a novel, brittle random fuse model (RFM), as well as elasto-plastic finite element simulations (FEM) of a stochastic plasticity model with damage, both models considering stochastic thermally activated damage/failure processes in disordered materials. Fuse networks are generated with hierarchical and nonhierarchical architectures. Random forests - a specific ML algorithm - allow us to measure the feature importance through a feature's average error reduction. RFM simulation data are found to become more predictable with increasing system size and temperature. Increasing the load or the scatter in local materials properties has the opposite effect. Damage accumulation in these models proceeds in stochastic avalanches, and statistical signatures such as avalanche rate or magnitude have been discussed in the literature as predictors of incipient failure. However, in the present study such features proved of no measurable use to the ML models, which mostly rely on global or local strain for prediction. This suggests the strain as viable quantity to monitor in future experimental studies as it is accessible via digital image correlation.
CRIS-Team Scopus Importer:2022-12-09
Keywords: Fracture; Machine learning; Random fuse model; Subcritical failure

Predicting the failure of two-dimensional silica glasses - Font-Clos, Francesc; Zanchi, Marco; Hiemer, Stefan; Bonfanti, Silvia; Guerra, Roberto; Zaiser, Michael; Zapperi, Stefano (Nature Communications, 2022)
The sheer number of parameters in deep learning makes the physical interpretation of failure predictions in glasses challenging. Here the authors use Grad-CAM to reveal the role of topological defects and local potential energies in failure predictions.
CRIS-Team WoS Importer:2022-06-03

Relating plasticity to dislocation properties by data analysis: scaling vs. machine learning approaches - Hiemer, Stefan; Fan, Haidong; Zaiser, Michael (Materials Theory, 2023)