Revealing properties of structural materials by combining regression-based algorithms and nano indentation measurements

Huhn, Sebastian; Sonnenberg, Heike; Eggersglus, Stephan; Clausen, B.; Drechsler, Rolf

doi:10.1109/ssci.2017.8285180

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…Future work will continue the investigations shown here and extend them by defined heat treatment steps. In the next step, mapping of descriptors to descriptors [28,29] is necessary to obtain a complete high-throughput method.…”

Section: Discussionmentioning

confidence: 99%

Reproducibility of High-Throughput Sample Properties Produced by a High-Temperature Molten Metal Droplet Generator

Moqadam

Baune

Bösing

et al. 2020

Metals

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A high-throughput method for the discovery of structural materials requires a large number of samples with highly reproducible properties. We propose using spherical micro-samples, which can be quickly produced by molten metal single droplet processes with high geometrical reproducibility. However, geometrical reproducibility does not automatically yield in the reproducibility of specific properties that are governed by the microstructure and thermal history of the samples. This work evaluates the reproducibility of two different steels (AISI D3 and 5140) in their as-synthesized state without additional heat treatment. By determining a set of well-established high-throughput descriptors by electrochemical analysis, particle-oriented peening, and micro machining, we show that high reproducibility can be achieved. Additionally, the determined properties correlate well with their austenitic (AISI D3) and martensitic (AISI5140) state. The AISI D3 shows an improved corrosion resistance, increased cutting forces during machining, and a higher deformation during particle-oriented peening. The reproducibility of the sample synthesis indicates that this type of sample is well suited for high-throughput methods to find new structural materials.

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Section: Discussionmentioning

confidence: 99%

Reproducibility of High-Throughput Sample Properties Produced by a High-Temperature Molten Metal Droplet Generator

Moqadam

Baune

Bösing

et al. 2020

Metals

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show abstract

“…ML methods have been recently used in science and engineering [45][46][47][48], and have been also applied for the prediction of microstructural properties [48], optimization of material design [49], as well as inference of deformation history [50]. In the context of nanoindentation, ML has been increasingly used for the prediction of material properties such as strength, hardness and elastic moduli [51][52][53][54][55]. In this paper, we investigate two particular ML approaches that are focused on the analysis of either post-indent displacement images (e.g.…”

Section: B Machine Learning Methods and Protocolsmentioning

confidence: 99%

Inference and Prediction of Nanoindentation Response in FCC Crystals: Methods and Discrete Dislocation Simulation Examples

Tzimas,

Michopoulos,

et al. 2019

Preprint

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Nanoindentation, a common technique for probing the mechanical properties of crystalline materials, exhibits both surface and bulk-dominated responses that are linked to the parent crystal's elasticity and plasticity. For FCC crystals, the nanoindentation response is primarily controlled by the indented-grain's crystalline orientation and dislocation density. However, the indentation properties, such as hardness, have ambiguous significance at the nanoscale due to tip-specific size effects. Through extensive discrete dislocation dynamics simulations of flat-punch nanoindentation on FCC single crystals, we show that a one-to-one correspondence between an indented location and the pre-existing dislocation density is possible with the help of statistical, machine-learning algorithms. We cluster and classify various experimentally plausible ensembles with varying dislocation density and/or crystalline orientation, using nanoindentation force-depth curves or post-indent surface displacement images. We use this classification to statistically predict the nanoindentation response of a given location, through the average response of classified load-displacement curves.In addition, we demonstrate that our simulation results qualitatively agree with experimental data on common FCC single crystals. Ultimately, we propose a novel pathway to uncover the depth dependence of local surface material properties for more complete, non-destructive, microstructural characterization, through nanoindentation.

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“…ML methods have been recently used in science and engineering (DeCost et al 2017, Ramprasad et al 2017, Mueller et al 2016, Pilania et al 2013) and may predict microstructural properties (Pilania et al 2013), optimize material design (Liu et al 2015) and infer deformation history (Papanikolaou et al 2019). The usage of ML in mechanical deformation studies started from analyzing nanoindentation responses towards the prediction of material properties (Khosravani et al 2017, Iskakov et al 2018, Meng et al 2015, Huhn et al 2017. In a new direction on this topic, a recent work (Papanikolaou et al 2019) showed that the analysis of small-deformation strain correlation images may unveil the prior deformation history of materials.…”

Section: Unveiling the Crystalline Prior Deformation History Using Un...mentioning

confidence: 99%

Effects of Rate, Size, and Prior Deformation in Microcrystal Plasticity

Papanikolaou¹,

Tzimas²

2019

Mechanics and Physics of Solids at Micro‐ and Nano‐Scales

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Revealing properties of structural materials by combining regression-based algorithms and nano indentation measurements

Cited by 8 publications

References 14 publications

Reproducibility of High-Throughput Sample Properties Produced by a High-Temperature Molten Metal Droplet Generator

Reproducibility of High-Throughput Sample Properties Produced by a High-Temperature Molten Metal Droplet Generator

Inference and Prediction of Nanoindentation Response in FCC Crystals: Methods and Discrete Dislocation Simulation Examples

Effects of Rate, Size, and Prior Deformation in Microcrystal Plasticity

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