Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior

Spear, Ashley D.; Kalidindi, Surya R.; Meredig, Bryce; Kontsos, Antonios; Graverend, Jean‐Briac le

doi:10.1007/s11837-018-2894-0

Cited by 28 publications

(19 citation statements)

References 28 publications

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“…Moreover, it can be achieved through descriptive, predictive and prescriptive approaches. Based on the descriptive identification of linkages between process parameters, generated microstructures and resulting mechanical properties (Deshpande et al, 2016;Cecen et al, 2018), as well as the related fatigue performances and failure mechanisms (Spear et al, 2018), it is possible to predict or even prescriptively tailor and optimize microstructural features.…”

Section: Microstructurementioning

confidence: 99%

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

et al. 2019

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Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment-and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant Bock et al. Machine Learning in Materials Mechanics identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out.

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Section: Microstructurementioning

confidence: 99%

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

et al. 2019

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“…With the increased brilliance of diffraction limited storage rings that are being powered up, a new data paradigm needs to be adopted. The convergence of experimental and simulated data through the digital twin approach has also a great potential [21] as missing data can be filled in using state of the art modelling tools. These data sets are to be shared, and for this our community need to agree on an open standard.…”

Section: Discussionmentioning

confidence: 99%

“…With experimentally driven simulation of the material response, a new approach where experimental data can be supplemented by simulated data arise [21]. This may be used to identify or validate material models against the experimental observation of deformation and failure [22].…”

Section: Introductionmentioning

confidence: 99%

In situ 4D mechanical testing of structural materials: The data challenge

Proudhon

Pelerin

King

et al. 2020

Current Opinion in Solid State and Materials Science

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This papers presents recent progress with materials investigations via in situ mechanical testing at the synchrotron, so called 4D experiments. More automated and more integrated stress rigs now allow to produce new kind of data sets to study deformation and fracture of structural materials. Two recent examples are presented, one with a polymer material and another one with a polycrystalline metallic alloy. Challenges with data organisation and storage and automatic analysis are discussed in the view of the upgrade of the main synchrotron sources which will greatly increase the number and size of the future data sets.

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“…Numerical modeling in mechanics and material science is not an exception. Various surrogate deep learning data-driven models have been trained to learn and quickly inference the thermal conductivity and advanced manufacturing of composites [14,15], topologically optimized materials and structures [16,17], the fatigue of materials [18], nonlinear material response such as in plasticity and viscoplasticity [19,20], and many other applications.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Sequence Methods in Multiphysics Modeling of Steel Solidification

Korić

Abueidda

2021

Metals

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The solidifying steel follows highly nonlinear thermo-mechanical behavior depending on the loading history, temperature, and metallurgical phase fraction calculations (liquid, ferrite, and austenite). Numerical modeling with a computationally challenging multiphysics approach is used on high-performance computing to generate sufficient training and testing data for subsequent deep learning. We have demonstrated how the innovative sequence deep learning methods can learn from multiphysics modeling data of a solidifying slice traveling in a continuous caster and correctly and instantly capture the complex history and temperature-dependent phenomenon in test data samples never seen by the deep learning networks.

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Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior

Cited by 28 publications

References 28 publications

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

In situ 4D mechanical testing of structural materials: The data challenge

Deep Learning Sequence Methods in Multiphysics Modeling of Steel Solidification

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