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  4. A review of the application of machine learning and data mining approaches in continuum materials mechanics
 
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A review of the application of machine learning and data mining approaches in continuum materials mechanics

Citation Link: https://doi.org/10.15480/882.2309
Publikationstyp
Journal Article
Date Issued
2019-05-15
Sprache
English
Author(s)
Bock, Frederic E.  
Aydin, Roland C.  
Cyron, Christian J.  
Huber, Norbert  orcid-logo
Kalidindi, Surya R.  
Klusemann, Benjamin  
Institut
Kontinuums- und Werkstoffmechanik M-15  
Werkstoffphysik und -technologie M-22  
TORE-DOI
10.15480/882.2309
TORE-URI
http://hdl.handle.net/11420/2852
Journal
Frontiers in Materials  
Volume
6
Start Page
Article No. 110
Citation
Frontiers in Materials (6): 110 (2019)
Publisher DOI
10.3389/fmats.2019.00110
Scopus ID
2-s2.0-85067394950
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 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.
Subjects
machine learning
materials mechanics
data mining
process-structure-property-performance relationship
knowledge discovery
MLE@TUHH
DDC Class
620: Ingenieurwissenschaften
Funding(s)
SFB 986: Teilprojekt B9 - Mikrostrukturbasierte Klassifizierung und mechanische Analyse nanoporöser Metalle durch maschinelles Lernen  
Lizenz
https://creativecommons.org/licenses/by/4.0/
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