Material Agnostic Data-Driven
Framework to Develop
Structure-Property Linkages
Dipen Patel, Triplicane Parthasarathy, and Craig Przybyla
1 Introduction
Integration of advanced material systems into most engineering applications
requires a detailed understanding of the structural and functional behavior of such
materials. Most advanced materials such as metallic alloys, ceramics, polymers,
hybrids, and composites exhibit hierarchical internal structure with rich details at
multiple length scales of interest. Such microstructures have significant impact
on their behavior. Relating the processing variables, structure and behavior is the
objective of most materials models, which are directed towards use in design or in
optimization, as envisioned by Integrated Computational Materials Engineering
(ICME) [1]. A reliable and robust material modelling framework is required
that satisfies the goals of ICME, captures the physics behind structure-property
relationships at every length scale, accounts for the effects of interactions between
different length scales, and provides a way to integrate these into a computationally
efficient macro-level model. Current physics-based, multiscale models are limited in
their use due to their computational expense, and when constraints are imposed, they
capture only partial interactions. For accelerated design and material optimization,
computationally efficient models that can capture all the salient effects are needed.
In this review, we explore the status of data science as an approach that could play
a significant role in the current and future of ICME-based materials engineering. As
the name implies this approach relies on data. Thus, it is important to understand
the availability of materials data in current engineering practice and identify areas
D. Patel · T. Parthasarathy
UES, Inc, Dayton, OH, USA
C. Przybyla ()
Air Force Research Laboratory/RX, Wright-Patterson Air Force Base, Dayton, OH, USA
e-mail: craig.przybyla@us.af.mil
© Springer Nature Switzerland AG 2020
S. Ghosh et al. (eds.), Integrated Computational Materials Engineering (ICME),
https://doi.org/10.1007/978-3-030-40562-5_9
249
Framework to Develop
Structure-Property Linkages
Dipen Patel, Triplicane Parthasarathy, and Craig Przybyla
1 Introduction
Integration of advanced material systems into most engineering applications
requires a detailed understanding of the structural and functional behavior of such
materials. Most advanced materials such as metallic alloys, ceramics, polymers,
hybrids, and composites exhibit hierarchical internal structure with rich details at
multiple length scales of interest. Such microstructures have significant impact
on their behavior. Relating the processing variables, structure and behavior is the
objective of most materials models, which are directed towards use in design or in
optimization, as envisioned by Integrated Computational Materials Engineering
(ICME) [1]. A reliable and robust material modelling framework is required
that satisfies the goals of ICME, captures the physics behind structure-property
relationships at every length scale, accounts for the effects of interactions between
different length scales, and provides a way to integrate these into a computationally
efficient macro-level model. Current physics-based, multiscale models are limited in
their use due to their computational expense, and when constraints are imposed, they
capture only partial interactions. For accelerated design and material optimization,
computationally efficient models that can capture all the salient effects are needed.
In this review, we explore the status of data science as an approach that could play
a significant role in the current and future of ICME-based materials engineering. As
the name implies this approach relies on data. Thus, it is important to understand
the availability of materials data in current engineering practice and identify areas
D. Patel · T. Parthasarathy
UES, Inc, Dayton, OH, USA
C. Przybyla ()
Air Force Research Laboratory/RX, Wright-Patterson Air Force Base, Dayton, OH, USA
e-mail: craig.przybyla@us.af.mil
© Springer Nature Switzerland AG 2020
S. Ghosh et al. (eds.), Integrated Computational Materials Engineering (ICME),
https://doi.org/10.1007/978-3-030-40562-5_9
249