Microstructure-based knowledge systems for capturing process-structure evolution linkages

Brough, David B.; Wheeler, Daniel; Warren, James A.; Kalidindi, Surya R.

doi:10.1016/j.cossms.2016.05.002

Cited by 41 publications

(29 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In prior work [73, 82, 83], it was found that spectral representations on functions on the local state space offered many advantages both in compact representation as well as in reducing the computational cost. In such cases, h indexes the spectral basis functions employed.…”

Section: Theoretical Foundations Of Materials Knowledge Systemsmentioning

confidence: 99%

“…The selection of these functions depends on the nature of local state descriptors. Examples include: (i) the primitive basis (or indicator functions) used to represent simple tessellation schemes [71, 72, 74–79, 84], (ii) generalized spherical harmonics used to represent functions over the orientation space [73, 82], and (iii) Legendre polynomials used to represent functions over the concentration space [83]. …”

Section: Theoretical Foundations Of Materials Knowledge Systemsmentioning

confidence: 99%

“…4, p j [ s ] is the spatially resolved (localized) response field (e.g., a response variable such as stress or strain rate in a structure-property linkage, or an evolved microstructure function in a process-structure linkage), and α [ h ; r ] are the Green's function based discretized influence kernels. These digital kernels are calibrated using regression methods [71–74, 82, 83]. …”

Section: Theoretical Foundations Of Materials Knowledge Systemsmentioning

confidence: 99%

“…3 provides schematic overviews of the MKS homogenization and localization workflows. More detailed explanations on the MKS homogenization and localization linkages can be found in prior literature [71–79, 83, 84]. …”

Section: Theoretical Foundations Of Materials Knowledge Systemsmentioning

confidence: 99%

See 3 more Smart Citations

Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials

Brough

Wheeler

Kalidindi

2017

Integr Mater Manuf Innov

Self Cite

View full text Add to dashboard Cite

There is a critical need for customized analytics that take into account the stochastic nature of the internal structure of materials at multiple length scales in order to extract relevant and transferable knowledge. Data driven Process-Structure-Property (PSP) linkages provide systemic, modular and hierarchical framework for community driven curation of materials knowledge, and its transference to design and manufacturing experts. The Materials Knowledge Systems in Python project (PyMKS) is the first open source materials data science framework that can be used to create high value PSP linkages for hierarchical materials that can be leveraged by experts in materials science and engineering, manufacturing, machine learning and data science communities. This paper describes the main functions available from this repository, along with illustrations of how these can be accessed, utilized, and potentially further refined by the broader community of researchers.

show abstract

Section: Theoretical Foundations Of Materials Knowledge Systemsmentioning

confidence: 99%

Section: Theoretical Foundations Of Materials Knowledge Systemsmentioning

confidence: 99%

Section: Theoretical Foundations Of Materials Knowledge Systemsmentioning

confidence: 99%

Section: Theoretical Foundations Of Materials Knowledge Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials

Brough

Wheeler

Kalidindi

2017

Integr Mater Manuf Innov

Self Cite

View full text Add to dashboard Cite

show abstract

“…Previous studies have successfully used these protocols to create structure-property linkages with effective properties or microstructure classification models that could be used to objectively quantify the variation in microstructures due to changes in manufacturing processes [13,29,30,34]. A related protocol for localization (i.e., top-down scale-bridging) has also been applied successfully to a variety of multiscale material phenomena [12,[24][25][26][27]32]. While previous MKS studies have successfully captured structure-property linkages, the application of the same framework to establishing process-structure evolution linkages requires the extensions developed and presented in this paper.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Extraction of Process-Structure Evolution Linkages from X-ray Scattering Measurements Using Dimensionality Reduction and Time Series Analysis

Brough

Kannan

Haaland

et al. 2017

Integr Mater Manuf Innov

Self Cite

View full text Add to dashboard Cite

The rapid development of robust, reliable, and reduced-order process-structure evolution linkages that take into account hierarchical structure are essential to expedite the development and manufacturing of new materials. Towards this end, this paper lays a theoretical framework that injects the established time series analysis into the recently developed materials knowledge systems (MKS) framework. This new framework is first presented and then demonstrated on an ensemble dataset obtained using small-angle X-ray scattering on semi-crystalline linear low density polyethylene films from a synchrotron X-ray scattering experiment.

show abstract

Materials informatics

et al. 2018

Self Cite

View full text Add to dashboard Cite

Materials informatics employs techniques, tools, and theories drawn from the emerging fields of data science, internet, computer science and engineering, and digital technologies to the materials science and engineering to accelerate materials, products and manufacturing innovations. Manufacturing is transforming into shorter design cycles, mass customization, ondemand production, and sustainable products. Additive manufacturing or 3D printing is a popular example of such a trend. However, the success of this manufacturing transformation is critically dependent on the availability of suitable materials and of data on invertible processing-structure-property-performance life cycle linkages of materials. Experience suggests that the material development cycle, i.e. the time to develop and deploy new material, generally exceeds the product design and development cycle. Hence, there is a need to accelerate materials innovation in order to keep up with product and manufacturing innovations. This is a major challenge considering the hundreds of thousands of materials and processes, and the huge amount of data on microstructure, composition, properties, and functional, environmental, and economic performance of materials. Moreover, the data sharing culture among the materials community is sparse. Materials informatics is key to the necessary transformation in product design and manufacturing. Through the association of material and information sciences, the emerging field of materials informatics proposes to computationally mine and analyze large ensembles of experimental and modeling datasets efficiently and cost effectively and to deliver core materials knowledge in user-friendly ways to the designers of materials and products, and to the manufacturers. This paper reviews the various developments in materials informatics and how it facilitates materials innovation by way of specific examples. KeywordsMaterials informatics • Materials data analytics • Materials modelling • Materials data mining • Materials selection • Materials web platform • Materials 4.0 B Seeram Ramakrishna

show abstract

Microstructure-based knowledge systems for capturing process-structure evolution linkages

Cited by 41 publications

References 75 publications

Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials

Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials

Extraction of Process-Structure Evolution Linkages from X-ray Scattering Measurements Using Dimensionality Reduction and Time Series Analysis

Materials informatics

Contact Info

Product

Resources

About