A Generalized and Modular Framework for Digital Generation of Composite Microstructures

Cecen, Ahmet; Yucel, Berkay; Kalidindi, Surya R.

doi:10.3390/jcs5080211

Cited by 9 publications

(2 citation statements)

References 59 publications

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“…Using solution samples and a neural network predictor, we generated future basis functions with low computing overhead. A non-intrusive method was used in [2] to establish multi-scale uncertainty quantification (UQ) and uncertainty propagation. Using the top-down sampling strategy, the non-stationary and continuous spatial variations of nested random fields were used There is a relationship between polycrystalline material microstructure unpredictability and fatigue life scatter, as shown by a fatigue crack initiation and life prediction model developed in [3].…”

Section: Introductionmentioning

confidence: 99%

“…The synergies between these two fields have a long story and throughout the past decades, ED has increasingly benefited from an integration with DS A generalized framework for the digital generation of composite microstructures using filter-based approaches that can devise and utilize a wide variety of cost functions reflecting the desired targets on geometrical and statistical measures. The use of filter-based approaches leads to remarkable computational advantages compared to the conventional approaches used currently for microstructure generation [2]. The framework provides a highly modular and flexible approach to generate stochastic ensembles of microstructures meeting user-defined microstructural characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Tversky Similarity based Deep Neural Learning Classification for Engineering Alloys

Raja

Vidhya

Sumithra

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Integrated Computational Materials Engineering (ICME) is an environment friendly technique used for performing cloth discovery and design. Computational methods introduced a new deep studying classification approach to display screen the candidate cloth designs. During the product designing stage, the ingredients are customised to meet particular needs. In ICME processes, there is always a degree of uncertainty in the process, structure, and property components. Uncertainties may be quantified, reduced, and propagated via structure–property links using the Tversky Similarity based Deep Neural Learning Classification (TS-DNLC) Method. In TS-DNLC Method, number of compound data are considered as input and given to the input layer. An input compound data is given to hidden layer 1. In that layer, regression is employed for performing the compound data analysis with structure–property linkages. After that, the regression coefficient value is sent to the hidden layer 2. In that layer, Tversky similarity function is used to identify the similarity between the regression coefficient value of training compound data and threshold value. Tversky similarity value varies from 0 to 1 and the results are transmitted to the output layer. By this way, TS-DNLC Method improves the performance of structure–property linkages. The computational cost of proposed TS-DNLC Method is higher than conventional uncertainty quantification.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tversky Similarity based Deep Neural Learning Classification for Engineering Alloys

Raja

Vidhya

Sumithra

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

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Microstructure Characterization and Reconstruction in Python: MCRpy

Seibert

Raßloff

Kalina

et al. 2022

Integr Mater Manuf Innov

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Microstructure characterization and reconstruction (MCR) is an important prerequisite for empowering and accelerating integrated computational materials engineering. Much progress has been made in MCR recently; however, in the absence of a flexible software platform it is difficult to use ideas from other researchers and to develop them further. To address this issue, this work presents MCRpy as an easy-to-use, extensible and flexible open-source MCR software platform. MCRpy can be used as a program with graphical user interface, as a command line tool and as a Python library. The central idea is that microstructure reconstruction is formulated as a modular and extensible optimization problem. In this way, arbitrary descriptors can be used for characterization and arbitrary loss functions combining arbitrary descriptors can be minimized using arbitrary optimizers for reconstructing random heterogeneous media. With stochastic optimizers, this leads to variations of the well-known Yeong–Torquato algorithm. Furthermore, MCRpy features automatic differentiation, enabling the utilization of gradient-based optimizers. In this work, after a brief introduction to the underlying concepts, the capabilities of MCRpy are demonstrated by exemplarily applying it to typical MCR tasks. Finally, it is shown how to extend MCRpy by defining a new microstructure descriptor and readily using it for reconstruction without additional implementation effort.

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