Tata Consultancy Services Limited

Avadhut Mohanrao Sardeshmukh

Sreedhar Sannareddy Reddy

Amarendra Kumar Singh

Purushottham Gautham Basavarsu

Prasenjit Das

Raghavendra Reddy Yeddula

Sapankumar Hiteshchandra Shah

Gautham Purushottham Basavarsu

Garima Jain

The disclosure generally relates to methods and systems for generating synthetic microstructure images of a material with desired features. Conventional techniques that make use of unsupervised deep generative models has no control on the generated microstructure images with specific, desired set of features. The present disclosure generates the synthetic microstructure images of the material with desired feature, by using a variational autoencoder defined with a style loss function. In the first step, the variational autoencoder is trained to learn latent representation of microstructure image of the material. In the second step, some of the dimensions of learned latent representation is interpreted as physically significant features. In the third and last step, the latent representation required for getting the desired features is appropriately sampled based on the interpretation to generate the synthetic microstructure images of the material with desired features.

Methods and systems for generating synthetic microstructure images of material with desired features

The method(s) and system(s) for implementing extensibility of computational design and modelling of subject area on a computational platform in respect of a new subject in a subject area through a modelling schema includes populating the new subject in the model repository as data elements corresponding to the domain entity model of the domain entity meta model. The method includes generating a subject integration layer and a subject information interaction layer from the populated subject data elements and generating GUI screens for user interactions from the populated subject data elements. The method, further, includes integrating user determined external simulation tools with the computational platform by mapping the user determined external simulation tool parameters to the subject data elements. Subsequently, the method includes generating simulation tool adapters from said mapping to achieve said integration.

Model driven computational platform for integrated design of products, materials and manufacturing processes

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Pune, India

Avadhut Mohanrao Sardeshmukh