Imagars LLC

Anand Kulkarni

Baldur Andrew Steingrimsson

Xuesong Fan

Peter K Liaw

Kwan Yi

Robert L Jones

Mikalai Kisialiou

Duckbong Kim

This invention presents an innovative framework for the application of machine learning for identification of alloys or composites with desired properties of interest. For each output property of interest, we identify the corresponding driving (input) factors. These input factors may include the material composition, heat treatment, process, microstructure, temperature, strain rate, environment or testing mode. Our framework assumes selection of optimization technique suitable for the application at hand and data available, starting with simple linear, or quadratic, regression analysis. We present a physics-based model for predicting the ultimate tensile strength, a model that accounts for physical dependencies, and factors in the underlying physics as a priori information. In case an artificial neural network is deemed suitable, we suggest employing custom kernel functions consistent with the underlying physics, for the purpose of attaining tighter coupling, better prediction, and extracting the most out of the—usually limited—input data available.

Machine learning to accelerate alloy design

This invention presents a framework for applying artificial intelligence to aid with product design, mission or retail planning. The invention outlines a novel approach for applying predictive analytics to the training of a system model for product design, assimilates the definition of meta-data for design containers to that of labels for books in a library, and represents customers, requirements, components and assemblies in the form of database objects with relational dependence. Design information can be harvested, for the purpose of improving decision fidelity for new designs, by providing such database representation of the design content. Further, a retrieval model, that operates on the archived design containers, and yields results that are likely to satisfy user queries, is presented. This model, which is based on latent semantic analysis, predicts the degree of relevance between accessible design information and a query, and presents the most relevant previous design information to the user.

Decisions with big data

This invention presents a transformative design decision support tool (an e-Design Assessment Engine), one that The design oversights are identified through proper structuring of the engineering design requirements, extraction of relevant design parameters through application program interfaces provided by the pertinent design tools, and mapping against the requirements. Big data analytics are applied to repositories of past designs, for the purpose of improving new designs. The Engine can be used stand-alone, as a part of a design ecosystem, or integrated into existing systems for product lifecycle or data management.

Automatic requirement verification engine and analytics

Growing community of inventors

Davidson, NC, United States of America

Anand Kulkarni