Oregon Patent of the Month – March 2024
The world of material science is continually evolving, driven by the quest to discover new alloys and composites with exceptional properties. Imagars, LLC, experts in machine learning and engineering design, has developed a new approach using predictive analytics and big data analytics to accelerate alloy design.
This innovative approach integrates a prediction module that harnesses the power of predictive analytics to efficiently explore the composition space of alloys or composites. At its core lies a sophisticated database importing module that ingests vast amounts of materials or manufacturing data, laying the foundation for informed decision-making.
But what sets this invention apart is its adaptability. With optional modules for data abstraction, preprocessing, and feature selection, the apparatus ensures unified comparison and extraction of crucial features essential for predicting material properties or manufacturing parameters accurately.
Central to its functionality is the prediction engine, a marvel of engineering that combines physics-based models with traditional black-box prediction techniques. This fusion not only enhances prediction accuracy but also expedites training, making it a formidable tool for accelerating alloy design.
Whether predicting fatigue behavior, optimizing heat treatments, or developing corrosion-resistant coatings, the prediction engine rises to the challenge, leveraging custom kernel functions and parametrized models to extract actionable insights from the data.
The apparatus supports both “forward” and “inverse” prediction, enabling researchers to predict material properties from source characteristics or infer source characteristics from desired material properties—a feat previously thought impossible.
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