An adaptive digital twin for smarter machining, accepted at an IEEE conference
CNC machines cut the precision parts that modern manufacturing depends on, and tuning them means balancing speed, quality and cost at the same time. Adit built an adaptive digital twin, a virtual model of the machining process that adjusts to each component's complexity, and used machine learning to optimize across multiple objectives. Reviewers noted the framework outperformed fixed, rules-based and standard machine learning approaches, with clear analysis of noise robustness and ablations.
A student athlete at Northwood School in Lake Placid, New York with interests across machine learning, engineering, physics and mathematics.
First author of a component-based adaptive digital twin framework for CNC machining, accepted at an IEEE conference.