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CASE STUDY · YRI FELLOW

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.

FIELDAI & Manufacturing
RESULTFirst-author paper accepted at an IEEE conference
VENUEIEEE, 2026
BEFORE THE FELLOWSHIP

A student athlete at Northwood School in Lake Placid, New York with interests across machine learning, engineering, physics and mathematics.

AFTER

First author of a component-based adaptive digital twin framework for CNC machining, accepted at an IEEE conference.

THE LEDGER
01First-author paper accepted at an IEEE conference
02Outperformed fixed, rules-based and standard ML strategies, per reviewers
03Included noise-robustness and ablation analysis
04Research combining machine learning with advanced manufacturing
NEXT CASE STUDYSadaf Shireen, Admitted to Brown, Johns Hopkins and UT Austin

Every case study starts with one application.

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