Ut-Battelle, Inc.

Joel M Asiamah

Milton Nance Ericson

Ali Passian

Michael D Iannacone

Joel A Dawson

Robert C Jordan

Varisara Tansakul

Srikanth B Yoginath

Gavin B Long

Additive manufacturing's reliance on embedded computing renders it vulnerable to tampering through cyber-attacks. Sensor instrumentation of additive manufacturing devices allows for rigorous process and security monitoring, but also results in a massive volume of noisy data for each run. As such, in-situ, near-real-time anomaly detection is challenging. A probabilistic-model-based approach addresses this challenge.

Stealthy process attack detection for automated manufacturing

Growing community of inventors

Knoxville, TN, United States of America

Joel M Asiamah