9 citations · 15 across the 9 of their papers we have counts for
7 papers · 1 filter
Physics-Distilled Neural Network enabled by Large Language Models for Manufacturing Process-Property Predictive Modeling
Ge Song, Kiarash Naghavi Khanghah, Anandkumar Patel +2
Predicting process-property relationships in manufacturing is often challenged by high experimental costs and the limited interpretability of complex 'black-box' models. This paper…
GPT-Micro: A large language paradigm for accelerated, inexpensive, and thermodynamics-consistent discovery of constitutive models in manufacturing
Soumik Dutta, Kiarash Naghavi Khanghah, Sania Shree +4
Constitutive modeling of the relationship between process-imposed material states and fundamental material properties is critical to control of material microstructure in manufactu…
LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing
Ahmadreza Eslaminia, Chuhan Cai, Cameron Smith +5
Additive manufacturing (AM) continues to transform modern manufacturing by enabling flexible, on-demand production of complex geometries across diverse industries. Fused filament f…
Hyperdimensional Computing for Sustainable Manufacturing: An Initial Assessment
Danny Hoang, Anandkumar Patel, Ruimen Chen +2
Smart manufacturing can significantly improve efficiency and reduce energy consumption, yet the energy demands of AI models may offset these gains. This study utilizes in-situ sens…
Domain-Aware Hyperdimensional Computing for Edge Smart Manufacturing
Fardin Jalil Piran, Anandkumar Patel, Rajiv Malhotra +1
Smart manufacturing requires on-device intelligence that meets strict latency and energy budgets. HyperDimensional Computing (HDC) offers a lightweight alternative by encoding data…
FDM-Bench: A Comprehensive Benchmark for Evaluating Large Language Models in Additive Manufacturing Tasks
Ahmadreza Eslaminia, Adrian Jackson, Beitong Tian +5
Fused Deposition Modeling (FDM) is a widely used additive manufacturing (AM) technique valued for its flexibility and cost-efficiency, with applications in a variety of industries…
Accelerated and Inexpensive Machine Learning for Manufacturing Processes with Incomplete Mechanistic Knowledge
Jeremy Cleeman, Kian Agrawala, Rajiv Malhotra
Machine Learning (ML) is of increasing interest for modeling parametric effects in manufacturing processes. But this approach is limited to established processes for which a deep p…