5 papers
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…
MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval
Kiarash Naghavi Khanghah, Hoang Anh Nguyen, Anna C. Doris +4
Engineering rulebooks and technical standards contain multimodal information like dense text, tables, and illustrations that are challenging for retrieval augmented generation (RAG…
Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models
Kiarash Naghavi Khanghah, Zhiling Chen, Lela Romeo +4
Additive manufacturing enables the fabrication of complex designs while minimizing waste, but faces challenges related to defects and process anomalies. This study presents a novel…
Large Language Models for Extrapolative Modeling of Manufacturing Processes
Kiarash Naghavi Khanghah, Anandkumar Patel, Rajiv Malhotra +1
Conventional predictive modeling of parametric relationships in manufacturing processes is limited by the subjectivity of human expertise and intuition on the one hand and by the c…