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AFSD-Physics: Exploring the governing equations of temperature evolution during additive friction stir deposition by a human-AI teaming approach

Autor: Shi, Tony, Ma, Mason, Wu, Jiajie, Post, Chase, Charles, Elijah, Schmitz, Tony

This paper presents a modeling effort to explore the underlying physics of temperature evolution during additive friction stir deposition (AFSD) by a human-AI teaming approach. AFSD is an emerging solid-state additive manufacturing technology that de

Externí odkaz: http://arxiv.org/abs/2401.16501

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A Physics-informed Machine Learning-based Control Method for Nonlinear Dynamic Systems with Highly Noisy Measurements

Autor: Ma, Mason, Wu, Jiajie, Post, Chase, Shi, Tony, Yi, Jingang, Schmitz, Tony, Wang, Hong

This study presents a physics-informed machine learning-based control method for nonlinear dynamic systems with highly noisy measurements. Existing data-driven control methods that use machine learning for system identification cannot effectively cop

Externí odkaz: http://arxiv.org/abs/2311.07613

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Integration of discrete-event dynamics and machining dynamics for machine tool: modeling, analysis and algorithms

Autor: Ma, Mason, Ren, Alisa, Tyler, Christopher, Karandikar, Jaydeep, Gomez, Michael, Shi, Tony, Schmitz, Tony

Machining dynamics research lays a solid foundation for machining operations by providing stable combinations of spindle speed and depth of cut. Furthermore, machine learning has been applied to predict tool life as a function of cutting speed. Howev

Externí odkaz: http://arxiv.org/abs/2211.09306

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