Daniel A. Hartman

Papers

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About

Daniel A. Hartman is a researcher specializing in the modeling, control, and robotic implementation of friction stir welding (FSW). His work bridges computational simulation and practical process monitoring, with a focus on enabling autonomous, high-quality welds. Hartman’s major contributions include developing a force-based misalignment detection system that uses a general regression neural network to predict tool offset from weld forces—a key advancement for real-time seam tracking. This work, published in 2008, has garnered 30 citations and demonstrates a novel application of machine learning to manufacturing feedback control. Earlier, Hartman created a three-dimensional computational fluid dynamics model of FSW using FLUENT, comparing Couette and Visco-Plastic fluid flow models for Al-6061-T6. This foundational modeling study, cited 17 times, provides critical insights for robotic FSW implementation. Together, his research integrates numerical simulation with sensor-driven control, advancing the reliability and automation of friction stir welding. Hartman’s work is particularly notable for its practical orientation, directly supporting the development of intelligent welding systems for industrial robotics.