http://yetl.yabesh.ir/yetl1/handle/yetl/4303704 2026-07-19T09:34:41Z 2026-07-19T09:34:41Z He, Honglu Wen, John T. http://yetl.yabesh.ir/yetl1/handle/yetl/4310770 2026-02-17T21:52:31Z 2025-01-01T00:00:00Z

Wire Arc Additive Manufacturing With Infrared Image Feedback He, Honglu; Wen, John T. Wire arc additive manufacturing (WAAM) is a metal 3D printing technology that rapidly prototypes by depositing molten metal wire onto a substrate. Traditionally, WAAM has relied on the open-loop control with carefully tuned parameters, a process that can be time-consuming and often results in inconsistent performance. Although laser line scanners and other 3D scanning techniques have been used to ensure geometric fidelity, they typically provide feedback through layer-by-layer scans, leaving imperfections from arc striking and extinguishing. This paper introduces a novel approach that incorporates infrared (IR) camera thermography to achieve more consistent and reliable WAAM printing with real-time feedback. By using IR live streaming to close the loop with in-layer updates, we demonstrate how this feedback mechanism can enhance control over bead width consistency and wire stick-out length, ultimately leading to higher-quality metal 3D-printed structures. Compared with open-loop preset constant welding parameters on a triangular wall geometry, our IR-guided WAAM process achieves 49% bead width variance reduction and 95% wire stick-out length tracking improvement.

2025-01-01T00:00:00Z Lu, Chen-Lung He, Honglu Ren, Jinhan Dhar, Joni Saunders, Glenn Julius, Agung Samuel, Johnson Wen, John T. http://yetl.yabesh.ir/yetl1/handle/yetl/4310725 2026-02-17T21:50:40Z 2025-01-01T00:00:00Z

Multi-Robot Scan-n-Print for Wire Arc Additive Manufacturing Lu, Chen-Lung; He, Honglu; Ren, Jinhan; Dhar, Joni; Saunders, Glenn; Julius, Agung; Samuel, Johnson; Wen, John T. Robotic Wire Arc Additive Manufacturing (WAAM) is a metal additive manufacturing technology offering flexible 3D printing while ensuring high-quality near-net-shape final parts. However, WAAM also suffers from geometric imprecision, especially for low-melting-point metals such as aluminum alloys. In this article, we present a multi-robot framework for WAAM process monitoring and control. We consider a three-robot setup: a 6-DoF welding robot, a 2-DoF trunnion platform, and a 6-DoF sensing robot with a wrist-mounted laser line scanner measuring the printed part height profile. The welding parameters, including the wire feed rate, are held constant based on the materials used, so the control input is the robot path speed. The measured output is the part height profile. The planning phase decomposes the target shape into slices of uniform height. During runtime, the sensing robot scans each printed layer, and the robot path speed for the next layer is adjusted based on the deviation from the desired profile. The adjustment is based on an identified model correlating the path speed to changes in height. The control architecture coordinates the synchronous motion and data acquisition between all robots and sensors. Using a three-robot WAAM testbed, we demonstrate significant improvements of the closed-loop scan-n-print approach over the current open loop result on both a flat wall and a more complex turbine blade shape.

2025-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4310449 2026-02-17T21:39:53Z 2025-01-01T00:00:00Z

Introduction of the ASME Letters in Translational Robotics At its core, the pursuit of technology should be driven by the desire to improve human life. Robots embody some of the highest levels of technical innovations to combine motion, perception, decision-making, and intelligence into products and systems that shape our world. Robots facilitate artificial-intelligence (AI) powered systems to interact with the real world, bringing unprecedented efficiency, adaptability, and autonomy to countless applications. Robotics bridges the gap between AI and physical applications by equipping AI with mechanical structures capable of movement, manipulation, and real-world interaction. This physical embodiment of AI has led to breakthroughs in fields such as manufacturing, logistics, healthcare, and personal assistance.

2025-01-01T00:00:00Z Uplap, Apoorva Rahmani, Mehran Menon, Jay Redkar, Sangram http://yetl.yabesh.ir/yetl1/handle/yetl/4310274 2026-02-17T21:33:58Z 2025-01-01T00:00:00Z

A Novel Real-Time Data-Driven Fractional-Order Proportional–Integral–Derivative Control of a Worm Robot Using Koopman Theory Uplap, Apoorva; Rahmani, Mehran; Menon, Jay; Redkar, Sangram Conventional control approaches for inchworm robots often exhibit limitations in achieving high-precision trajectory tracking and robust adaptability due to dynamic and uncertain interaction conditions inherent to their locomotion. To address this, we present the effectiveness of integrating fundamental control strategies such as proportional–integral–derivative (PID), model predictive control (MPC), and fractional-order PID (FOPID) controllers, with Koopman operator theory, which is demonstrated in managing the nonlinear dynamics of worm robot locomotion. We leverage data-driven modeling using the Koopman operator, transforming nonlinear dynamics into infinite-dimensional linear operators, and enabling the application of linear control strategies. The Koopman operator is calculated using a deep neural network to optimize it at each time-step, ensuring the highest possible accuracy. Through rigorous simulations and experimental validation, their capability to regulate movement, maintain stability, and achieve precise trajectory tracking in worm robots is highlighted. The study underscores how conventional controllers provide a practical and computationally efficient solution for nonlinear robotic control, making them viable options for real-world applications where adaptability and reliability are crucial.

2025-01-01T00:00:00Z