Closed-loop High-fidelity Simulation Integrating Finite Element Modeling with Feedback Controls in Additive Manufacturing
Oct 1, 2020
Closed-loop Simulation Integrating Finite Element Modeling with Feedback Controls in Powder Bed Fusion Additive Manufacturing
Jul 1, 2020
Robotic Target Following with Slow and Delayed Visual Feedback
Jan 1, 2020
Best Student Paper on Robotics, ASME Dynamic Systems and Control Division
My student Hui Xiao received this Best Student Paper on Robotics award on October 11, 2019, from the ASME Dynamic Systems and Control Division. Our paper, titled “Following fast-dynamic targets with only slow and delayed visual feedback - a Kalman filter and model-based prediction approach,” addresses key challenges in real-time vision-based robotic applications.
Oct 11, 2019
Control-Oriented Modeling and Repetitive Control of In-Layer and Cross-Layer Thermal Interactions in Selective Laser Sintering
Jan 1, 2019
Best Paper, ISCIE/ASME International Symposium on Flexible Automation
Dan Wang and I received the Best Paper Award on July 19, 2018, at the Japan Institute of Systems, Control, and Information Engineers (ISCIE) / ASME International Symposium on Flexible Automation. Our paper, titled “Synthesis and analysis of multirate repetitive control for fractional-order periodic disturbance rejection in powder bed fusion,” represents a significant step forward in advancing additive manufacturing control techniques.
Jul 19, 2018
Synthesis and Analysis of Multirate Repetitive Control for Fractional-order Periodic Disturbance Rejection in Powder Bed Fusion
Jul 1, 2018
A Multirate Repetitive Control for Fractional-Order Servos in Laser-Based Additive Manufacturing
Jun 1, 2018
NSF CAREER Award
This Faculty Early Career Development Program (CAREER) project researches into substantially higher accuracy and greater reproducibility in additive manufacturing (AM) processes. In contrast to conventional machining, where parts are made by cutting away unwanted material, additive manufacturing – also called 3D printing – builds three-dimensional objects of unprecedented complexity by progressively adding small amounts of material. Powder bed fusion (PBF), in which new material is added to the part being fabricated by applying and selectively melting a powdered feedstock, is a popular form of AM for fabricating complex metallic or high-performance polymeric parts. This project supports fundamental research to create new thermal modeling, sensing, and control algorithms that will lead to precise and reliable PBF. The modeling task will enable fast and accurate prediction of heat flow and temperature distribution during powder fusion. The resulting knowledge on directing heat flow is essential for achieving a desired three-dimensional shape. The sensing task will formulate new signal processing algorithms that discard unnecessary information to make full use of data-intensive sensor sources like high-speed video. Finally, these results will be integrated with new control algorithms in order to counteract process variations and provide repeatable, low-cost, high-quality parts. AM offers untapped potential in a wide range of products for the energy, aerospace, automotive, healthcare, and biomedical industries. PBF parts are increasingly preferred in applications ranging from advanced jet-engine components to custom-designed medical implants. The project creates new knowledge that will facilitate fabrication of products to benefit the US economy and improve quality of life. Broader impacts of the project will be augmented by dissemination of educational results to inculcate skills for innovative problem solving into undergraduate engineering education. Read more here.
Mar 15, 2018
Transmission of Signal Nonsmoothness and Transient Improvement in Add-On Servo Control
Mar 1, 2018