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Meeting MS&T23: Materials Science & Technology
Symposium Additive Manufacturing: Equipment, Instrumentation and In-Situ Process Monitoring
Presentation Title Exploring a Supervisory Control System Using ROS2 and IoT Sensors
Author(s) Matthew A. Roach, Bradley Jared
On-Site Speaker (Planned) Matthew A. Roach
Abstract Scope Whether collecting data from process monitoring sensors or controlling a system of multiple actuators and electrical systems, a powerful supervisory control system must be developed for additive manufacturing (AM) systems. The Robot Operating System version 2 (ROS2) is a set of software libraries that can be used to control robotics systems and has tools for sensor value publishing. This research project is exploring the use of computational nodes connected to process monitoring sensors and robotic or electrical systems to allow for a more in-depth knowledge of the system health and process as well as open the possibilities of process control. These nodes can be connected and controlled by the ROS2 architecture. Work will be discussed exploring the reliability and speed of common AM processes and sensors such as robot controllers and thermal monitoring.


An Efficiency Study of Multi-Mode Laser Profiles
Customized Glove Box for In Situ Monitoring of Laser Directed Energy Deposition
Exploring a Supervisory Control System Using ROS2 and IoT Sensors
Fill Impact Welding: Additive Manufacturing through Ballistic Impact of Metallic Sheets
Implementing Statistical Process Control in Laser Powder Bed Fusion Metal Additive Manufacturing
In-situ Pyrometric Sensing for Real-time AM Process Monitoring and Control
Investigating the Effect of Part Geometry on Microstructure for Laser Powder Bed Fusion of Bismuth Telluride using In-Situ Process Monitoring
Melt Pool Scale Process Monitoring for Laser Hot Wire Additive Directed Energy Deposition
Quantification of Build Interruptions through In-Process Monitoring and Mechanical Test
Real Time Observations of In-Situ Alloying Molybdenum and Ti-6Al-4V in Laser Directed Energy Deposition Additive Manufacturing
Reinforcement Learning for In-situ Melt Pool Control during Laser Powder Bed Fusion
Robust Detection of L-PBF Process Anomalies Using High-speed On-axis Melt Pool Pyrometry
Two-color Melt Pool Thermal Imaging on Powder-blown Laser-DED to Advance Understanding of Melt Pool Thermal-fluid Physics

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