Accelerating Large-Format Metal Additive Manufacturing: How Controls R&D Is Driving Speed, Scale, and Efficiency
Large-format metal additive manufacturing (AM), also known as metal Big Area Additive Manufacturing (m-BAAM), is poised to be a disruptive technology in several sectors, including the tool and die and aerospace industries, where high costs and long lead times associated with metallic components are the driving factors toward a change from conventional methods of manufacturing. The challenge of large-format metal additive manufacturing, however, is simultaneous control of geometry, material properties, and residual stress and distortion. Along with pre-print modeling and simulation and post-print characterization, real-time sensing and closed-loop control have become important tools in addressing this challenge. The capability to sense build geometry and thermal properties in real-time means that systems can react automatically to compensate for changing geometric and thermal conditions as they arise, taking significant front-end workload off the AM user with respect to modeling and process planning. It also means that in addition to mass production, low-volume production runs of custom components are possible in rapid-turnaround development cycles.
This paper will highlight work at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility to develop closed-loop, feedback control for laser-wire based Directed Energy Deposition, a process being developed in partnership with GKN Aerospace specifically for the production of Ti-6Al-4V pre-forms for aerospace components. A multi-sensor approach has been utilized to measure both layer height on an interlayer basis and melt pool size in real-time, and multiple modes of closed-loop control have been developed to manipulate process parameters (laser power, print speed, deposition rate) to control these variables. Layer height control and melt pool size control have yielded excellent local (intralayer) and global (component-level) geometry control, and the impact of melt pool size control in particular on thermal gradients and material properties is the subject of continuing research. Further, these modes of control have allowed the process to advance to higher deposition rates (exceeding 7.5 lb/hr), larger parts (1-meter scale), shorter build times, and higher overall efficiency. The control modes will be examined individually, highlighting their development, demonstration, and lessons learned, and it will be shown how they operate concurrently to enable the printing of large, near net shape Ti-6Al-4V components. Specifically, a large-scale structural demonstrator component will be presented as a case-study in which the entire 3D printing workflow for m-BAAM will be discussed in detail, including design principles for large-format metal AM, toolpath generation, parameter development, process control, and system operation, as well as post-print net-shape geometric analysis and finish machining.
Accelerating Large-Format Metal Additive Manufacturing: How Controls R&D Is Driving Speed, Scale, and Efficiency
Category
Technical Paper Publication
Description
Session: 02-02-01 Conference-Wide Symposium on Additive Manufacturing I
ASME Paper Number: IMECE2020-23322
Session Start Time: November 17, 2020, 02:15 PM
Presenting Author: Brian Gibson
Presenting Author Bio: Dr. Brian T. Gibson is an R&D Staff Member in the Manufacturing Systems Research group at the Manufacturing Demonstration Facility of ORNL. His research resides at the intersection of robotics and materials processing, with broad interests that include sensing, automation, and control in metal additive manufacturing (AM) and solid-state processing. His current research is focused on robotics, sensor integration, and the development of multi-modal closed-loop control of geometric and thermal properties in metal-Big Area Additive Manufacturing (m-BAAM), with a concentration on laser-based processes, including both directed energy deposition and metal powder bed fusion.
Authors: Brian Gibson Oak Ridge National Laboratory
Paritosh Mhatre Oak Ridge National Laboratory
Michael Borish Oak Ridge National Laboratory
Tayler Sundermann University of Nebraska
Justin WestOak Ridge National Laboratory
Bradley Richardson Oak Ridge National Laboratory
Lonnie Love Oak Ridge National Laboratory
John Potter GKN Aerospace
Emma Vetland GKN Aerospace
William Henry GKN Aerospace
Christopher Allison GKN Aerospace
Emma Betters Oak Ridge National Laboratory
Scott Smith Oak Ridge National Laboratory