About this Abstract |
| Meeting |
2025 AWS Professional Program
|
| Symposium
|
2025 AWS Professional Program
|
| Presentation Title |
Laser Forming of Sheet Metal in Thermal Vacuum |
| Author(s) |
Benjamin L Rupp, Andrew O'Connor, Jonathan M Bonebrake, Ellis R Crabtree, John C Ivester, Ayman M Girgis, Jeffrey W Sowards, Brian Valdez, Jennifer Jones |
| On-Site Speaker (Planned) |
Benjamin L Rupp |
| Abstract Scope |
Laser forming (LF) of sheet metal is an emerging process that can form complex shapes and structures without physical contact with the workpiece. Allied with a joining process, complicated structures can be created. Beyond terrestrial applications, the LF process appears highly relevant to in-space manufacturing of large, complex structures where the reactive forces of mechanical bending or forming techniques would be exceedingly difficult to counteract.
Building off of work performed in low vacuum at the University of Florida, NASA Marshall has demonstrated LF of common aerospace metals in thermal vacuum (TVAC). Both stainless steel and titanium alloys were readily bent in high vacuum and over temperatures ranging from -120°C to 60°C. Work to optimize the LF process for aluminum in these extremes is ongoing, complicated by its high reflectivity to infrared laser light, high thermal conductivity, and relatively low solidus temperature. Copious data recorded during these LF under TVAC experiments – including obverse and reverse thermography, workpiece displacement, thermocouple readings, and per-pass bend measurements – is both revealing the physical basis of LF and anchoring computational models. Metallography, microscopy, and mechanical testing are further elucidating the LF process.
Work on a digital twin of the LF process is progressing. Finite element thermal modeling elucidated the difference between LF under atmospheric and vacuum conditions. Post-bend distortion was quantified by structured light scanning. Explorations into machine learning have indicated the viability of certain techniques like support vector machines to find linear equations that indicate the relative contribution of process parameters and environmental conditions to the LF process. Uniting these methods will allow not only understanding of the fundamental influences on LF but also development of a predictive model for in-space LF. |
| Proceedings Inclusion? |
Undecided |