About this Abstract |
| Meeting |
2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026)
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| Symposium
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2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026)
|
| Presentation Title |
AI-Driven Robotic Forming of Sheet Metals: A Smart Manufacturing Approach for Defense Applications |
| Author(s) |
Kenneth Duran, MD Tusher Ahmed, Ahmed Bendaouia, Jianzhi Li |
| On-Site Speaker (Planned) |
Kenneth Duran |
| Abstract Scope |
Modern day defense applications demand lightweight, rapidly manufacturable, and highly customizable structures; however, conventional forming methods for thin metallic components remain slow, tooling-intensive, and highly dependent on trial-and-error process optimization. One of the greatest challenges in sheet metal forming is Springback, the elastic recovery of the material after unloading, which causes the final geometry to deviate significantly from the intended shape. Springback remains difficult to predict analytically. This project presents a simulation and machine learning driven robotic forming framework for intelligent sheet metal shaping aimed at next-generation drone manufacturing. A high-throughput computational pipeline based on PyMAPDL, a platform to efficiently automate ANSYS Mechanical finite element simulation (FEA), will be developed to automate large-scale three-point bending simulations across a wide parametric design space. By combining physics-based finite element simulations with data-driven intelligence, this work seeks to accelerate agile defense manufacturing and advance autonomous forming technologies for aerospace applications. |
| Proceedings Inclusion? |
Planned: Post-meeting proceedings |