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Meeting MS&T23: Materials Science & Technology
Symposium Leveraging Integrated Computational Materials Engineering for High-fidelity Physics-based and Machine Learning Models
Presentation Title Robotic Bending of Craniomaxillofacial Graft Fixation Plates
Author(s) Brian Patrick Thurston, Javier Vazquez-Armendariz, Luis Olivas-Alanis, Tobias Mahan, Ciro Rodriguez, Michael Groeber, Stephen Niezgoda, Hany Emam, Roman Skoracki, Jian Cao, Glenn Daehn, David Dean
On-Site Speaker (Planned) Brian Patrick Thurston
Abstract Scope Large craniomaxillofacial (CMF) fixation plates may be needed within a few days to a week in cases of trauma or advanced stage cancer. While these plates can be 3D printed, the standard-of-care procedure is for the attending surgeon to manually bend an off-the-shelf Ti6V4Al CMF graft fixation plate to fit a patient’s 3D printed model derived from a virtual surgical planning session. The manual bending operation may result in a plate that does not fit the underlying bone well (i.e., there may be small gaps). In addition, repeated bending may introduce damage, reducing the plate´s fatigue life. Our group has developed a treatment planning and fixation plate design software that also integrates a fabrication process to instruct a robotic system to shape fixation plates with an orderly sequence of bending and twisting operations that provide good fit and enhanced material properties.


Application of Machine Learning Framework in Predicting Creep Response of High Temperature Alloys
Hybrid Simulation Method Based on Molecular Dynamics and Machine Learning to Improve Property Prediction with Lower Computational Cost in Complex System
New Refractory High Entropy Alloys Discovery by Physics Discovery
Novel Convolutional-Recurrent Hybrid Neural Network for Predicting Fission Gas Release in UO2 Nuclear Fuel
Robotic Bending of Craniomaxillofacial Graft Fixation Plates
Simulating Macroscale Microstructures Using Advanced Programming and Numerical Methods

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