Machine Learning-Enhanced Model Predictive Control for Incremental Bending of Skeletal Fixation Plates

Abstract Skeletal fixation plates are essential components in craniomaxillofacial (CMF) reconstructive surgery to connect skeletal disunions. To ensure that these plates achieve geometric conformity to the CMF skeleton of individual patients, a pre-operative procedure involving manual plate bending is traditionally required. However, manual adjustment of the fixation plate can be time-consuming and is prone to geometric error due to the springback effect and human inspection limitations. This work represents a first step towards autonomous incremental plate bending for CMF reconstructive surgery through machine learning-enabled springback prediction and feedback bending control. Specifically, a Gaussian process is first investigated to complement the physics-based Gardiner equation to improve the accuracy of springback effect estimation, which is then incorporated into nonlinear model predictive controller to determine the optimal sequence of bending inputs to achieve geometric conformity. Evaluation using a simulated environment for bending confirms the effectiveness of the developed approach.