Development of an Automatic Robotic Procedure for Machining of Skull Prosthesis

The project presented in this paper develops within the field of automation in the medical-surgical sector. It aims at automating the process for the realization of prosthetic devices for the skull in cranioplasty, following a craniotomy intervention for brain tumor removal. The paper puts emphasis on the possibility to create the prosthetic device in run-time during the surgery, in order to ease the work that surgeons have to do during the operation. Generally, a skull prosthesis is realized before the day of the intervention, based on the plan of the medical operation, on the results of computed tomography, and through image processing software. However, after the surgery is performed, a non-negligible geometrical uncertainty can be found between the part of the skull actually removed and the cut planned during the preliminary analysis, so that the realized prosthesis (or even the skull, at worse) may need to be retouched. This paper demonstrates the possibility to introduce a fully automated process in a hospital environment, to manufacture in runtime the prosthetic operculum, relying on the actual geometry of the incision of the skull detected during the intervention. By processing a 3D scan of the skull after the craniectomy, a digital model of the prosthesis can be created and then used as an input to generate the code to be run by a robotic system in charge of the workpiece machining. Focusing on this second step, i.e., the manufacturing process, the work describes the way the dimensions of the raw material block are automatically selected, and the way robot trajectories for milling operation are automatically generated. Experimental validation demonstrates the possibility to complete the prosthesis within the surgery time, thus increasing the accuracy of the produced prosthesis and consequently reducing the time needed to complete the operation.

The Effect of Material and Geometrical Uncertainty on the Homogenized Properties of Graphene Sheet-Reinforced Composites

This paper focuses on the computational homogenization of graphene sheet-reinforced composites with randomly dispersed inclusions and uncertainty in the constituent materials. Material uncertainty of the matrix and of the graphene inclusions are considered separately and their relative effect on the homogenized properties is assessed. The uncertainty in the inclusion material is due to structural defects of the graphene lattice and is taken into account using random variables for each component of the elasticity matrix. Moreover, Monte Carlo simulation is used to extract the statistical characteristics of the homogenized properties of the composite material. The results lead to useful conclusions regarding the effect of material and geometrical uncertainty on the macroscopic properties of graphene sheet-reinforced composites.