Improving 3d-printed Patient-matched Implants with Digital Twin Technology for Metrology

Additive manufacturing (AM) enables the creation of highly customizable, patient-specific designs with morphologies that optimize osteogenesis, particularly in medical implants. The control of lattice structures has been shown to enhance patient outcomes post-surgery, but the osseointegration process is significantly influenced by the geometric characteristics and porosity of the 3D-printed lattice. Individualized medical approaches are often subject to high variability in production, presenting challenges for implementing effective quality control checks. Traditional measurement systems struggle to inspect complex geometries, particularly the internal structures, where flaws and porosity can occur during the printing process. Recent advancements in computed tomography (CT) technology provide a solution by allowing the production of a digital twin of the component, enabling the evaluation of structural integrity, porosity, roughness of internal surfaces, and detection of internal defects and impurities. High-energy X-ray sources facilitate non-destructive scanning of large and dense AM parts, making CT the preferred method for inspecting and measuring AM products. This session will discuss how modern X-ray CT and 3D X-ray microscopy (XRM) techniques can be utilized not only for the characterization of AM parts but also for monitoring the AM process at various stages. Additionally, the application of deep learning (DL) techniques to enhance measurement throughput and improve image quality will be demonstrated. The benefits of these methodologies include improved fit, enhanced functionality, reduced surgical time, faster recovery, minimized risk of complications, reduced bone loss, and increased patient satisfaction.

Presentation Stage: Optimization — Maximizing Performance and Efficiency