Manufacturing Technology Centre (United Kingdom)

Abstract The durability and long-term survival of medical implants are major concerns for patients and surgeons. The laser shock peening (LSP) process can enhance the implant’s in-vivo lifespan through the compressive residual stress introduced on the implant’s surface. In the current research, a novel hybrid machine learning (ML) prediction tool was developed to calculate LSP induced residual stresses. Laser energies of 3 J, 5 J, 7 J with three overlapping levels of 33%, 50% and 67% with a constant laser spot diameter were introduced to a Ti-6Al-7Nb titanium hip implant material. A three-dimensional finite element model was developed incorporating an explicit dynamic analysis to capture the dynamic material response during the LSP process. Furthermore, The random forest ML algorithm was adapted so that the laser energy and overlapping were set as input parameters, while the associated residual stresses were set as output parameters. The mean squared error (MSE), root MSE and coefficient of determination for testing data sets concerning residual stress were 112.9%, 10.6% and 97%, respectively. The FEM and ML results both show a good agreement with the experimental data. The new proposed approach allows development of ML models even with a limited experimental data. The accuracy and performance of each model are discussed and the limitations are addressed. The approach carried out in this paper enables subsequent predictions of surface treatment techniques of implants through residual stress fields and can be applied to a wide range of applications.

gVirtualXray (gVXR) is an open-source framework that relies on the Beer-Lambert law to simulate x-ray images in real time on a graphics processor unit (GPU) using triangular meshes. A wide range of programming languages is supported (C/C++, Python, R, Ruby, Tcl, C#, Java, and GNU Octave). Simulations generated with gVXR have been benchmarked with clinically realistic phantoms (i.e. complex structures and materials) using Monte Carlo (MC) simulations, real radiographs and real digitally reconstructed radiographs (DRRs), and x-ray computed tomography (CT). It has been used in a wide range of applications, including real-time medical simulators, proposing a new densitometric radiographic modality in clinical imaging, studying noise removal techniques in fluoroscopy, teaching particle physics and x-ray imaging to undergraduate students in engineering, and XCT to masters students, predicting image quality and artifacts in material science, etc. gVXR has also been used to produce a high number of realistic simulated images in optimization problems and to train machine learning algorithms. This paper presents applications of gVXR related to XCT.

This study explores the fabrication and characterisation of 3D-printed polylactic acid (PLA) scaffolds reinforced with calcium hydroxyapatite (cHAP) for bone tissue engineering applications. By varying the cHAP content, we aimed to enhance PLA scaffolds' mechanical and thermal properties, making them suitable for load-bearing biomedical applications. The results indicate that increasing cHAP content improves the tensile and compressive strength of the scaffolds, although it also increases brittleness. Notably, incorporating cHAP at 7.5% and 10% significantly enhances thermal stability and mechanical performance, with properties comparable to or exceeding those of human cancellous bone. Furthermore, this study integrates machine learning techniques to predict the mechanical properties of these composites, employing algorithms such as XGBoost and AdaBoost. The models demonstrated high predictive accuracy, with R2 scores of 0.9173 and 0.8772 for compressive and tensile strength, respectively. These findings highlight the potential of using data-driven approaches to optimise material properties autonomously, offering significant implications for developing custom-tailored scaffolds in bone tissue engineering and regenerative medicine. The study underscores the promise of PLA/cHAP composites as viable candidates for advanced biomedical applications, particularly in creating patient-specific implants with improved mechanical and thermal characteristics.