In recent years, thanks to the development of additive manufacturing techniques, pros-thetic surgery has reached increasingly cutting-edge levels, revolutionizing the clinical course of patients suffering from joint arthritis, rheumatoid arthritis, post-traumatic arthrosis, etc. This work aims to evaluate the best materials for prosthetic surgery in hip implants from a tribological and mechanical point of view by using a machine-learning algorithm coupling with multi-body modeling and Finite Element Method (FEM) simulations. The innovative aspect is represented by the use of machine learning for the creation of a humanoid model in a multibody software environment that aimed to evaluate the load and rotation condition at the hip joint. After the boundary conditions have been defined, a Finite Element (FE) model of the hip implant has been created. The material properties and the information on the tribological behavior of the material couplings under investigation have been obtained from literature studies. The wear process has been investigated through the implementation of the Archard’s wear law in the FE model. The results of the FE simulation show that the best wear behavior has been obtained by CoCr alloy/UHMWPE coupling with a volume loss due to a wear of 0.004 ${\mathsf{\mu }\mathrm{m}}^3$ at the end of the simulation of ten sitting cycles. After the best pairs in terms of wear has been established, a topology optimization of the whole hip implant structure has been performed. The results show that, after the optimization process, it was possible to reduce implant mass making the implant 28.12% more lightweight with respect to the original one.

The knee is one of the joints most vulnerable to disease and injury, particularly in athletes and older adults. Surface temperature monitoring provides insights into the health of the analysed area, supporting early diagnosis and monitoring of conditions such as osteoarthritis and tendon injuries. This study presents an innovative approach that combines infrared thermography techniques with a Resnet 152 (DeepLabCut based) to detect and monitor temperature variations across specific knee regions during repeated sit-to-stand exercises. Thermal profiles are then analysed in relation to weight distribution data collected using a Wii Balance Board during the exercise. DeepLabCut was used to automate the selection of the region of interest (ROI) for temperature assessments, improving data accuracy compared to traditional time-consuming semi-automatic methods. This integrative approach enables precise and marker-free measurements, offering clinically relevant data that can aid in the diagnosis of knee pathologies, evaluation of the rehabilitation progress, and assessment of treatment effectiveness. The results emphasize the potential of combining thermography with DeepLabCut-driven data analysis to develop accessible, non-invasive tools for joint health monitoring or preventive diagnostics of pathologies.

The use of thermal analysis in dental implant procedures marks a significant advancement, allowing the study of temperature changes during implant placement. Research has demonstrated the value of thermal imaging in assessing tissue health, detecting early damage, and understanding biomechanical interactions during these procedures. The study involved testing the heat generated when screwing titanium dental implants (Diagram, Schutz‐Dental GmbH, Rosbach, Germany) into resin D3 bone‐like blocks that mimic bone′s mechanical properties. Various screwing protocols were evaluated, considering the presence or absence of lubricant, the type of instrument used (manual or micromotor), and the number of revolutions. Temperature changes on the surface of the implant were monitored with a microbolometric infrared camera, FLIR A40. In total, 24 implants were placed using different protocols. Results indicated that the highest temperature rises occurred during manual screwing without physiological solution, especially in undersized preparation sites where friction was increased. Adding lubrication significantly lowered the surface temperature, although water buildup affected measurement accuracy. Overall, the findings showed that screwing speed, lubrication, and preparation size influence thermal behavior during implant placement. The study concludes that thermal analysis is essential for understanding dental implant procedures, with thermocameras providing a noninvasive method for examining thermal dynamics during insertion. This can lead to improved implant designs and techniques that reduce thermal damage to surrounding tissues, thereby enhancing success rates and safety. The research emphasizes the need for further in vivo studies, the development of new materials for drills and implants, and personalized drilling protocols based on patient‐specific factors. It also highlights the importance of education and training on thermal analysis for dental professionals and considers regulatory and ethical issues in advancing these technologies.

AISI 304L is a low-carbon austenitic stainless steel widely used in common engineering applications, according to its good mechanical properties such as high ductility, corrosion resistance, and easy weldability. This material is adopted in various environmental conditions, such as pressure vessels or in pipelines where fluid temperature variations occur and affect its mechanical behavior. Nowadays, the applications of advanced investigation methodologies, such as infrared thermography, are widely adopted for the rapid analysis of the fatigue properties of common materials, especially metals. In this work, fatigue properties were evaluated on AISI304L specimens having two different microstructural states, one in the as-received condition and the other one after solution annealing. Fatigue test campaigns were performed with the application of the Risitano Thermographic Method. The microscopic analysis highlights the differences in the microstructure before and after the heat treatment. The characteristics of the microstructures are the main ones responsible for the different fatigue behavior obtained with experimental tests. The Thermographic Method proved to be a valid rapid approach for the fatigue analysis, confirming that the annealing process led to an improvement in the fatigue strength of the material.

The accurate prediction of fatigue life in ship structures is essential for ensuring operational safety and long-term performance in harsh marine environments. This study presents a contribution to the MADELEINE Project (MeAsurement of sea state conDitions to monitor the ship fatiguE Life and Enhance IN real-time conditions the safEty of navigation), which aims to develop advanced tools for fatigue monitoring and prediction. In this work, numerical hydrodynamic simulations were performed to evaluate the dynamic response of a container ship subjected to wave-induced loading across multiple sea states and operating conditions. Response Amplitude Operators (RAOs) for vertical bending moments and shear forces were computed for various wave directions and ship speeds, and results were compared with classification society standards. Using North Atlantic Sea state data, fatigue stresses were estimated and used to evaluate fatigue life through a probabilistic approach based on Palmgren-Miner’s rule. Focus was given to welded joints, which are known to be critical fatigue-prone areas due to stress concentrations. The findings reveal the limitations of simplified regulatory methods and demonstrate the potential of direct numerical simulations in providing more accurate fatigue predictions. This study lays the groundwork for future integration of finite element analysis (FEA), with the goal of enabling real-time structural health monitoring and supporting predictive maintenance in maritime operations