Finite Element Modeling for Virtual Design to Miniaturize Medical Implants Manufactured of Nanostructured Titanium with Enhanced Mechanical Performance

Digital technologies to unlock safe and sustainable opportunities for medical device and healthcare sectors with a focus on the combined use of digital twin and extended reality applications: A review

Medical devices have increased in complexity where there is a pressing need to consider design thinking and specialist training for manufacturers, healthcare and sterilization providers, and regulators. Appropriately addressing this consideration will positively inform end-to-end supply chain and logistics, production, processing, sterilization, safety, regulation, education, sustainability and circularity. There are significant opportunities to innovate and to develop appropriate digital tools to help unlock efficiencies in these important areas. This constitutes the first paper to create an awareness of and to define different digital technologies for informing and enabling medical device production from a holistic end-to-end life cycle perspective. It describes the added-value of using digital innovations to meet emerging opportunities for many disposable and reusable medical devices. It addresses the value of accessing and using integrated multi-actor HUBs that combine academia, industry, healthcare, regulators and society to help meet these opportunities. Such as cost-effective access to specialist pilot facilities and expertise that converges digital innovation, material science, biocompatibility, sterility assurance, business model and sustainability. It highlights the marked gap in academic R&D activities (PRISMA review of best publications conducted between January 2010 and January 2024) and the actual list of U.S. FDA's approved and marketed artificial intelligence/machine learning (AI/ML), and augmented reality/virtual reality (AR/VR) enabled-medical devices for different healthcare applications. Bespoke examples of benefits underlying future use of digital tools includes potential implementation of machine learning for supporting and enabling parametric release of sterilized products through efficient monitoring of critical process data (complying with ISO 11135:2014) that would benefit stakeholders. This paper also focuses on the transformative potential of combining digital twin with extended reality innovations to inform efficiencies in medical device design thinking, supply chain and training to inform patient safety, circularity and sustainability.

Digital-Twin-Driven Intelligent Insulated-Gate Bipolar Transistor Production Lines

With the rapid development of novel energy vehicles, power generation, photovoltaics, and other industries, power electronic devices have gained considerable attention. Insulated-gate bipolar transistors (IGBTs) have been widely used in those fields. With the emergence of intelligent manufacturing concepts such as Germany's "Industry 4.0" and China's "Made in China 2025", conventional manufacturing which needs to be upgraded with higher efficiency and yield is rapidly pivoting toward digitalization and intelligence. The digital twin methodology has been extensively used in various industries for constructing virtual models of physical entities, facilitating real-time data interconnection to reduce costs and improve efficiency. This study proposes a modular intelligent IGBT production line based on the digital twin. Real-time data are transmitted from a physical line to a digital line for storage and analysis. The digital line is visualized, and an intelligent management platform containing multiple functions is developed. Additionally, a process simulation database is established to obtain the optimal process parameters. Numerous quality issues that can arise during each process of IGBT packaging are addressed using a problem-solving approach based on the digital twin methodology. Consequently, this digital-twin-based IGBT intelligent production line effectively enhances yield rates and efficiency. IGBT modules with various packaging forms such as ACF, ACE, and ACD are manufactured.

Toward Digital Twin Development for Implant Placement Planning Using a Parametric Reduced-Order Model

Real-time stress distribution data for implants and cortical bones can aid in determining appropriate implant placement plans and improving the post-placement success rate. This study aims to achieve these goals via a parametric reduced-order model (ROM) method based on stress distribution data obtained using finite element analysis. For the first time, the finite element analysis cases for six design variables related to implant placement were determined simultaneously via the design of experiments and a sensitivity analysis. The differences between the minimum and maximum stresses obtained for the six design variables confirm that the order of their influence is: Young's modulus of the cancellous bone > implant thickness > front-rear angle > left-right angle > implant length. Subsequently, a one-dimensional (1-D) CAE solver was created using the ROM with the highest coefficient of determination and prognosis accuracy. The proposed 1-D CAE solver was loaded into the Ondemand3D program and used to implement a digital twin that can aid with dentists' decision making by combining various tooth image data to evaluate and visualize the adequacy of the placement plan in real time. Because the proposed ROM method does not rely entirely on the doctor's judgment, it ensures objectivity.