During the last year, IMDEA Materials has launched a series of strategic internal initiatives in order to further advance the research lines of the Institute and promote internal collaboration between research groups. These three initiatives comprise the design of structural fire-safe batteries. Below, you can find a brief description of the goals of each strategic project.
Towards fire-safe structural batteries
The goal of the project is the development of new battery concepts combining: high efficiency, light-weight, augmented mechanical properties (structural, toughness) and improved fire-safety. This will be achieved through the combination of expertise of different research groups in molecular design, synthesis of advanced nanostructured current collectors and active materials, development of fire-retardants and polymer electrolytes, device fabrication and in-situ characterisation, and multi-physics study of laminated structures and devices.
Damage-tolerant additive manufacturing of metallic alloys
The objective of this project is the development of novel additive manufacturing (AM) strategies for metallic alloys that combine the benefits of this processing route with a damage tolerant behaviour. The combination of the expertise of different research groups in design and fabrication of metallic powders, process monitoring, multi-physics modelling of AM, multiscale modelling of solidification and high throughput characterisation techniques, will be a key factor for the success of this project.
Simulation-based smart manufacturing of composites
Industry 4.0 is one of the main trends in industrial manufacturing of all kinds of products. Regarding composites, the development of smart manufacturing strategies to automatically detect and recognize processing disturbances occurring during the manufacturing process by means of resin injection and infusion might be an interesting approach. Signals acquired using a sensor network will help to detect failure patterns and decide the appropriate corrective actions in order to reduce costs and part rejection during manufacturing.
The nature of this project requieres the use of machine learning algorithms in combination with multiphysics simulations to help to detect processing disturbance patterns occurring during manufacturing. These algorithms are trained using experimental results acquired with advanced in-situ sensoring as well as synthetic data generated from fluid flow simulations.