Strategic initiatives

During 2017, IMDEA Materials 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, the development of damage-tolerant metallic alloys for additive manufacturing and the application of machine learning algorithms to smart manufacturing of composites processes using Industry 4.0 principles. 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

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.