This showcase entails CMCL’s chemistry knowledge graph (KG) and provides a consistent framework to store, access and interpret vastly growing chemical data, marine emissions data, location data and air quality data, in an intelligent manner using the DOME 4.0 ecosystem.

The ambitious CO2 reduction targets in the automotive industry make the use of lightweight Fibre Reinforced Polymeric materials (FRPs) very attractive.

Polymer additives are used to obtain polymeric materials with functionalities tailored to the application, such as corrosion protection.

Predicting the fatigue life of adhesively bonded structures is an important aspect of the design and process optimisation in many industrial applications, including aircraft, railway and other engineering sectors.

The MARKET4.0 project coordinated by INTRA aims at implementing an e-marketplace for industrial equipment trading based on a peer-to-peer Industrial Data Space (IDS)-based infrastructure, providing key enabling technologies in terms of communication and collaboration in advanced multisided platforms.

Materials Cloud is a web platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data.

This use case concerns data-oriented services for process data technology in liquid-phase thermodynamics, improving the production and design of formulated products in fast-moving consumer goods sectors (industrial end user: Unilever).

Smart manufacturing aims at plants with fully computerised and automated production processes. Such degree of automation requires constant analytics manufacturing assets that includes monitoring, diagnostics, and optimisation of production assets such assembly lines and manufacturing robots.

This showcase deals with the virtual development of composite materials in a machine learning framework. Such materials are of key importance, for instance, for the automotive market. However, to facilitate accessibility and broad adoption of advanced machine learning tools by companies developing materials, user-friendly, integrated workflows need to be developed.