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Overall Expected Results

Overall Expected Results

The key aim of DOME 4.0 is to deliver a multi-sided, secure marketplace ecosystem open to everyone and that facilitates B2B data transactions and enables data users/consumers, data owners/providers and digital service providers to trigger data-based innovation and create new or improved products, processes and services. DOME 4.0 also aims at transforming data into knowledge and intelligence assets, while ensuring that the sovereignty, security and provenance of the industrial data are managed optimally. For example, DOME 4.0 will be expected to produce guidance on aspects of data provenance capture, its associated spatial and temporal costs, and granularity. The adoption of widely agreed standardised data documentation for semantic interoperability using consensual ontology and taxonomy-based data representations in close cooperation with NMBP-39-2020-CSA and related initiatives is anticipated to yield a consistent information system that can be adopted by third parties. DOME 4.0’s focus on detailed business process models entails a) the technical aspects of the ecosystem including its running and maintenance and b) the transaction experience with its customers (data providers, data consumers and service providers). Thus, one anticipated result will be the business model for the sustainability of the open marketplace ecosystem beyond the funding period.

The nine B2B showcases will also offer quantitative measures for assessing the in-project exploitation of DOME 4.0. These showcases will be gauged in terms of the improved effectiveness of decision making, improved quality by design and reduction of cost and time to market. The targeted B2B showcases are given in the table below.

 

NO.

B2B SHOWCASES

DATA SOURCES

PARTNERS

INDUSTRIAL SECTORS

1

Chemical kinetics Knowledge Graph (KG) – marine, air quality

 

Ontokin KB: species, thermodynamics, chemical kinetics, sensors and geolocation data

CMCL

 

MARINE/SHIPPING

ENVIRONMENTAL

NANOPARTICLES

2

Light weight construction – fibre reinforced plastics

Laboratory experiments, multiscale models

FRAUNHOFER,

BOSCH

PLASTICS

3

Polymeric additives for coatings: anti-corrosion

Thermodynamic, Laboratory Regulatory, Modelling

FRAUNHOFER,

SISW

POLYMERS

4

Structural adhesives: Fatigue behaviour

Experimental data, MatWeb: Materials property data

FRAUNHOFER,

SISW

 

ADHESIVES

5

Production equipment tools and service catalogues (metals, plastics, high-tech)

Semantic data repositories of MARKET4.0

 

INTRA

MANUFACTURING

6

Turnkey services & custom workflows integrating simulations and data

Materials Cloud (Open Science, FAIR data principles)

EPFL

MATERIALS

7

Formulated consumer

products

gPROMS (PSE), molecular simulation (UKRI), Cheméo (Céondo), and REFPROP (NIST)

UKRI

 

CHEMICAL PROCESSES

AND MATERIALS

8

Semantic Analytics of Manufacturing Assets

Bosch I4.0 Knowledge Graph, manufacturing production data

 

BOSCH

SMART MANUFACTURING

9

Virtual development of composite materials

Experimental data, material

data sheets

SISW

COMPOSITE MATERIALS

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