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MIDIH (Manufacturing Industry Digital
Innovation Hub) and the opportunities for SMEs
Angelo Marguglio (ENG)
Susanne Kuehrer (EITD)
• MIDIH - Manufacturing Industry Digital Innovation Hubs
• Call FoF 12 - 2017
• Duration: 36 months (started in October 2017)
• Coordinator: Susanne Kuehrer, EIT Digital
• Budget: 8,524,832.50 Euro
• Funding: 7,999,157.50 Euro
• Funding for Third Parties: 1,920,000 Euro
MIDIH Project Factsheet
Strong Partnership
• 9 CPS/IOT Competence Centers
• 2 Teaching Factories POLIMI INNOVALIA
• 2 Regional Manufacturing Digital
Innovation Hubs IMR STRATH
• 3 Pan –European Digital Innovation Hubs
• EIT Digital, IDSA, FIWARE
• 3 Industrial case-study providers
• CR FIAT, IDSA, NECO
• 2 Open source digital platform providers
• ATOS, Engineering
• 2 IoT specialized SMEs
• NISSA, HOPU
I4MS: materializing DEI
for Manufacturing SMEs
CloudiFacturing
The DEI implementation:
four coordinated pillars
The DEI implementation:
four coordinated pillars
MIDIH Reference Architecture
MIDIH Reference
Implementations
ARROWHEAD
CLOUD
AS OSSR
FIWARE
Orion
Translator
System
Arrowhead Translator
+ NGSI plugin
FIWARE temp. sensor
Arrowhead Filter
service
Web interface
DataManager
[ INNO Didactic Factory ]
Multiple protocols
(Data Formats & Data Models
transformations)
DATA GATHERING
DATA MODELS & TRANSFORMATION
SIDAM: Smart Industry Data Models
A simplified taxonomy (ontology, IS-A hierarchy) of Smart Industry Objects (IoT Boards, IoT
Trackers, Containers, Trucks, …) in the chosen domain.
SCOPE DATA
Specific data for
Maintenance,
Planning, Quality, etc.
CONTEXT DATA
External sources
such as Smart City,
Environment, etc.
DATA AT REST
From ERP, PLM and
other systems
DATA IN MOTION
From Real World
such as Devices,
Machines, etc.
MIDIH Innovation
Lighthouse Pilots
MIDIH Innovation Boosters:
External Experiments
This project is funded by the European Union Framework Programme for Research and Innovation Horizon 2020 under Grant Agreement
No. 767498
MIDIH Coordinator: Susanne Kuehrer, EIT Digital, Email: susanne.kuehrer@eitdigital.eu, Project Homepage: www.midih.eu
Business Experiments in Cyber Physical Production Systems
MID3 Management Module
SOLUTION: ARCHITECTURE AND
COMPONENTS
MID3 Manufacturing Industry Data Driven Digital Twin
MANUFACTURING SME AND CHALLENGE
BENEFITS AND LESSON LEARNT
Digital Twin
implementation for the
I4.0Lab of
Polytechnic of Milan.
Evolving digital profile,
mirroring the real system
behaviour, for performance
prediction and optimization.
Real to digital mirroring means being able to
simulate the behavior of the real system with an
error < 10%
Real applications give immediate perception of all
the potentialities of I4.0 technologies.
The possibility to collaborate on the extension and
enhancement of open frameworks like Arrowhead
and Fiware creates value for the whole ecosystem.
OUTLOOK
ArrowHead Framework to interface the Digital Twin
Simulation with Discrete Manufacturing Machine Tools
and Robots of the Industrial Shop Floor
Improve the Digital Twin modularity level
Technology
Transfer System®
SMART FACTORY
AH Connector
Simulation
Engine
Plant model
Plant logics
3D
kinematics
OPCtoREST
ServiceProvider
Monitoring
engine
Temporary
parameters
store
Configuration and
simulation
management GUI
OPC-UA
Industrial Shop Floor
Move towards “Digital Twin as a service”
Authorization
System
Orchestration
System
ServiceRegistry
System
Arrowhead Framework
local cloud
CPS/IOT in MIDIH
THE WORLD WE WORK IN
12
HOW TO INTEGRATE THESE
SYSTEMS
13
FIWARE Smart Industry
Reference Architecture
SF
MIDIH
SMART FACTORY
INTEGRATED
ARCHITECTURE
Smart Factory, Smart Product & Smart Supply Chain Functionalities
MIDIH Integrated Platforms for Smart
Factory, Product & Supply Chain
• Collection and transfer of all types of data (Data Ingestion)
• Real-time analytics, processing huge amount of streaming data in order to predict and
detect events based on underlying patterns and correlations (processing DiM), applying
edge computing techniques
• Storage layer for persisting all type of data (past data, meta-data, models ) Data
Persistence.
• Data-analytics services on multidimensional and complex data, including exploratory
analysis, multivariate analysis, predictive analytics and deep learning (processing DaR)
• Visualization services to enable users to contextualize, understand and apply results for
better decision making
SP
MIDIH
SMART PRODUCT
INTEGRATED
ARCHITECTURE
• Collaboration between OEMs and subcontracts through standardized interfaces
• Global real-time visibility regarding production, inventory, and materials;
• Supply chain decision-making through advanced analytics and next generation optimization
software, allowing a quick response in supply chain planning’s;
• Provide mechanisms for secure data sharing based on digital identity, sharing policy,
sharing agreement and data certification.
MIDIH
SMART SUPPLY
CHAIN
INTEGRATED
ARCHITECTURE
SSC
THE PATH WE ARE FOLLOWING
CPS/IOT innovations
delivered in MIDIH
Industrial IoT and
Analytics
Platform
MIDIH4Data
Brownfield
Integration via
Open APIs
MIDIH4
Interoperability
Edge-oriented
Local Clouds for
Factory
Automation
MIDIH4Edge
• Definition and implementation of distributed cloud
architectures (such as Fog Computing, Local Clouds or
generically Edge-oriented), addressing the trade-off
between quality of the decisions and timeliness.
• Create interoperability among the existing edge
infrastructures frameworks such as Arrowhead, FogFlow,
FIWARE and 4diac.
• Reduce the effort required for access to shop floor
contextual data by improving integration of the MIDIH
platform with machine-level and unit-level control
software.
MIDIH4Edge: Objectives
MIDIH4Edge: Achievements
● The implementation of the MIDIH Edge Computing Node as part of
FogFlow project as a compliment of FIWARE (https://www.fiware.org/),
in order to have a highly distributed cloud architecture (such as Fog
Computing, Local Clouds or generically Edge-oriented)
● Arrowhead – FIWARE - 61499 interoperability
• Adding support for the FIWARE framework and its platforms through
the use of the Arrowhead Translator system.
• A Systems of Systems approach where Arrowhead Systems can search
for services using NGSIv2 and FIWARE
● Integration path between Arrowhead and 4diac
• Analysis of the applicability of 4diac as an open source factory data
provider via Arrowhead and/or OPC UA
● POLIMI Teaching Factory deployment integrating FogFlow, Arrowhead,
4diac and OPC UA
MIDIH4Interoperability:
Mastering the IIOT disruption
 Common technology that spans industries brings bold new approaches
and enables fast change
 The real value is a common framework that connects sensor to cloud,
integrated existing and heterogenous data sources, interoperates
between vendors, and spans industries
INTEROPERABILITY IS THE KEY
MIDIH4Interoperability:
FIWARE – OPC UA
MIDIH4Interoperability:
FI-MIND Bridge
Smart Industry DAta Model
(SIDAM)
Improving interoperability to
support more and more data to be
shared
Data Sovereignty
Ensuring a secure data exchange
among parties
MIDIH4Interoperability:
Main outcomes
MIDIH4Interoperability:
Smart Industry DAta Models
(SIDAM)
SCOPE DATA
Specific data for
Maintenance,
Planning, Quality, etc.
DATA AT REST
From ERP, PLM and
other systems
DATA IN MOTION
From Real World such
as Devices, Machines,
etc.
CONTEXT DATA
External sources such
as Smart City,
Environment, etc.
A simplified taxonomy (ontology, IS-A hierarchy) of Smart Industry Objects (IoT
Boards, IoT Trackers, Containers, Trucks, …) in the chosen domain
MIDIH4Interoperability:
SIDAM in operation
MIDIH4Data:
Increased data space
Talking machine
?????
MACHINE DATA
TRANSLATOR
MIDIH4Data:
Increased data space =>
Making order
Talking machine
?????
MACHINE DATA
TRANSLATOR
OPPORTUNITIES FOR SMEs
2nd Open Call
Timing and Budget
Publication Date: 6th May 2019
Deadline: 6th August 2019, at 17:00 Brussels local time
Expected duration: 6 Months
Total budget: € 960,000
Maximum funding request per proposal: € 60,000
Applicants single entities
Evaluation
Results
publication
Experiment
starting
September 2019 November 2019 December 2019
2nd Open Call
May-August
2019
Topics
• Technological topics which address
technologies around the MIDIH architecture
o Expected applicants are IT SMEs as technology
providers
• Experimentation topics must cover one of the
three main scenarios: Smart Factory, Smart
Product or Smart Supply chain. The usage of
components of the reference architecture is
mandatory
o Expected applicants are manufacturing SMEs
Technological topics
T1 Modeling and Simulation innovative HPC/Cloud
applications for highly personalised Smart
Products, Smart Factory and Smart Supply
Chain
T2 Smart Factory and Smart Product Digital Twin
models alignment and validation via edge
clouds distributed architectures
T3 Advanced applications of AR / VR Technologies
for Remote Training / Maintenance Operations
(Smart Product and Smart Factory)
T4 Machine Learning and Artificial Intelligence
advanced applications in Smart Product, Smart
Factory and Smart Supply Chains management
Experimental topics
E1 Integrating Additive Manufacturing into
legacy production system for experiments
with CPS / IOT production technologies
E2 Integrating CPS / IOT technologies to bridge
factory automation and robotics
E3 Integrating CPS / IOT discrete production
technologies in Process Industry
E4 Integrating CPS / IOT factory logistics
technologies in internal/external logistic
scenario
• Website:
https://midih.eu/opencall_2.php
• Guide for applicants:
https://midih.eu/documents/MIDIH%20OC%202_%20guide%20f
or%20applicants%20v1.0.pdf
• Application:
https://midih.ems-innovalia.org/
More information
Thanks for your attention!
Angelo Marguglio
angelo.marguglio@eng.it
Head of «Smart Industry and
Agrifood» Unit
MIDIH Technical Coordinator
Susanne Kuehrer
susanne.kuehrer@eitdigital.eu
Senior Project Manager
MIDIH Project Coordinator

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2019 06-19 EIT Digital industry event

  • 1. MIDIH (Manufacturing Industry Digital Innovation Hub) and the opportunities for SMEs Angelo Marguglio (ENG) Susanne Kuehrer (EITD)
  • 2. • MIDIH - Manufacturing Industry Digital Innovation Hubs • Call FoF 12 - 2017 • Duration: 36 months (started in October 2017) • Coordinator: Susanne Kuehrer, EIT Digital • Budget: 8,524,832.50 Euro • Funding: 7,999,157.50 Euro • Funding for Third Parties: 1,920,000 Euro MIDIH Project Factsheet
  • 3. Strong Partnership • 9 CPS/IOT Competence Centers • 2 Teaching Factories POLIMI INNOVALIA • 2 Regional Manufacturing Digital Innovation Hubs IMR STRATH • 3 Pan –European Digital Innovation Hubs • EIT Digital, IDSA, FIWARE • 3 Industrial case-study providers • CR FIAT, IDSA, NECO • 2 Open source digital platform providers • ATOS, Engineering • 2 IoT specialized SMEs • NISSA, HOPU
  • 4. I4MS: materializing DEI for Manufacturing SMEs CloudiFacturing
  • 5. The DEI implementation: four coordinated pillars
  • 6. The DEI implementation: four coordinated pillars
  • 8. MIDIH Reference Implementations ARROWHEAD CLOUD AS OSSR FIWARE Orion Translator System Arrowhead Translator + NGSI plugin FIWARE temp. sensor Arrowhead Filter service Web interface DataManager [ INNO Didactic Factory ] Multiple protocols (Data Formats & Data Models transformations) DATA GATHERING DATA MODELS & TRANSFORMATION SIDAM: Smart Industry Data Models A simplified taxonomy (ontology, IS-A hierarchy) of Smart Industry Objects (IoT Boards, IoT Trackers, Containers, Trucks, …) in the chosen domain. SCOPE DATA Specific data for Maintenance, Planning, Quality, etc. CONTEXT DATA External sources such as Smart City, Environment, etc. DATA AT REST From ERP, PLM and other systems DATA IN MOTION From Real World such as Devices, Machines, etc.
  • 10. MIDIH Innovation Boosters: External Experiments This project is funded by the European Union Framework Programme for Research and Innovation Horizon 2020 under Grant Agreement No. 767498 MIDIH Coordinator: Susanne Kuehrer, EIT Digital, Email: susanne.kuehrer@eitdigital.eu, Project Homepage: www.midih.eu Business Experiments in Cyber Physical Production Systems MID3 Management Module SOLUTION: ARCHITECTURE AND COMPONENTS MID3 Manufacturing Industry Data Driven Digital Twin MANUFACTURING SME AND CHALLENGE BENEFITS AND LESSON LEARNT Digital Twin implementation for the I4.0Lab of Polytechnic of Milan. Evolving digital profile, mirroring the real system behaviour, for performance prediction and optimization. Real to digital mirroring means being able to simulate the behavior of the real system with an error < 10% Real applications give immediate perception of all the potentialities of I4.0 technologies. The possibility to collaborate on the extension and enhancement of open frameworks like Arrowhead and Fiware creates value for the whole ecosystem. OUTLOOK ArrowHead Framework to interface the Digital Twin Simulation with Discrete Manufacturing Machine Tools and Robots of the Industrial Shop Floor Improve the Digital Twin modularity level Technology Transfer System® SMART FACTORY AH Connector Simulation Engine Plant model Plant logics 3D kinematics OPCtoREST ServiceProvider Monitoring engine Temporary parameters store Configuration and simulation management GUI OPC-UA Industrial Shop Floor Move towards “Digital Twin as a service” Authorization System Orchestration System ServiceRegistry System Arrowhead Framework local cloud
  • 12. THE WORLD WE WORK IN 12
  • 13. HOW TO INTEGRATE THESE SYSTEMS 13
  • 15. SF MIDIH SMART FACTORY INTEGRATED ARCHITECTURE Smart Factory, Smart Product & Smart Supply Chain Functionalities MIDIH Integrated Platforms for Smart Factory, Product & Supply Chain • Collection and transfer of all types of data (Data Ingestion) • Real-time analytics, processing huge amount of streaming data in order to predict and detect events based on underlying patterns and correlations (processing DiM), applying edge computing techniques • Storage layer for persisting all type of data (past data, meta-data, models ) Data Persistence. • Data-analytics services on multidimensional and complex data, including exploratory analysis, multivariate analysis, predictive analytics and deep learning (processing DaR) • Visualization services to enable users to contextualize, understand and apply results for better decision making SP MIDIH SMART PRODUCT INTEGRATED ARCHITECTURE • Collaboration between OEMs and subcontracts through standardized interfaces • Global real-time visibility regarding production, inventory, and materials; • Supply chain decision-making through advanced analytics and next generation optimization software, allowing a quick response in supply chain planning’s; • Provide mechanisms for secure data sharing based on digital identity, sharing policy, sharing agreement and data certification. MIDIH SMART SUPPLY CHAIN INTEGRATED ARCHITECTURE SSC
  • 16. THE PATH WE ARE FOLLOWING
  • 17. CPS/IOT innovations delivered in MIDIH Industrial IoT and Analytics Platform MIDIH4Data Brownfield Integration via Open APIs MIDIH4 Interoperability Edge-oriented Local Clouds for Factory Automation MIDIH4Edge
  • 18. • Definition and implementation of distributed cloud architectures (such as Fog Computing, Local Clouds or generically Edge-oriented), addressing the trade-off between quality of the decisions and timeliness. • Create interoperability among the existing edge infrastructures frameworks such as Arrowhead, FogFlow, FIWARE and 4diac. • Reduce the effort required for access to shop floor contextual data by improving integration of the MIDIH platform with machine-level and unit-level control software. MIDIH4Edge: Objectives
  • 19. MIDIH4Edge: Achievements ● The implementation of the MIDIH Edge Computing Node as part of FogFlow project as a compliment of FIWARE (https://www.fiware.org/), in order to have a highly distributed cloud architecture (such as Fog Computing, Local Clouds or generically Edge-oriented) ● Arrowhead – FIWARE - 61499 interoperability • Adding support for the FIWARE framework and its platforms through the use of the Arrowhead Translator system. • A Systems of Systems approach where Arrowhead Systems can search for services using NGSIv2 and FIWARE ● Integration path between Arrowhead and 4diac • Analysis of the applicability of 4diac as an open source factory data provider via Arrowhead and/or OPC UA ● POLIMI Teaching Factory deployment integrating FogFlow, Arrowhead, 4diac and OPC UA
  • 20. MIDIH4Interoperability: Mastering the IIOT disruption  Common technology that spans industries brings bold new approaches and enables fast change  The real value is a common framework that connects sensor to cloud, integrated existing and heterogenous data sources, interoperates between vendors, and spans industries INTEROPERABILITY IS THE KEY
  • 22. MIDIH4Interoperability: FI-MIND Bridge Smart Industry DAta Model (SIDAM) Improving interoperability to support more and more data to be shared Data Sovereignty Ensuring a secure data exchange among parties
  • 24. MIDIH4Interoperability: Smart Industry DAta Models (SIDAM) SCOPE DATA Specific data for Maintenance, Planning, Quality, etc. DATA AT REST From ERP, PLM and other systems DATA IN MOTION From Real World such as Devices, Machines, etc. CONTEXT DATA External sources such as Smart City, Environment, etc. A simplified taxonomy (ontology, IS-A hierarchy) of Smart Industry Objects (IoT Boards, IoT Trackers, Containers, Trucks, …) in the chosen domain
  • 26. MIDIH4Data: Increased data space Talking machine ????? MACHINE DATA TRANSLATOR
  • 27. MIDIH4Data: Increased data space => Making order Talking machine ????? MACHINE DATA TRANSLATOR
  • 29. Timing and Budget Publication Date: 6th May 2019 Deadline: 6th August 2019, at 17:00 Brussels local time Expected duration: 6 Months Total budget: € 960,000 Maximum funding request per proposal: € 60,000 Applicants single entities Evaluation Results publication Experiment starting September 2019 November 2019 December 2019 2nd Open Call May-August 2019
  • 30. Topics • Technological topics which address technologies around the MIDIH architecture o Expected applicants are IT SMEs as technology providers • Experimentation topics must cover one of the three main scenarios: Smart Factory, Smart Product or Smart Supply chain. The usage of components of the reference architecture is mandatory o Expected applicants are manufacturing SMEs
  • 31. Technological topics T1 Modeling and Simulation innovative HPC/Cloud applications for highly personalised Smart Products, Smart Factory and Smart Supply Chain T2 Smart Factory and Smart Product Digital Twin models alignment and validation via edge clouds distributed architectures T3 Advanced applications of AR / VR Technologies for Remote Training / Maintenance Operations (Smart Product and Smart Factory) T4 Machine Learning and Artificial Intelligence advanced applications in Smart Product, Smart Factory and Smart Supply Chains management
  • 32. Experimental topics E1 Integrating Additive Manufacturing into legacy production system for experiments with CPS / IOT production technologies E2 Integrating CPS / IOT technologies to bridge factory automation and robotics E3 Integrating CPS / IOT discrete production technologies in Process Industry E4 Integrating CPS / IOT factory logistics technologies in internal/external logistic scenario
  • 33. • Website: https://midih.eu/opencall_2.php • Guide for applicants: https://midih.eu/documents/MIDIH%20OC%202_%20guide%20f or%20applicants%20v1.0.pdf • Application: https://midih.ems-innovalia.org/ More information
  • 34. Thanks for your attention! Angelo Marguglio angelo.marguglio@eng.it Head of «Smart Industry and Agrifood» Unit MIDIH Technical Coordinator Susanne Kuehrer susanne.kuehrer@eitdigital.eu Senior Project Manager MIDIH Project Coordinator