The 1st MIDIH Open Call closed on June 29th, 2018. The brief public report about the recipients of financial support is available here. Further information about the development of each one of the selected experiments is available at the Open Call 1 winners page.
Project MIDIH Manufacturing Industry Digital Innovation Hubs, co-funded from the European Union's Horizon 2020 research and innovation programme under grant agreement No 767498, foresees as an eligible activity the provision of financial support to third parties, as a means to achieve its own objectives.
MIDIH Call-1 targets the development of data driven applications, by IT SMEs as technology providers, and experiments in CPS/IoT by Manufacturing SMEs.
The open call aims at complementing functionalities around MIDIH reference architecture and performing experiments in CPS/IOT based on the components provided by the architecture. The experiments must cover one of the three main scenarios: Smart Factory or Smart Product or Smart Supply chain.
The types of activities to perform that qualify for receiving financial support are data driven experiments in CPS/IoT under the following topics:
Technological topics which address technologies around the MIDIH architecture
Expected applicants are IT SMEs as technology providers
T1. Modeling and Simulation innovative HPC/Cloud applications for highly personalised Smart Products
The Smart Products MIDIH reference architecture defines reference functions and reference implementations for innovative applications acquiring and processing data from the Product Lifecycle, from its design to its operations to its end of life. Modelling and Simulating complex one-of-a-kind products in the different configurations (e.g. as-designed. as-manufactured, as-maintained, as-recycled or re-manufactured) requires the availability of huge and sophisticated computational IT resources, that just modern Cloud-HPC datacenters could offer.
The T1 topic looks for product-oriented industrial modelling & simulation IT experiments, which are using the MIDIH "Data in Motion" and "Data at Rest" architectures and reference implementations and the MIDIH Data Infrastructures. Candidates are required to provide advanced algorithms / applications based on the MIDIH architecture and to provide the correspondent datasets to be experimented in MIDIH HPC/Clouds
T2. Smart Factory Digital Twin models alignment and validation via edge clouds distributed architectures
Edge / Fog computing reference architectures and distributed local clouds frameworks aim at inserting a new computational layer between the Real World and the Cloud. Smart factory Digital Twins are digital representations of a real world artefact in a production site (a machine, a robot, or even the whole production line). Traditionally such models run on the cloud but when real-time (or near real time) performance is required, they can be moved and deployed on a reduced scale closer to the real world.
The T2 topic looks for factory-oriented Digital Twin IT experiments, which are using the MIDIH "edge / fog" computing architecture and reference implementations and the MIDIH Didactic Factories in Milano and Bilbao. Candidates are required to provide advanced Factory digital models and to deploy them onto the MIDIH edge/fog framework available in our two didactic factories.
T3. Advanced applications of AR / VR Technologies for Remote Training / Maintenance Operations (Smart Product and Smart Factory)
Virtual and Augmented reality applications are suitable to enhance both Smart Factory and Smart Product scenarios. In a Smart Factory scenarios, production systems, machineries, robots, warehouses, AGVs need to be properly virtualised, while in a Smart Product scenarios, virtual models are needed for complex products such as airplanes, vessels, trucks. Typical applications are concerned with remote training, virtual design and commissioning, maintenance operations involving both engineers, workers and even citizens.
The T3 topic looks for product-oriented or factory-oriented virtual / augmented reality IT experiments, which are using the MIDIH "Data in Motion" and "Data at Rest" architectures and reference implementations and the MIDIH Training Facilities. Candidates are required to provide advanced VR/AR applications based on the MIDIH architecture and to experiment such systems in one of our two Training Factories in Milano and Bilbao.
T4. Machine Learning and Artificial Intelligence advanced applications in Smart Supply Chains management and optimisation
According to EC Digitising EU Industry communication and subsequent working groups (especially the WG 2 about Digital Platforms for Manufacturing), Industrial IoT, Industrial Analytics and Artificial Intelligence are the three major pillars for Industry 4.0 Digital Transformation. MIDIH is focussing on providing Open Source "Data in Motion" and "Data at Rest" reference implementations as development (API and SDK) platforms for innovative applications. The MIDIH Smart Supply Chain scenario is particularly suitable for advanced ML /AI distributed applications due to its inherent heterogeneity of models, ontologies, systems which makes it very difficult for a mere statistical Data Analytics solution to meet its requirement.
The T4 topic looks for ML/AI applications on multi-stakeholders' owned heterogeneous datasets justifying Data Sovereignty and Smart Contracts requirements. Optionally, MIDIH could also provide candidates with the needed IoT-Cloud Infrastructure (SIEMENS MINDSPHERE based) in order for them to join the MINDAPPS Business Ecosystem.
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
Expected applicants are manufacturing SMEs
E1. Integrating CPS / IOT subtractive production technologies in Additive Manufacturing experimental facilities
Additive Manufacturing includes different technologies for products manufacturing through the addition of layers of materials (polymer, metals, composites or ceramics) to obtain complex shapes, functional or semi functional prototypes from data models (typically CAD).
The E1 topic looks for CPS/IOT data-driven experiments to explore the design challenges and opportunities of additive manufacturing combined with traditional subtractive technologies, aspects of products customization, rapid manufacturing, design concepts, assembly strategies, combinations of components, cybersecurity etc. Experiments must use the MIDIH reference architectures and reference implementations and the MIDIH Data Infrastructures.
In alignment with AMABLE, the I4MS project which facilitates digital design and solution for secure data chain in additive manufacturing, experiments results will be shared publicly in dissemination events and through the I4MS tools.
E2. Integrating CPS / IOT factory automation technologies in Robotics experimental facilities
Robots are used in manufacturing to execute mainly these types of operations: material handling (pick up and place, movements), processing operations (tool manipulation, welding), assembly and inspection. Current challenges for robotics in manufacturing are related to efficiency, human-robot collaboration, and cognitive operations.
The E2 topic looks for CPS/IOT data-driven experiments for sensor data collection, data analytics, and machine learning for the implementation of factory automation technologies supported by robotics which must use MIDIH reference architectures and reference implementations and the MIDIH Data Infrastructures.
Candidates are required to provide experiments based on the MIDIH architecture and to provide the correspondent datasets to be experimented in MIDIH HPC/Clouds.
In alignment with Horse, the I4MS project which proposes a flexible model of smart factory involving collaboration of humans, robots, AGV’s (Autonomous Guided Vehicles) and machinery in the manufacturing environment, experiments results will be shared publicly in dissemination events and through the I4MS tools.
E3. Integrating CPS / IOT discrete manufacturing technologies in Process Industry experimental facilities
The manufacturing industry can essentially be classified into two main categories: process industry and discrete product manufacturing. The process industry transforms material resources into a new material with different physical and chemical properties. This material is then usually shaped by discrete manufacturing into an end user product or intermediate component.
The E3 topic looks for CPS/IOT data-driven experiments involving all actors along the full value chain – from different types of raw material suppliers, through industrial transformation into intermediate products and applications, with the goal of reducing the environmental footprint and increase industrial efficiency. The experiments must use MIDIH reference architecture and reference implementations and the MIDIH Data Infrastructures.
Candidates are required to provide experiments based on the MIDIH architecture and to provide the correspondent datasets to be experimented in MIDIH HPC/Clouds.
In alignment with SPIRE, the EU Public-Private Partnership dedicated to innovation in resource and energy efficiency enabled by the process industries, experiments results will be shared publicly in dissemination events and through the SPIRE tools.
E4. Integrating CPS / IOT factory logistics technologies in Warehouse management experimental facilities
CPS/IoT play a fundamental role in the factory internal logistics: innovative IT applications need to be developed specifically for planning, scheduling and monitoring raw materials and finite products inside the production system.
The E4 topic looks for CPS/IOT data-driven experiments involving the integration of the different actors and stakeholders of the supply chain that will guarantee a total coordination and alignment between all the value chain phases. The experiments must use MIDIH reference architecture and reference implementations and the MIDIH Data Infrastructures.
Candidates are required to provide experiments based on the MIDIH architecture and to provide the correspondent datasets to be experimented in MIDIH HPC/Clouds.
In alignment with L4MS, the I4MS project that will develop deployment of small and flexible logistics solutions to make logistics automation extremely attractive for manufacturing SMEs, experiments results will be shared publicly in dissemination events and through the I4MS tools.
Identifier: MIDIH OC1
Call title: First call for MIDIH: Data driven applications and experiments in CPS/IOT
Project full name: Manufacturing Industry Digital Innovation Hubs
Acronym: MIDIH
Grant agreement number: 767498
Publication Date: 29th March 2018
Deadline: 29th June 2018 , at 17:00 Brussels local time
Expected duration of participation: 6 Months
Indicative budget for MIDIH Call-1: € 960,000
Maximum funding request per proposal: € 60,000
Submission language: English
Internet address for full open call information: midih.eu/open-call.php
Submission site: https://midih.ems-innovalia.org/
A contact tool is available inside the submission site.