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MIDIH Second Open Call Data driven applications and experiments in CPS/IoT
Call identifier: MIDIH-OpenCall 2
Publication date: 2019-04-08
Status: Closed
Opening date: 2019-05-06 09:00:00 (Brussels time)
Closing date: 2019-08-06 17:00:00 (Brussels time)
Call general detailsThematic areasSupporting documentation
Call Summary
MIDIH Call-2 targets the development of data driven applications, preferably by IT SMEs as technology providers, and experiments in CPS/IoT, preferably 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.
Call Keywords
IoT
BigData
CPS
I4MS
Techonological Topics
Candidates should provide applications to complement fuctionalities around MIDIH reference architecture.
Topics:
T1. Modeling and Simulation innovative HPC/Cloud applications for highly personalised Smart ProductsT1. Modeling and Simulation innovative HPC/Cloud applications for highly personalised Smart Products
The 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.
HPCModellingSimulationSmart ProductIoTCPS
T2. Smart Factory Digital Twin models alignment and valid. via edge clouds distributed architecturesT2. Smart Factory Digital Twin models alignment and valid. 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-worldartefact 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. Security and real-time capabilities are strong requirements in such a context.
Digital TwinModellingIoTBigDataCPS
T3. Advanced applications of AR / VR Technologies for Remote Training / Maintenance OperationsT3. Advanced applications of AR / VR Technologies for Remote Training / Maintenance Operations
Virtual and Augmented reality applications are suitable to enhance both Smart Factory and Smart Product scenarios. In a Smart Factory scenario, production systems, machineries, robots, warehouses, AGVs need to be properly virtualised, while in a Smart Product scenario, virtual modelsare 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.
T4. Machine Learning and Artificial Intelligence advanced apps in Smart Supply Chains mgt and optim.T4. Machine Learning and Artificial Intelligence advanced apps in Smart Supply Chains mgt and optim.
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 scenarios aresuitable 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.
Candidates should propose 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.
Topics:
E1. Integrating AM into legacy production system for experiments with CPS/IOT production technol.E1. Integrating AM into legacy production system for experiments with CPS/IOT production technol.
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 legacy production systems in the followingaspects: 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.
E2. Integrating CPS / IOT technologies to bridge factory automation and roboticsE2. Integrating CPS / IOT technologies to bridge factory automation and robotics
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.
E3. Integrating CPS / IOT discrete productiontechnologies in Process IndustryE3. Integrating CPS / IOT discrete productiontechnologies in Process Industry
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.
CPS/IoT play a fundamental role in the factory internaland externallogistics: innovative IT applications need to be developedspecifically 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 supplychain 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.