Projects overview
SCOTT | Secure COnnected Trustable Things
01-05-2017
-31-10-2020
QU4LITY | Digital Reality in Zero Defect Manufacturing
01-01-2019
-31-07-2022
Details: RA/RM (basic standard) applied for QU4LITY-RA
Details: IoT RA
Details: AI, Framework for AI systems using ML
Details: IoT, interoperability framework
Details: Security RA
Details: Edge Computing
Details: General principles of the Digital Factory framework
Details: AAS, Interoperability
Details: OS: Robot Operating System
Details: IIRA
Details: Production system engineering; process quality and optimisation designer; distributed industrial-process measurement and control systems
Details: Qualit modelling and support of quality workflow scenarious
Details: Blockchain
Details: Information model (QIF) and data formed into XML instance files support the entire scope of model based definition manufacturing quality workflow
Details: Cloud Computing, Concepts
Details: Industrial IoT standards and roadmapping
Details: Qualit modelling and support of quality workflow scenarious
Details: RAMI4.0 - Digital twin, process optimization run-time
Details: IoT, Use cases
Details: AI, Use cases
Details: Quality,; guidance for onfiguration management - activity that applies technical and administrative direction over the life cycle of a product and service, its configuration identification and status, and related product and service configuration information.
Details: Quality, requirements for a quality management system
improve false positive rate by 20%. Measured as false positives rate, actual value is considered confidential.
Quality- Fall Off Rate, from 95% (as is) to 98,5% (to be)
Improve OEE (A) from 80% (as is) to 87% (to be)
Deviation on cycle-time, from 98% (as is) to 99% (to be)
Sintef delivered information to put “soft” part of organization also in daily management structure. Nowadays the KPI’s are hard technical related. Other topic is to use digital tools for operator- whiteboard sessions. People claim they are more digital oriented at home compared to work floor.
Stakeholder-training Logbook: No results obtained yet, as the implementation is not far enough to train stakeholders.
Improve OEE from 75% (as is) to 85% (to be)
Improve OEE (A) from 80% (as is) to 87% (to be)
Deviation on cycle-time, from 98% (as is) to 99% (to be)
Quality- Fall Off Rate, from 95% (as is) to 98,5% (to be)
ZDMP | Zero Defect Manufacturing Platform
01-01-2019
-30-06-2023
Details: IoT/Device Integration
Details: IoT/Device Integration
Details: IoT/Device Integration
Details: ---
Details: IoT Architecture
Details: Maintenance
Details: IoT/Device Integration
Details: IoT/Device Integration
Details: Enterprise Systems, Interoperability, Integration
Details: Enterprise Systems, Interoperability, Integration
Details: Enterprise Systems, Interoperability, Integration
Details: Industrial Automation Systems, Product Catalogues
Details: Information Management; Information Secutity
Details: ZDMP-IoT/Device Integration; Data interoperability, OPC ,I4.0
Details: Messaging, message Exchange
Details: Maintenance
Details: Maintenance
Details: none
EFPF (European Factory Platform) | European Connected Factory Platform for Agile Manufacturing
01-01-2019
-31-12-2022
MANUELA | Additive Manufacturing using Metal Pilot Line
01-10-2018
-31-03-2023
UPTIME | UNIFIED PREDICTIVE MAINTENANCE SYSTEM
01-09-2017
-28-02-2021
The economic impact of UPTIME is the most important one and can be seen at 2 levels :
- Short/mid-term impact: improvement of financial results of industrial companies and their competitiveness thanks to better operational performance (optimized maintenance and production)
- Mid/long term impact: the improved competitiveness will help to reconquer some market shares and then reinforce an offensive marketing strategy (low margin segments could be reprioritized)
PreCoM | Predictive Cognitive Maintenance Decision Support System
01-11-2017
-28-02-2021
SERENA | VerSatilE plug-and-play platform enabling remote pREdictive mainteNAnce
01-10-2017
-31-03-2021
SERENA aims towards the data-driven condition evaluation of machine and production equipment, which through machine learning techniques can provide insight in the remaining useful life of the equipment enabling the avoidance of production stops and thus reducing its overall costs. The combination of data-driven and physics based techniques is envisioned to increase the reliability of the prediction and contribute to a high perfromance production without undesired interruptions.
THe evalaution and assessment of the equipment condition through the predictive manitenance and data analytics of the SERENA project will move towards the preservation of the production equipment in normal workiong conditions ensuring high quality products.
SERENA solutions on predictive maintenance and maintenance-aware scheduling are expected to reduce the overall ratio of cost to perfromance by the on-time scheduling of maintenance operations with the minimum intervention to the production schedule.
Production equipment uses a number of process resources to operate such as water, air, lubricants, other. In time maintenance activities wnabled by the SERENA predictive maintenance platfrom and optimising the scheduling of maintenance operations can have a significant impact on the consumption of such resources when the equipment is not in proper working condition.
The prediction of maintenance needs of the production equipment thorugh the predictive analytics and scheduling of the SERENA project is expected to reduce the defective workpieces caused by manufacturing equipments not in proper working condition.
The predictive maintenance solutions of the SERENA project are expected to contribute to the sustainability of the production equipment ot proper functional condition thus reducing the energy consumption that is present when machine's are close to the end of their life or in a need of maintenance.
Indutrial equipment not in proper working condition consumes greater quantities of input and operational sources than normal. The SERENA data-driven condition evaluation and prediction of potential failures will enabled the sustainability of the production machines to proper operational condition, thus contributing to reduced process resources.
PROGRAMS | PROGnostics based Reliability Analysis for Maintenance Scheduling
01-10-2017
-31-03-2021
PROGRAMS solution will allow to gain a “10% increased in-service efficiency through reduced failure rates, downtime due to repair, unplanned plant or production system outages and extension of component life.”
•reduced failure rates:- analysis of real sensor data coupled with physically-based models allows to understand the real status of system components
- optimal maintenance strategies allows to perform maintenance before components crash
- precise determination of components RUL will allow to replace/repair components before critical downtime
- smart maintenance strategies allow to minimize maintenance impact on the production activities
- repair or replace of components before crash will lead to less unexpected components breakage
- rationalized maintenance crews deployments will reduce the number of unavailable spare parts or maintenance teams.
- shared maintenance information will reduce time for reacting to unplanned outages.
- Smart RULE algorithms will allow to exploit a component up to its full useful life
- tuning of processing parameters allows to use equipment for a longer time
All these aspects will reduce the costs related to maintenance activities, thus increasing the sustainability of the production process.
Precise determination of the RUL of components will allow to replace them before their status degrades production equipment performances beyond unacceptable levels.
Reducing failure rates and production equipment unavailability will improve factory productivity.
PROGRAMS solutions will allow a more widespread adoption of predictive maintenance as a result of the demonstration of more accurate, secure and trustworthy techniques at component, machine and system level. In fact one of the biggest obstacles is getting people to change long-held maintenance practices. PROGRAMS will:
- Demonstrate that, when correctly applied, the LCC of machines and components actually decreases when using PdM based technology, while the machine availability increases.
- Provide an easy to install solution able to detect the deterioration of performance (at component, machine and system level) and manage maintenance accordingly.
- Implement SotA security layers, showing that the security of maintenance data transmission methods is on par with widespread and accepted communication protocols, like the ones used for e-commerce.
PROGRAMS solution will increase accident mitigation capability”
- Component failures will be reduced by optimal maintenance strategies and analysis of real sensor data coupled with physically-based models .
- Maintenance efficiency will be increased by precisely pinpointing the components whose deterioration is or will impairing the system performance.
- PROGRAMS will allow to identify design problems or faulty components batches by comparing initial FMECA analysis with updated reliability information.
- A plant wide network will constantly update employees on maintenance activities and components degraded performances.
- The automatic tuning of control parameters will mitigate the impact of a damaged/worn components until maintenance can restore them to their optimal conditions.
L4MS | Logistics for Manufacturing SMEs
01-10-2017
-31-03-2021
MIDIH | Manufacturing Industry Digital Innovation Hubs
01-10-2017
-30-09-2020
I4MS-Go | I4MS Going to Market Alliance
01-09-2017
-29-02-2020
iDev40 | Integrated Development 4.0
01-05-2018
-31-10-2021
DELPHI4LED | From Measurements to Standardized Multi-Domain Compact Models of LEDs
01-06-2016
-30-09-2019
SWARMs | Smart and Networking UnderWAter Robots in Cooperation Meshes
01-07-2015
-31-07-2018
Met4FoF | Metrology for the Factory of the Future
01-06-2018
-31-05-2021
EUCoM | Standards for the evaluation of the uncertainty of coordinate measurements in industry
01-06-2018
-31-05-2021
SmartCom | Communication and validation of smart data in IoT-networks
01-01-2018
-01-01-2021
ROSSINI | RObot enhanced SenSing, INtelligence and actuation to Improve job quality in manufacturing
01-10-2018
-31-03-2022
Cobots and in general HRC solutions, which can be used for multiple functions due to their flexibility in deployment, offer higher returns on investment and faster payback, compared to legacy industrial robots. Moreover, cobots’ safety features and ease of use make integration and implementation less costly than the traditional industrial robots. In high-cost countries, having skilled human workers engaged in suitable configured manufacturing, assembly, finishing and inspection operations side by side with cobots is one of the most cost- effective ways to leverage the unique value-adding capabilities of a skilled human workforce
ROSSINI develops a Design Level that allows users to follow and evaluate process designs on multiple dimensions, where job quality and related metrics are the primary outcomes together with productivity, product quality and cost
The joint action of the dynamic scheduler and of the dynamic planner can lead to a reduction of up to 45% of the overall task execution time
The workcells employing the ROSSINI human-robot mutual understanding framework can increase production flexibility by 5%.
HRC solutions make it possible to integrate production machinery, warehousing systems and production facilities into single human-centred cyber-physical systems. As such, the traditional frontier between the production and logistical tasks of manufacturing can be expected to become increasingly interconnected. Lastly, an increased uptake of HRC leads to a change in the robotics value chain itself. Suppliers, integrators and users are bound to collaborate more intensively which is already leading to new business models such as rental/leasing agreements, pay-on-production, predictive maintenance, etc.
ROSSINI delivers high performance HRC workcells, combining the safety of traditional cobots with the working speed and payloads of industrial robots, capable of optimising task execution. This triggers manufacturers’ investment in HRC technology, increasing European factories productivity and thus competitiveness versus low-cost manufacturers.
ROSSINI is expected to unleash new market opportunities for the consortium industrial partners, resulting in a total yearly turnover of worth 125M€ by 2027
ROSSINI ambition is to develop a framework for Human-Robot Mutual Understanding in collaborative operations which will incorporate a human-centred process design level to address and account for human factors like job quality, user experience, trust, feeling of safety, and liability, in the early design stages.
The Rossini Smart and Safe Sensing System (RS4) combine information from several different customised sensing technologies (Vision, Laser Scanning, Radar, Mat, etc.) in order track not only the position but also the speed of each operator and object in the scene, thus ensuring operators’ safety. Moreover, the use of cobots reduces the amount of working hours spent in physical working thus shifting the workforce away from more physically laborious tasks, towards those of assembly, programming etc. In this way, jobs could become more interesting given their need for higher levels of creativity, problem solving and decision making, definitively resulting in an improved job satisfaction.
CoLLaboratE | Co-production CeLL performing Human-Robot Collaborative AssEmbly
01-10-2018
-31-03-2022
Expected impact: Demonstrating the potential to bring back production to Europe
Improvement of productivity in different assembly tasks:
i.Performing Car Starter Assembly
ii.Windshield visual quality check and preassembly
iii.Performing LCD TV Assembly
iv.Αircraft parts assembly
Expected impact of 15% increase in OECD Job Quality Index through work environment and safety improvement
Concerning the ecological and economic operation of a factory, data analytics tools in combination with simulation approaches can contribute to improved throughput, bottleneck-reduction, or both for the production line. Through the optimization of the processes, production execution on organization and logistic level can be optimized by reducing the amount of material within the system, the lead times, or both.