MADEin4 | Metrology Advances for Digitized ECS industry 4.0
01-04-2019
-30-09-2022
01-04-2019
-30-09-2022
01-03-2018
-30-04-2021
01-10-2018
-31-03-2021
01-01-2019
-30-06-2023
01-01-2019
-31-12-2022
01-01-2019
-31-07-2022
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.
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)
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
01-12-2018
-30-11-2022
01-10-2018
-31-12-2021
01-09-2018
-31-08-2022
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.
01-07-2015
-31-07-2018
01-10-2017
-30-09-2021
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It is expected that new nozzle design and thus new water quench will be available for the customers in 5 years time. It is expected that those new products will attract new clients: 5 new contracts in 1 year increasing to 10 new contracts in 5 years, which will increase the turnover of Ferram by 500k Euros in 1 year and 3,5 million Euros in 5 years after the experiment end.
IT4Innovations as DIH and resource provider is expecting the experiment to increase their innovation potential and improve their competitiveness. They are also expecting to increase the number of customers: It is expected that 2 new customers will be attracted due to new capabilities within 5 years after the experiment and will create new sources of revenue.
The cloud-based optimisation of lamination oven’s configuration will lead to the following significant impacts: saving in energy consumption will result in saving of 18,000 kWh a year (short-term) and it will reach 27,000 kWh a year (medium-term). These figures can be multiplied by three in the long term because EndeF is going to build two new ovens. EndeF will drop CO2 emissions by 4,500 kg CO2eq and by 6750 kg CO2eq a year (short- and medium-term).
Hydal, as end user, will benefit from optimized process of water quenching by saving energy and scrap material and by having shorter turnover time as well. Expected economic impact is estimated 100 K€ a year on energy savings alone. Expected economic impact is estimated on 500 000 Euros on in turnover increase in first year after the experiment. This value will rise to the 2 million of euro after 5 years from the experiment.
For end user Ferram, it is expected that the experiment will increase in the turnover and in the number of new jobs created. Outcomes of the experiment will allow Ferram to develop a new generation of water quenches which allow for heat treatment of profiles that are hard or even impossible to cool down with existing water quenches today. This will bring a competitive advantage to Ferram and thus increase the number of customers.
01-10-2017
-31-03-2021
Z-BRE4K heavily contributes to the economic sustainability of the manufacturing sector by deploying an advanced maintenance solution aimed to attain zero unexpected breakdowns. In this regard, Z-BRE4K will avoid fatal failures, thus minimizing the breakdown times and need for spare parts and overhauls and will estimate the remaining useful life of critical subsystems of machinery, lines and shopfloor so that maintenance operations can be scheduled and optimized.
Z-BRE4K solution will guarantee the improved product quality since the machines will be stopped and the maintenance will be carried out before any failure occurs and defective products are manufactured.
By reducing failures, downtimes, unplanned outages the manufacturing will minimize the lead time and maximise the response time: stock-outs, lot processing delays, capacity bottlenecks will be avoided.
Once the Z-BRE4K system has evaluated an anomaly or a deterioration trend, the maintenance scheduling is optimized. Moreover, manufacturing machinery execution parameters can be adapted so that the remaining useful life of the incumbent system can be improved, providing Operations Managers with a flexible shopfloor.
One of the main advantages of Z-BRE4K is the Z-SYNCHRONIZE strategy that enhances the coordination of production planning, logistics and maintenance operations.
The minimization of downtimes and failures, the optimization of maintenance operations and the reduction of row material waste will lead to an increase the in-service efficiency by 26% and increased productivity.
Z-BRE4K will lead to the optimisation of the performance, avoiding waste due to malfunctioning machinery and increased energy consumption due to the presence of failures. the reduction of the electric costs is extimated by 10%.
The avoidance of defective production and overproduction will lead to a better efficiency in the use of materials.
Z-Bre4k will contribute to the optimisation of the manufacturing processesresulting in significantly less waste and scrap. Z-Bre4k will contribute to the reduction of defective production thanks to the optimisation of manufacturing through model-based control and improved accuracy. Moreover, it will allow to avoid overproduction that is to say manufacturing items for which there are no orders thanks to the collection of data that will control the production process producing only what is required and not overproduce.
Z-BRE4K will provide end-users with a solution with direct benefits for the manufacturing sector in Europe such as increasing the in-service efficiency by 24% (estimated) through a combination of preventive, predictive and prescriptive maintenance strategy. Thus, companies will be able to shift some of the operative resources from maintenance to production. The benefits of Z-BRE4K strategies deployment will result in the creation of 400 jobs and over 42M€ ROI within the consortium over after the 4th year of commercialization.
A dedicated Z-SAFETY strategy will look for the drastic reduction of accidents.
01-10-2017
-30-09-2020
01-09-2017
-30-11-2022
01-11-2017
-30-04-2021
01-10-2017
-31-03-2021
01-09-2017
-28-02-2021
The economic impact of UPTIME is the most important one and can be seen at 2 levels :
01-09-2017
-29-02-2020
01-11-2017
-28-02-2021
01-10-2017
-31-03-2021
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.
01-10-2017
-30-09-2020
Details: IoT/Device Integration