NIMBLE | Collaboration Network for Industry, Manufacturing, Business and Logistics in Europe
01-10-2016
-31-03-2020
01-10-2016
-31-03-2020
01-10-2016
-30-09-2019
01-10-2016
-31-03-2020
MANUWORK aims to design the workplace of the future where the tasks are allocated to the production line operators based on their skills while also increasing their capabilities through Human-Robot Collaboration and Augmented Reality instructions
01-10-2016
-30-09-2019
01-10-2016
-30-09-2020
01-09-2016
-31-05-2021
01-10-2016
-31-03-2020
The solution provided byZ-Fact0r reduces the number of scrapped parts at different steps of the production process. The use of just the needed amount of raw materials is thus a consequence of applying Z-Fact0r, but it has manifold repercussions, as there is reduction of wear of equipment and tools by doing it right the first time. It also eliminates the duplication of auxiliary materials needed to produce the parts.
The Z-Fact0r platform, and the intensive monitoring of both product and process that accompanies it, has been key for the reduction of defective manufactured parts at the flute grinding process. Should similar solutions be implemented in the rest of machines at NECO premises with a defective rate reduction of around 25%, some 20.000€ could be saved by NECO yearly in the form of raw materials (outside-diameter-grinded High Speed Steel bars), which would instead require to be recycled. Furthermore, if the cutting angle happens to be wrong, the parts cannot be remanufactured and need to be scrapped.
The Z-Fact0r solution contributes to prevent defective parts to be sintered and reduces the number of defects during the machining of sintered parts. Most of the defective, very hard components, cannot be recovered by further machining or by healing operations and must thus be fully scrapped. Besides these recyclable materials there are also losses on the different production steps: from milling, spray drying, pressing, green machining and sintering.
With lesser defective part manufacturing, in the same proportion as HSS (raw material), oil consumption from the grinding machine (used as a coolant and in order to reduce friction between the tool and the part) is reduced, and the abrasive material waste generated from the cutting tool is also reduced, something which comes along with the oil as waste waters. Also, further upstream, the usage of salts for the heat treatment process also diminishes.
The reduction of the defective rate in the flute grinding process is key, due to this part of the manufacturing chain being the one with the higher proportion of scrapped parts. The time working on the reworking of the parts or the manufacturing of new, “extra” ones, inside a certain batch, multiplied by the hourly cost of the machines would be the numerical estimation of the saved energy, which, of course, is proportional to CO2 emissions. However, the flute grinding process is not the process with the most energy consumption, but it is, by far, the heat treatment (which is located upstream and, thus, the reduction of the defective in this particular process where the NECO pilot focuses would also impact on the energy saving of the machines and processes that come before).
Obtaining the part with the right specifications at the first attempt means that the energy consumption will be at its lowest. The confidence Z-Fact0r solution brings also allows us to work with closer tolerances, less excesses of material, and thus less re-working time throughout the production steps.
01-10-2016
-31-10-2019
vf-OS as an operating system is addressed to be used by humans on stations, work centers and enterprises. Due to its collaborative nature and the app store nature, vf-OS platform will cover human interacting on a connected world of manufacturing and logistic enterprises.
01-10-2016
-31-03-2021
01-10-2016
-30-09-2019
01-01-2017
-30-06-2020
01-11-2015
-31-10-2017
01-09-2017
-28-02-2021
The economic impact of UPTIME is the most important one and can be seen at 2 levels :
01-11-2017
-28-02-2021
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
-31-03-2021
01-10-2017
-31-03-2021
01-10-2017
-30-09-2020
01-09-2017
-29-02-2020
01-11-2017
-30-04-2021
01-10-2018
-31-03-2023
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.
01-10-2018
-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
user interfaces targeting various user groups: