LEVEL-UP | Protocols and Strategies for extending the useful Life of major capital investments and Large Industrial Equipment
01-10-2019
-30-09-2023
01-10-2019
-30-09-2023
01-09-2022
-31-08-2026
01-10-2020
-30-09-2023
01-12-2019
-30-11-2022
01-01-2020
-31-12-2023
01-01-2020
-31-12-2023
01-01-2019
-31-07-2022
01-10-2017
-31-03-2021
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.
01-10-2017
-31-03-2021
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.
01-11-2017
-31-10-2020
01-10-2017
-30-09-2020
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.
01-10-2016
-30-09-2020
01-10-2022
-30-09-2026
By accelerating and upscaling the structuring process, the OPTIMAL project will increase the process efficiency and yield, which will allow for “first time right” fabrication of the required structures, lower consumption of resources, waste reduction, lower CO2 emissions, increase of productivity, and cost reduction.
01-06-2022
-31-05-2025
01-10-2016
-31-03-2020
01-01-2015
-01-01-2018
02-09-2013
-01-09-2017
01-09-2016
-31-08-2019
The COMPOSITION Integrated Information Management System will be a digital automation framework that optimises the manufacturing processes by exploiting existing data, knowledge and tools, integrating them with newly installed cyber physical systems (CPS) and automation software, in order to increase productivity and to allow for dynamic adaptation to changing market requirements. CPS using IoT devices such as wireless sensors nodes (WSN) can be easily retrofitted to existing equipment and infrastructure to gather sensory data and detect anomalies as well as opportunities to improve productivity and cvcle time.
01-01-2015
-31-12-2017
01-01-2015
-01-01-2018
01-10-2012
-30-09-2015
X-act delviers advanced robotic systems involving humans and dual arm robots in cooperation, thus advancing the way manufacturing systems perform. Therefore it aims to bring about Products, processes and production systems co-evolution.
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.
01-12-2014
-01-12-2017
A reduction of at least 20% of defective parts produced by additive manufacturing by CNC LMD, that mus be discarded.
A reduction of at least 50%of defective repairing by robotic LMD, that must reworked to be repaired again .
01-01-2015
-01-01-2018
01-10-2016
-30-09-2019
01-09-2016
-31-08-2019
01-09-2012
-31-08-2016
01-10-2016
-30-09-2019
11-01-2015
-31-10-2018
01-11-2012
-31-10-2016