AI REGIO | Regions and DIHs alliance for AI-driven digital transformation of European Manufacturing SMEs
01-10-2020
-30-09-2023
01-10-2020
-30-09-2023
01-01-2019
-31-07-2022
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%.
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
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.
01-10-2016
-30-09-2020
01-10-2016
-31-03-2020
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.
01-09-2016
-31-08-2019
01-01-2015
-31-12-2017
01-01-2015
-01-01-2018
01-10-2016
-31-03-2020
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.
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
01-09-2012
-31-08-2016
01-11-2012
-31-10-2016
11-01-2015
-31-10-2018
Currently, without any demonstrator running, we did not achieve a reduction of waste.
This KPI can probably only be estimated within ReCaM. Effects to the reduction of waste wight arise through reuse of machines and reduction of erroneous products through automatic production or a strict worker guidance and quality checks.
01-10-2016
-30-09-2019
01-10-2016
-30-09-2020
01-11-2011
-30-04-2015
01-11-2011
-31-10-2014
01-11-2011
-31-10-2014
10-01-2015
-30-09-2018
01-10-2016
-30-09-2019
10-01-2015
-30-09-2018
01-09-2015
-31-10-2019
10-01-2015
-30-09-2018
11-01-2015
-31-10-2018
09-01-2015
-31-08-2018