CLOUDFLOW | Computational Cloud Services and Workflows for Agile Engineering
01-07-2013
-31-12-2016
01-07-2013
-31-12-2016
01-01-2015
-01-01-2018
01-01-2015
-01-01-2018
01-01-2015
-31-12-2017
01-01-2015
-01-01-2018
01-12-2014
-01-12-2018
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 .
01-02-2015
-01-02-2018
01-01-2015
-01-01-2018
01-09-2015
-31-08-2018
09-01-2015
-31-08-2018
We will generate Objects closer to their nominal shape and thus less material will be removed by subtractive processes, and reduce energy consumption and indirectly CO2 emission
01-09-2015
-31-10-2019
10-01-2015
-30-09-2018
10-01-2015
-30-09-2018
01-09-2014
-17-11-2016
09-01-2015
-31-08-2018
10-01-2015
-30-09-2018
01-09-2016
-31-08-2019
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.
01-10-2016
-30-09-2019
01-10-2016
-30-09-2020
01-10-2016
-30-09-2019
01-10-2016
-31-03-2020
10-01-2015
-30-09-2018
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.
11-01-2015
-31-10-2018
01-10-2016
-30-09-2020
01-10-2016
-30-09-2019
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.