From Elliot Cloud, through the SCALDATA ProjectWe are committed to digitalisation and the application of new technological solutions to improve the competitiveness of SMEs. In collaboration with CITD Engineering and Technologies, Andalucía Smart City Cluster and AEI Smart City ClusterWe have carried out a nine-month project to generate predictions on the defectology of manufactured parts by means of artificial intelligence techniques applied to manufacturing parameters.
Project data
- Call for applications: Support programme for Business and Innovation Clusters (BICs) 2022.
- Duration: 9 months (21/03/2022 - 21/12/2022).
- Project budget: 267,590 euros.
- Grant awarded: 205,491 euros.
Objective of the SCALDATA Project
Additive manufacturing processes are efficient in the use of materials and energy and contribute to making production processes more flexible. Therefore, in a scenario of scarce resources, with increasingly tight deadlines and costs, it is imperative to replace and complement traditional material removal processes with more efficient ones such as additive processes. However, there are still many factors that prevent the adoption of this technology, especially the appearance of defects in the manufactured parts, which are unpredictable, random and difficult to detect.
The main objective of the project will be to exploit data relating to powder metal bed additive manufacturing processes and use artificial intelligence to generate predictions for the typology, location and effect of defects in the parts produced. Knowledge of this defectology, both in terms of location and size, is of vital importance for the validation of the part, especially for applications with fatigue requirements.
Currently, the distribution of defects in a printed part is obtained thanks to non-destructive inspection methods such as tomography or radiography, being necessary to reach minimum resolutions to be able to detect defects that could be critical for the correct functioning of the part. This process is cost and time inefficient and often leads to discarding parts after costly manufacturing, machining or other post-processing and inspection processes.
