DISCMAM - Digital Supply-Chain for On-Site Maintenance in defence by Additive Manufacturing

Participantes:

ADAXIS
CENSEC
DTI
EULER 3D
FIELDMADE
INNOTEC
LORTEK
OPTIMUS 3D
TU/e
ZABALA Consulting

Programa:

EDF-2022-LS-RA-DIS

Sectores:

Defensa

Tecnologías:

Análisis de Datos

Control y Robótica

DED-LB

Plataformas digitales

Visión Artificial

Objetivo

Currently, military operations face significant downtimes due to maintenance requirements of equipment, vehicles, and other critical assets, heavily relying on repair and spare parts supply. By leveraging metal AM technologies, the project seeks to enable rapid response in manufacturing metallic spare parts and repairing critical components, regardless of the operational location. DISCMAM will develop a secure and disruptive digital thread, facilitating remote assistance for repair and spare parts manufacturing in military field operations. Through two demonstrative use cases utilizing Powder Bed Fusion (PBF) and Directed Energy Deposition Laser-based (DED-LB) technologies, DISCMAM will validate its innovative solution. The digital thread will establish an effective digital supply chain for on-site maintenance within the EU defence sector.
Key benefits include eliminating the need for AM highly specialized operators/technicians on-site, reducing supply time, shortages, and spare parts costs, minimizing non-usage time of critical assets, and optimizing on-site stocks and digital inventory.

Solución

LORTEK is the coordinator of the project, and it will be focused on digitalization strategies of DED-LB technology. During the project, LORTEK’s DED cell will be upgraded integrating hardware and software as a showroom of a DED portable machine. For that, LORTEK will work on the process by developing methodologies for the development of fast-responsive predictive models for mobile DED-LB systems. Additionally, it will integrate monitoring solutions for remote assistance of the process and part qualification. Moreover, to reconstruct the parts to be repaired LORTEK will develop algorithms for part digitalization view planning and recognition for geometry reconstruction.

 

discmam.eu