Project goals

The funding project MRO 2.0 - Maintenance, Repair and Overhaul with the repair chain "Upgrade instead of classic repair" is being carried out within the framework of the Werner von Siemens Centre for Industry and Science.

In Phase 2 of MRO 2.0, the research and industry partners aim to work on the results obtained in Phase 1 as well as other questions that arise in this context, with human-machine interaction, sustainability and comprehensive networking forming fundamental focal points for the realization of the repair process chain.

Our contribution

Gestalt Robotics is involved in several work packages:

MRO2.DI2 Networked Shopfloor

The aim of the work package is to provide scalable networking concepts for continuous shadow data acquisition for local use or for interaction with various digital machine or component twins. Based on networked edge, fog and cloud computing, intelligent, modularized and distributed software services are to be provided and tested, which are developed in corresponding sub-work packages.

Gestalt Robotics contributes expertise and support in the modeling and design of edge cloud solutions, especially via 5G networks and for closed-loop controls. We design, develop and lay out scalable microservices in distributed systems with a focus on assistance technologies and develop and optimize the image processing method.

MRO2.DI-4 Smart Expert Operation

The aim of the work package is the methodical qualification of risk-conscious, AI-integrated processes in the MRO process and the operation of these AI solutions. In addition, a generally valid metric for the analysis of AI decisions such as updating to new AI solutions or selecting the appropriate training strategy is sought.

Gestalt Robotics is responsible for the development of quality and qualification metrics for AI models in image processing.

MRO2.AM-3 Image-based quality tool for the in-situ monitoring of L-PBF processes

The aim of this work package is to implement a camera-based, manufacturer-independent quality tool for the in-situ monitoring of the Laser Powder Bed Fusion (L-PBF) process. The monitoring system should detect errors in the assembly process at an early stage and thus enable intervention in the production process. The system should automatically detect error images and display them via a system-independent interface.

Gestalt Robotics develops AI-based defect detection and supports test specimen development and the development of warpage control.