Project Goals

The funding project MRO 2.0 – Maintenance, Repair and Overhaul with the repair chain “Upgrade instead of classic repair” is being processed within the framework of the Werner von Siemens Centres for Industry and Science.

In Phase 2 of MRO 2.0, the research and industrial partners aim to address the results developed in Phase 1 as well as other questions arising in this context, with human-machine interaction, sustainability, and comprehensive networking being fundamental focuses for the realization of the repair process chain.

Our Contribution

Gestalt Robotics is involved in several work packages:

MRO2.DI2 Connected Shopfloor

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

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

MRO2.DI-4 Smart Expert Operation

The goal of this work package is the methodical qualification of risk-aware, AI-integrated processes in the MRO process as well as the operation of these AI solutions. Additionally, a universal metric for analyzing AI decisions, such as updating to new AI solutions or selecting the appropriate training strategy, is sought.

Gestalt Robotics is responsible for developing 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 goal 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 aims to detect errors in the build process at an early stage, thus allowing intervention in the manufacturing process. The system should automatically recognize error patterns and display them via a system-independent interface.

Gestalt Robotics develops an AI-based error detection system and supports in the test body development and the development of distortion control.