Project: TEAMING.AI
Updated at: 20-08-2024
Project: TEAMING.AI
Updated at: 20-08-2024
The current state of the art is limited in terms of team modelling and dynamic adaptability. The project’ approach addresses both by integrating methods for process modelling and knowledge-update mechanisms to come up with an enriched digital shadow that goes beyond static models.
This software tool is designed for real-time process management and ensures the effective execution of these teaming processes. It performs continuous observation and analysis, allowing for the immediate detection of any deviations or disruptions in the team workflows. This recognition supports a prompt response, facilitating adjustments to maintain the operation of the platform.
Project: TEAMING.AI
Updated at: 20-08-2024
Even though state-of-the-art process orchestration tools are equipped with logging mechanisms, they do not come with semantically enhanced digital shadows of process knowledge. The aim to close this gap. The knowledge graphs representation not only provides semantic linking/integration of heterogeneous manufacturing knowledge (process, product, resources) but also supports reusability, extensibility, and interoperability.
Project: TEAMING.AI
Updated at: 20-08-2024
The development of advanced machine diagnostics software based on machine learning is intricately linked with hardware development. Accurate information obtained from machines and connected devices is crucial to the correct functioning of the software. Software sockets have been developed to link incoming data from injection machines with a data language that the system can understand.
The developed software sockets allow a seamless data acquisition from in-plant machines so data can be immediately consumed by the system. The data ingestion allows the AI models to be trained and used in a more simplified manner.
Project: TEAMING.AI
Updated at: 20-08-2024
Usage of wide-angle cameras to monitor a very large work place and analyse multiple workers simultaneously, creating a digital shadow of a human-centred work process.
Rapid on-the-fly analysis of multi-person scenes on a large scale reduces occupational health officers' analysis time and allows for accurate work situation descriptions, as well as simplified follow-up improvement comparisons.
Project: TEAMING.AI
Updated at: 20-08-2024
Besides addressing the current lack of dynamic knowledge graph embedding methods, the partner’s work also includes a lean updating approach to efficiently recognize knowledge graph modifications.
Knowledge graph embeddings can be computed on the fly to use them in downstream applications within dynamic domains such as manufacturing.
Project: TEAMING.AI
Updated at: 20-08-2024
The method provides a modular approach for knowledge graph population and curation from complex and heterogeneous industrial/manufacturing domains. It helps different stakeholders to represent and share their mental models in a uniform standard representation that can be interpreted by both humans and AI agents.
Project: TEAMING.AI
Updated at: 20-08-2024
The main contribution is a novel machine learning architecture that can process both vision data and sensor time-series data to concurrently to obtain greater defect detection accuracy.
High accuracy defect detection, which leads to reduced scrap rates and cost savings
Project: QU4LITY
Updated at: 01-02-2024
Project: BOREALIS
Updated at: 31-01-2024
Project: AI REGIO
Updated at: 25-01-2024
The experiment has achieved the following objectives:
Provide a tool able to help operators to find the exact sheet they need for production line.
The tool can significatively decrease time usually needed to find out, handle and pick up the sheet that is needed from the production line.
The tool is expected to self-learn over time.
Project: CloudiFacturing
Updated at: 12-01-2024
Tools developed and deployed on CloudiFacturing platform during this experiment allow end-users who operate water quenches to derive operational conditions of the water quench and thus, increase precision and repeatability of the process and decrease its dependence on the experience of the operator. The outcomes of the experiment also bring new knowledge to the whole process. Additionally, the developed technologies allow using numerical modeling and simulation in the design of a new generation of water quenches and their components.
Project: MARKET4.0
Updated at: 12-01-2024
Simplified and Efficient Selection of Suitable Manufacturing Equipment: For the equipment manufacturer, IDSRAM permits the trusted connection of the equipment repository with external apps (ESS), permitting (in a time-saving process) the best selection of a manufacturing equipment, thanks to accessible data that where not accessible before.
The MARKET4.0 Metal Domain Data Space propose two solutions:
Project: A4BLUE
Updated at: 22-12-2023
A collaborative robotic cell has been implemented for the deburring operation where the robot executes the most exhausting phases, while the worker focuses on final quality inspection. Regarding the assembly process, an AR solution, using ultra-real animations has been implemented to guide operators through tasks. Additional AR functionalities include the visualization of textual information (tips, best practices…), access to technical documents and voice recording
Project: AI REGIO
Updated at: 17-07-2023
Project: AI REGIO
Updated at: 04-07-2023
Project: AI REGIO
Updated at: 04-07-2023
Project: AI REGIO
Updated at: 04-07-2023
Project: AI REGIO
Updated at: 04-07-2023
Project: AI REGIO
Updated at: 04-07-2023
Provide automatically feasible resource suggestions to the system designer
Reduce time used for system design and reconfiguration planning
Allow more alternative resources to be considered during design and planning, leading to potentially innovative solutions
Reduce humane errors in search and filtering
Automating the search and filtering of feasible resources and resource combinations for specific product requirements from large search spaces.
Automating the identification of required reconfiguration actions on current layout.
Project: AI REGIO
Updated at: 04-07-2023
Project: AI REGIO
Updated at: 04-07-2023
Project: AI REGIO
Updated at: 04-07-2023
The troubleshooting system implemented in the Experiment is an AI-driven advanced solution that improved an already existing system, totally deterministic. The new solutions instead is based on a probabilistic approach. Compared to the previous one, it is:
Project: AI REGIO
Updated at: 04-07-2023
Project: AI REGIO
Updated at: 04-07-2023
Shorten the engineering cycle => automatic feedback and decision making
Save time and reduce cost => reduce manual operations, automation process
Automatic recommendation for printing configuration and additive manufacturing feasibility => Covering all the lifecycle (ideation, request, design, simulation …)
Project: AI REGIO
Updated at: 04-07-2023
Project: AI REGIO
Updated at: 04-07-2023
Project: AI REGIO
Updated at: 04-07-2023
Project: AI REGIO
Updated at: 04-07-2023
Deployment of an IIoT platform serving IILAB demonstrators
Increased real time visibility on a production system
Increased flexibility and efficiency of a production system
Increased robustness of operations of robotic mobile manipulators
Real-time Production Data Monitoring
Industrial Autonomous Systems Reliability
Project: AI REGIO
Updated at: 04-07-2023
Online prediction of the maximum number of pieces to be produced by a specific machine according to the current status of the tooling and the readings of its control parameters.
Optimization of machine control parameters configuration to maximize the tooling life while the quality of the production is maintained.
Maximization of the tooling usage time of every machine, while maintaining the machining quality.
Optimization of machine control parameters configuration to maximize the tooling life while the quality of the production is maintained
Coordination of the tooling change in all the machines. This objective would face the tooling change considering the impact of machines among themselves, instead of just one by one.
Even though state-of-the-art process orchestration tools are equipped with logging mechanisms, they do not come with semantically enhanced digital shadows of process knowledge. The aim is to close this gap. Process engineers do not need to become knowledge graph experts. They can just plug in our extension to automatically retrieve knowledge graph representations of their processes.