Pilot - WHR Dryer Factory Holistic Quality Platform
Project: QU4LITY
Updated at: 01-02-2024
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
Automation and integration of the manual Quality Control (QC)
Objectiveness and precision of the QC
Productive increase of QC supervisors
Project: AI REGIO
Updated at: 04-07-2023
Monitoring and prediction of water consumption
Automatic detection of water leakages
Optimization of the water pipes network maintenance and installation planning
Project: AI REGIO
Updated at: 04-07-2023
Improved fault detection
Increased facility availability
Reduced maintenance service costs (for the customer)
Reduced analysis & diagnosis times (of the Maintenance Service provider)
Project: AI REGIO
Updated at: 04-07-2023
Enhance production and control strategy with AI,
Improve Worker’s safety using AI to identify unsafe working conditions
Improve predictive maintenance accuracy
Enable remote and highly interactive control strategy for working environment
Project: AI REGIO
Updated at: 04-07-2023
Provide methods for the continual learning of AI model
Provide a specific user solution designed to help decision making of operators
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
Score the quality of measurement
Detect failures and their root causes
Project: AI REGIO
Updated at: 04-07-2023
An adaptive support system for training and guidance in assembly making use of an AI based adaptive algorithm based on both productivity and quality as well as operator capacity and needs
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
Increased Overall Equipment Effectiveness
Reduced Carrying Cost of Inventory
Increased On Time Orders
Project: AI REGIO
Updated at: 04-07-2023
improved accuracy of robot arm during milling operations
Project: AI REGIO
Updated at: 04-07-2023
Improved Reliability of Maintenance and Service
Reduced Working Capital
Improved Customers Acceptance
Decreased Number of training data to adapt the model to new applications
Decreased Training time
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.
Project: A4BLUE
Updated at: 04-10-2022
An AR based solution is proposed for instructions visualization enabling also on-job training activities and guidance. Regarding the ergonomics, an autonomous tool-trolley has been integrated including voice command and AR based gesture steering.
Project: A4BLUE
Updated at: 04-10-2022
The assembly collaborative robot considers both the operation being performed and operator’s anthropometric characteristics for control program selection and part positioning. Besides, the workplace includes multimodal interactions with both the dual arm assembly and logistic robots as well as with the Manufacturing Execution System. Verbal interaction includes natural speaking (i.e. Spanish language) and voice-based feedback messages, while nonverbal interaction is based on gesture commands considering both left and right-handed workers and multichannel notifications (e.g. push notifications, emails, etc.). Furthermore, the maintenance technician is assisted by on event Intervention request alerts, maintenance decision support dashboard and AR/VR based step by step on the job guidance.
Project: A4BLUE
Updated at: 04-10-2022
The proposed solution comprises an adaptive smart tool and an AR instruction application using HoloLens wearable devices and a framework for ensuring digital continuity starting from the data recorded in the system for manufacturing engineering up to the execution and analysis phase
Project: DigiPrime
Updated at: 03-02-2022
The Battery Pilot will aim at demonstrating that the DigiPrime platform can unlock a sustainable business case targeting the remanufacturing and re-use of second life Li-Ion battery cells with a cross-sectorial approach linking the e-mobility sector and the renewable energy sector, specifically focusing on solar and wind energy applications.
As the proactive exploitation of the DigiPrime platform enables the car-monitored SOH tracing and availability, less testing is needed to assess the residual capacity of the battery. Moreover, by knowing the structure of the battery packs, a decision support system can be implemented to adjust the de-and remanufacturing strategy accordingly and select the most proper cells for re-assembly second-life modules, thus unlocking a systematic circular value chain for Li-ion battery cells re-use. Furthermore, excessively degraded cells which cannot be re-used can be sent to high-value recycling, based on the knowledge of their material compositions.
Project: Digital Fibre Ecosystem
Updated at: 03-02-2022
Benefits:
Manufacturing systems are often characterised by ‘silos’ of data which cannot be accessed easily horizontally, and by varied and incompatible data types. By utilising a single data bus for all data to be transmitted on, standards are more easily implemented and all data is accessible by all equipment.
This is particularly important in this context where diverse sources of data (such as metrology systems, CAD data) must be analysed by software (e.g. data analytics, metrology software), and then used to adapt a process (e.g. robotic pathing, machining processes).
When a manufacturing system is fixed and will repeat the same tasks, having hard-coded and non-dynamic data exchange may be sufficient. When a system is reconfigurable and flexible, being able to define data sources and destination in software is critical (so-called software-defined networking).
Integration of adaptive robot control technology into a complex and variable manufacturing process allows for accurate positioning of assembly components despite variability in component manufacture, existing assembly deviation, and the robots themselves.
This allows for progress towards jig-less assembly – saving non-recurring costs in the assembly of large, low batch products. Rather than building large, welded jigs and fixtures, robots are used to position and align parts. As the robots can easily be reused, this saves significant time and money.
Note: Since this demonstrator implementation, the Adaptive Robot Control and K-CMM technologies are now available from True Position Robotics .
The main innovation will be represented by the introduction in production of MPFQ model fused with AQ control loops: Functional Integration and Correlation between Material, Quality, Process and Appliance Functions.