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.

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

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.

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

CETMA was able to exploit the possibilities that customized simulation offers to SME’s specialized in boat hulls manufacturing. Thanks to cloud resources, enough power computing is available to analyze different scenarios in a few days instead of several weeks.
Designers of CATMARINE and SKA are now able to achieve high-quality products by analyzing different manufacturing scenarios without wasting time, money and material. The platform is able to optimize the resin injections points/vents and verify the presence of defects in the final product, thus ensuring a complete and correct mold-filling.

https://www.cloudifacturing.eu/experiment2outcome/

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.

The realized progress advances the state of the art in several aspects:                                               

• Electric machine design process including electromagnetic finite element computation, winding design, and post-processing are available on a pay-per-use basis as a cloud solution. To the knowledge of the experiment partners, such a solution has never before been presented.
• Seamless, automated creation of production data avoids data handover errors and provides fast transition from simulation and design to manufacturing of motor prototypes. The full parametric manner of realization also provides a feature never available before.
• The capability to bring a customer requirement in the form of a motor specification to a fully functional motor prototype within one week is a revolutionary USP for Hanning. Despite the fact that this capability still needs to be demonstrated in real life, the steps taken into this direction have greatly improved Hanning’s process know-how and agility.

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.

Benefits:

• Interoperability with legacy data management solution (e.g. MindSphere, HANA, etc.)
• Open solution acting as horizontal stack that connects legacy systems.
• Speeding up the development of digitalized industrial processes both in pilot and factory scale.

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.

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:

• The first solution permits the connection of equipment manufacter repositories with a single connector (LMS/TNO). The benefit of this solution is that equipment manufacturers do not require knowledge of IDS RA and it is the connector owner who has it.
• The second solution has the benefit to connect one service with several data providers through several IDS connectors. The benefit of this second solution is that the data provider controls the data flow and is independent of the connector provider.

Improved fault detection

Increased facility availability

Reduced maintenance service costs (for the customer)

Reduced analysis & diagnosis times (of the Maintenance Service provider)

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 …)

improved accuracy of robot arm during milling operations

Increased Overall Equipment Effectiveness

Reduced Carrying Cost of Inventory

Increased On Time Orders

Provide methods for the continual learning of AI model

Provide a specific user solution designed to help decision making of operators

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 

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

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.

Automation and integration of the manual Quality Control  (QC)

Objectiveness and precision of the QC

Productive increase of QC supervisors 

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.

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

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.

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:

• more robust: each answer modifies the probability associated with each component without deleting or discarding any of them
• more flexible: the system also includes the ability to skip a specific question
• more efficient: the tool can determine the best question based on the probability associated with each component. 

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

Score the quality of measurement

Detect failures and their root causes

Monitoring and prediction of water consumption

Automatic detection of water leakages

Optimization of the water pipes network maintenance and installation planning

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.

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

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.

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.

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.

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.

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.

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.

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 .

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 .