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Two white papers published by our Innovation team
Author: Ioannis Batas
March 20th 2025
At CORE Innovation Days, held earlier this year, our Innovation Department published two white papers: "Our Innovation Management Methodology for EC-funded Projects" and "Factory of the Future: What’s Happening, What’s Evolving, and What’s Next."
The two white papers were distributed to attendees, offering valuable insights into our innovation management methodology and the transformative potential of Industry 4.0.
White Paper #1: "Our Innovation Management Methodology for EC-funded Projects"
In the dynamic landscape of EU-funded research, turning innovative concepts into tangible solutions requires a strategic approach. In the first white paper, we present our Innovation Management methodology, which unfolds through a four-phase exploitation strategy designed to help researchers and consortium partners navigate the entire process. From identifying project Key Exploitable Results (KERs) to developing market roadmaps, our methodology ensures that results are protected through Intellectual Property Rights (IPR) and strategically positioned for market adoption.
Our Innovation Management methodology also features the CORE Exploitation Canvas, a tool we designed to streamline the market entry of KERs from EU-funded projects. The canvas guides teams through 10 key blocks: identifying partners and IP ownership, selecting IPR protection, analyzing the target market, addressing barriers, assessing broader impact, evaluating the State of the Art and Unique Selling Points, outlining exploitation routes, setting actions and milestones, and identifying costs and revenue streams. By simplifying complex innovation processes, the CORE Exploitation Canvas helps teams align outputs with market needs, accelerate adoption, and create sustainable impact.
You can download the white paper here.
White Paper #2: "Factory of the Future: What’s Happening, What’s Evolving, and What’s Next"
Industry 4.0 is revolutionising manufacturing, presenting both opportunities and challenges for companies. Our second paper explores the transformative potential of smart factories enabled by AI, IoT, and other advanced technologies. The global Industry 4.0 market is experiencing explosive growth, projected to reach €511 billion by 2032, driven by the shift towards scalable, automated, and interconnected production systems.
This white paper analyses key technologies shaping the future of manufacturing, from AI and IoT to robotics and additive manufacturing, while addressing the barriers companies should mitigate. It highlights critical issues like workforce upskilling, cybersecurity risks, and the integration of legacy systems, offering practical strategies to navigate these challenges. By bridging current capabilities with future possibilities, this paper serves as a guide for manufacturers looking to embrace digital transformation and secure long-term competitiveness in a rapidly evolving market.
Our insights for this second paper were further enriched by our involvement in leading the exploitation management activities of the MODUL4R and M4ESTRO projects.
You can download the white paper here.
White papers now available online
At CORE Group, we believe that knowledge sharing is essential for driving technological progress and empowering innovators to transform ideas into impactful solutions.
If you missed the event or want to explore further, both white papers are now accessible. Learn how CORE Innovation is shaping the future of research exploitation and innovation management.
For more information, don't hesitate to reach out to our Innovation Department.
FAIRE: Federated Artificial Intelligence for Remaining useful life Edge analytics
Revolutionising Industrial Operations with FAIRE: Federated AI for Predictive Maintenance
Author: Konstantina Tsioli, Pavlos Stavrou
February 20th 2025
At CORE Innovation Days in January, CORE unveiled a groundbreaking demonstration of FAIRE (Federated Artificial Intelligence for Remaining Useful Life Edge Analytics), a cutting-edge solution that combines AI, edge computing, and federated learning to address critical challenges in industrial operations.
This innovative approach not only enhances operational efficiency but also ensures data privacy and scalability, making it a game-changer for industries like manufacturing, energy, and pharmaceutical.
What is FAIRE
FAIRE is a ground-breaking solution based on the MODUL4R and RE4DY EU projects. FAIRE is a federated AI solution designed to optimise industrial processes by leveraging edge computing and federated learning.
It enables real-time data processing and predictive analytics, while keeping sensitive data secure and on-premise. FAIRE showcased how it can be applied to predictive maintenance for CNC machines, but its applications extend far beyond this use case.
Key FAIRE Features
Edge Computing: This solution utilises edge devices deployed directly on the shop floor to collect and process data locally. This reduces latency, minimises bandwidth usage, and ensures real-time insights without relying on constant cloud connectivity.
In the demo, two edge devices were connected to CNC machines, collecting data relevant to tool wear and predicting the Remaining Useful Life (RUL) of milling tools.
Remaining Useful Life (RUL): is a predictive tool that estimates the time left before a machine or component fails or requires maintenance, based on real-time data and historical performance patterns. In the context of FAIRE, the RUL model predicts tool wear in CNC machines, enabling proactive maintenance and reducing downtime while ensuring data privacy and security.
Federated Learning: FAIRE employs federated learning to enable collaborative intelligence across multiple machines or factories. Instead of sharing raw data, only model parameters (e.g., insights and updates) are sent to a central server, ensuring data privacy and compliance with regulations like GDPR. This approach allows machines to "learn" from each other, improving prediction accuracy and operational efficiency without compromising sensitive information.
Data Privacy and Security: By keeping data on-premise and sharing only model updates, FAIRE ensures that proprietary information remains secure. This is particularly important for industries with strict data protection requirements.
Scalability and Flexibility: FAIRE’s architecture is designed to scale effortlessly. As new machines or edge devices are added to the network, they can seamlessly integrate into the federated learning ecosystem, enhancing the system’s overall intelligence and resilience.
Predictive Maintenance for CNC Machines
The demonstration of FAIRE solution focuses on a real life application: predictive maintenance for CNC machines. Here’s how it worked:
Data Collection: Two edge devices were connected to two CNC machines, collecting real-time data on tool wear and machine performance using industrial protocols like OPC-UA and MQTT.
Local Processing: The edge devices preprocessed the data locally, running AI models to detect anomalies and predict RUL. Results were displayed on monitors, providing operators with actionable insights.
Federated Learning: Model updates from each edge device were aggregated to a central server to update the global model. The updated model was then sent back to the edge devices, enhancing their predictive accuracy.
Real-Time Insights: Operators then could monitor tool wear and RUL in real time, enabling proactive maintenance and reducing downtime.
The benefits of FAIRE
FAIRE offers numerous benefits for industrial operations:
Smarter Machines: Continuous learning and adaptation improve machine performance and operational efficiency.
Enhanced Data Privacy: Sensitive data remains on-premise, ensuring compliance with data protection regulations and/or requirements.
Cost Optimisation: Reduced data transmission and proactive maintenance minimise operational costs.
Collaborative Intelligence: Federated learning enables machines to learn from each other, improving model accuracy across the network.
Scalability: The solution can easily scale to include additional machines or factories, making it suitable for large industrial networks.
Application areas
While the demonstration of FAIRE solution involved an example of CNC machines, its capabilities extend to various industries:
Pharmaceutical: In a sector where protecting sensitive and production data is paramount, this solution safeguards data privacy and security.
Automotive: Enhance predictive maintenance for automotive production lines.
Aerospace: Improve the performance and reliability of aircraft components.
Energy and Smart Grids: Monitor and optimise power grid equipment like transformers and substations.
Mining: Optimise the operation of heavy machinery like excavators and drilling equipment.
FAIRE represents a significant leap forward in industrial AI, combining the power of edge computing and federated learning to deliver real-time insights, enhance data privacy, and optimise operations. By addressing critical challenges like unexpected downtime, inefficient data handling, and legacy equipment limitations, FAIRE empowers industries to achieve smarter, safer, and more efficient operations.
Solutions like FAIRE will play a critical role in shaping the future of industrial automation and data-driven decision-making.
Introducing Smart Data Management to Mining Operations
Authors: Konstantina Tsioli, Nikolaos Gevrekis, Konstantinos Plessas
February 13th 2025
The CORE Innovation Centre team has developed a key backend platform for the MASTERMINE project, which aspires to become the go-to ecosystem for mines that envision digitalisation, environmental sustainability, productivity monitoring and public acceptance.
A key module of the MASTERMINE project is Cybermine, which serves as the access point to the physical world managing the field data for all components, connecting the physical and digital world through technologies like IIoT, the cloud, edge computing and machine learning ensuring the smart mine design and predictive maintenance of equipment and vehicles.
Our team has developed a backend platform within the Cybermine module, which seamlessly integrates and manages data from various sources across the mining industry. As a smart data management system, it automates data collection, storage, and access, ensuring flexibility, scalability and efficiency in the use of data.
Here’s a look at the ingredients that make this platform innovative.
An overview of the back-end platform developed by the CORE IC team
Collecting Data from Different Sources
Everything begins with data sources, which can include sensors, devices, or systems in the mining industry that generate information. However, not all data is the same - different sources provide data in different formats, structures, and transmission methods. Some data is received in real-time, while other data arrives in scheduled batches. In some cases, end users may even need to manually upload files. The platform uses a multi-layered storage approach, enabling it to secure and organise data types like real-time updates, historical records, and large files. Tools such as S3 buckets, InfluxDB, and PostgreSQL ensure both speed and reliability.
Making the Data Available to Users
Once stored, the data needs to be easily accessible. This is where the Consumption Layer comes in. This layer allows users to retrieve any data they need, whether it’s raw data straight from the source, processed insights, real-time feeds, or historical records. Through this layer, users can access raw or processed data quickly and efficiently, tailored to their specific needs, such as real-time monitoring or historical analysis.
Innovation: Making Data Collection Smarter
Traditionally, integrating data from different devices and sensors required custom-built services for each type of data source, making the process slow and complicated. The platform developed by our team eliminates this challenge by offering an automated, intelligent system that dynamically adapts to any new data source. Consequently, the effort needed to integrate new data sources is significantly reduced.
Significance for the Mining Industry
Mining operations are known for their harsh conditions, making it challenging to collect and manage data effectively. A platform like the one developed by CORE IC is critical because it simplifies data integration and enables the seamless collection of data from diverse sources, even in environments where traditional methods struggle. Its robust architecture ensures data reliability and accessibility, even in remote or extreme locations.
The ability to interpret data ahead of time is crucial for mining operations, particularly regarding heavy machinery, where real-time insights can prevent breakdowns, enhance maintenance schedules, and ensure operational continuity. By transforming raw data into actionable insights, the platform empowers decision-makers — whether managers, operators, or engineers — to make informed decisions, improving safety, productivity, and efficiency. Ultimately, the platform supports innovation, sustainability, and operational resilience in the demanding context of the modern mining industry.
CORE Group participated in a focus group workshop at HALCOR
Author: Maria Tassi
November 13th 2024
A successful Focus Group Workshop was held on 31st of October 2024 at the HALCOR facilities, in Boeotia, Greece as part of the CARDIMED project.
Participants in the workshop included representatives from CORE Group, ICCS, NTUA, HALCOR employees and managers, as well as regional stakeholders, enabling collaboration and knowledge exchange.
The CARDIMED project
CARDIMED is a project funded by the Horizon Europe Programme focused on boosting Mediterranean climate resilience through widespread adoption of Nature-based Solutions (NBS) across regions and communities.
Our CORE team will develop a cloud-based orchestration middleware for efficient data handling across diverse sources, and also focus our efforts on industrial symbiosis through smart water management in the HALCOR demo site, using digital twin technologies.
The workshop aimed at promoting innovative solutions in industrial manufacturing, conducted in the context of Digital Solutions creation that offer tailored views for visualising information to non-experts, citizens etc., with emphasis on the Demonstration case of Industrial symbiosis through smart water management.
Workshop goals and objectives
The main objective of the workshop was to engage end users to gather feedback and prioritise the requirements, and consequently translate the business requirements of end user, HALCOR, to technical requirements leading to implementation of digital solutions and bringing innovation to the industry.
The workshop was opened by M.Sc. Efstathia Ziata (HALCOR), who presented the CARDIMED project and its objectives. Following her presentation, Dr. Ioannis Meintanis (CORE IC) gave insights on the digital twin solution, which is a replica of a physical asset that simulates its behaviour in a virtual environment, highlighting its role in supporting Water-Industrial Symbiosis within HALCOR's factories.
Dr. Maria Tassi (CORE IC) presented other Digital Solutions implemented as part of the HALCOR demo, such as the Nature-Based Solutions (NBS) definition and scenario-based impact assessment interface, the climate resilience dashboards and data storytelling, the citizen engagement app and intervention content management and the NBS exploitation and transferability support module, highlighting their potential to enhance efficiency and sustainability in operations.
Notable contributors to the round table discussions included M.Sc. Katerina Karagiannopoulou (ICCS) and M.Sc. Nikolaos Gevrekis (CORE IC), who provided valuable perspectives on the digital solutions.
Impact on Industry
The success of the workshop lies in end users’ discussions on the various digital solutions, who provided valuable feedback and prioritised user requirements to be integrated in the Digital Twin solution. Their insights will be critical in shaping a final product that effectively addresses the evolving demands of the industry.
These innovations are set to significantly impact the manufacturing industry, by enhancing resource efficiency and sustainability. They will help optimise water usage and promote resource reuse across interconnected processes, leading to cost savings and reduced environmental footprints.
CORE Group’s collaboration with HALCOR
These technological advances will enable HALCOR to optimise its manufacturing processes and resource management in real time, resulting in improved operational efficiency, significant cost savings and reduced water consumption. By adopting sustainable practices, HALCOR can strengthen its reputation as an industry leader in sustainability and appeal to environmentally conscious stakeholders.
HALCOR is a strategic partner for CORE Group, with a collaboration extending across three more Horizon Europe projects, TRINEFLEX, StreamSTEP and THESEUS. As part of the TRINEFLEX project, HALCOR has integrated COREbeat, CORE Group’s all-in-one Predictive Maintenance Platform at its Copper Tubes Plant facility in Boeotia. COREbeat’s asset monitoring capabilities are helping HALCOR acquire deep monitoring insights and increase the availability, flexibility, efficiency and reliability of their equipment.
COREbeat, our all-in-one Predictive Maintenance solution, relies on the beatBox hardware component, pictured here.
The TEAMING.AI project reaches a successful conclusion
Authors: Maria Lentoudi, Ioannis Batas
26th September 2024
The TEAMING.AI project has officially wrapped up its activities, with a Final Review meeting held earlier this summer in Valencia, at the premises of Industrias Alegre. This meeting marked the culmination of 3.5 years of dedicated effort, showcasing the remarkable outcomes of this collaborative project.
Comprising a consortium of 15 partners from 8 countries, TEAMING.AI entered into force in January 2021 with the goal of increasing the sustainability of EU production with the help of Artificial Intelligence. The project has since yielded remarkable results, including more than 23 open-access publications.
Project overview
TEAMING.AI project’s aim was to make breakthroughs in smart manufacturing by introducing greater customisation and personalisation of products and services in AI technologies. Through a new human and AI teaming framework, the aim of our consortium was to optimise manufacturing processes, maximising the strengths of both the human and AI elements, while maintaining and re-examining safety and ethical compliance guidelines.
This was achieved through the development of an innovative operational framework, designed to cope with the heterogeneity of data types and the uncertainty and dynamic changes in the context of human-AI interaction with update dynamics more instantly than with pre-existing technologies.
Our CORE team led on the Dissemination and Exploitation Work Package, being involved in various tasks within the project framework to expand TEAMING.AI’s impact. More specifically, we led the project’s strategic management and replicability, as well as leading the dissemination and communication strategy.
Strategic Management & Replicability of TEAMING.AI
The CORE innovation team was responsible for the strategic management of the consortium, identifying Key Exploitable Results of the project and carrying out market analysis. Our work for this part of the project included:
Identifying Market Barriers: Our team conducted a market barriers analysis, based on input provided through a custom questionnaire. The project’s end users were surveyed, and the survey was also circulated to the ICT-38 2020 projects, increasing our end user sample. After completing the survey, we identified mitigation strategies for the barriers discussed.
Pains & Gains: We identified the most significant pains our end users face based on a unique research plan. The results of this part of our research were highly impactful, being included in Chapter 23 of the “Artificial Intelligence in Manufacturing” open access book. You can find out more here.
Value Propositions: Our team identified the value propositions offered throughout the project, through interactive workshops with our partners to help us align the identified jobs, pains, and gains with the Teaming.AI Engine result.
PESTLE Analysis: A PESTLE analysis was performed to describe Political, Economic, Social, Technological, Legal and Environmental factors that are related to Teaming.AI. Results show strong political presence to enable further scale-up activities of the project’s results. The uncertain economic conditions may influence investment decisions. The social factors indicate the need for more efficient activities and upskilling. The technologies are emerging and considered enablers according to Gartner. Finally, from an environmental point of view, results have remained a little stagnant according to the IPCC.
Market Replication & Analysis: As a final task, our team worked on Market Replication. The technology providers relevant to Teaming.AI were considered a possible segment for replication besides the project’s end users. A workshop was held with the project’s technology providers to determine requirements to address these segments.
Dissemination and Communication Activities
When it comes to dissemination and communication, the evaluation revealed a strongly positive outcome for our team’s strategy. CORE worked hand-in-hand with the entire consortium to maximize the project's impact and ensure the project’s objectives were communicated effectively to relevant audiences and stakeholders.
The project’s official website acted as its main communication hub, supported by a strong presence on social media platforms, the creation of various communication materials, including 11 videos throughout the duration of the project, the publication of 33 media articles, the release of 10 dedicated newsletter editions, and 11 press releases. These efforts were aimed at increasing the project’s visibility and public engagement.
TEAMING.AI consortium also produced 28 scientific peer-reviewed publications in top-ranked journals or conferences, attended 43 events delivering 38 presentations, promoted TEAMING.AI through 3 exhibit booths at key industry events, and organised one final conference. TEAMING.AI also joined the AI4MANUFACTURING Cluster and participated in 5 cluster workshops alongside 13 other H2020 and Horizon EU research projects to expand its impact.
A recording of the final workshop is available here for viewing on YouTube. Additionally, 10 more short videos introducing the TEAMING.AI concept and summarizing its research activities are accessible via the project’s YouTube channel.
The project’s website, designed and maintained by CORE, will continue as a central hub for useful information and resources. Visitors can learn more about important research activities performed and results through press-releases, newsletters, open-access scientific papers and public deliverables that can be found on the website.
The project has also shaped significant online communities, with more than 1.300 followers on LinkedIn and 900 on X.
The dissemination and communication activities have played a crucial role in ensuring that the TEAMING.AI project’s activities and results were effectively shared with both scientific and industry communities, as well as the general public.
It was great working with our TEAMING.AI consortium to deliver impactful change in human-AI interactions for the manufacturing sector. Looking forward to future collaborations.
Future-proofing a 120-year old marble quarry
COREbeat is digitally transforming Marini Marmi,
a historic marble quarry in the north of Italy.
Future-proofing a 120-year old marble quarry
Author: Alexandros Patrikios
July 5th 2024
Our sustainable future relies on longevity, which can be ensured through the meaningful restoration and modernisation of our historic past. That is the case for Marini Marmi, a historic stone transformation facility in the North of Italy, which has supplied material for a variety of big structures with significant heritage impact all over the World.
COREbeat is helping this historic facility modernise its legacy equipment and machinery, through the use of cutting-edge predictive maintenance algorithms.
The Dig_IT project
CORE Group’s collaboration with Marini Marmi stems from the Dig_IT project, a Horizon 2020-funded project which aims to address the needs of the mining industry, moving forward towards a sustainable use of resources while keeping people and environment at the forefront. The Marini use-case of the Dig_IT project aims to reduce unpredicted downtime contributing to overall productivity optimisation.
Where COREbeat comes in
COREbeat, CORE Group’s flagship predictive maintenance solution, promises to eliminate downtimes through the use of Deep Learning algorithms. Offering a 360-solution, COREbeat comprises compact hardware, AI-infused software and an intuitive web and mobile UI and has been applied to many production lines across Europe. Its capabilities present Marini Marmi with a pivotal next step in the digitalisation of their facilities.
At the Marini Marmi quarry, COREbeat has been installed in different assets of a large-scale marble-cutting engine. Installation took less than 2 hours, and Marini employees could immediately monitor the behavior of their gang saw. In less than 6 weeks, COREbeat’s predictive maintenance capabilities also became available.
When things break down
After the initial installation and training period, the quarry’s employees received a notification, informing them that one of the parts was in critical condition and required immediate attention. In just one week, the part was now in critical condition, with a COREbeat’s health score below 10% indicating that it is time for maintenance. The factory staff scheduled maintenance for the machine 4 days later, and the machine kept working for another 3 days, breaking down within the indicated timeframe. The results were highly positive for COREbeat, but the Marini Marmi staff were faced with delays that might have been prevented.
Find out more
COREbeat is still up and running at Marini Marmi through the Dig_IT project. If you’re interested in finding out more about Dig_IT, you can follow the project’s dedicated page on social media. For all the rest of our 40+ EU research projects, you can find more information on our dedicated webpage.
And if you’re impressed with COREbeat’s predictive maintenance capabilities, you can reach out to info@core-beat.com, and learn how our team can help you.
Transforming Greek manufacturing with COREbeat
CORE Group’s all-in-one predictive maintenance platform
is the go-to solution for EP.AL.ME., the leading Greek manufacturer.
Transforming Greek manufacturing with COREbeat
Author: Alexandros Patrikios
April 11th 2024
COREbeat, CORE Group’s signature product, is here to cover an inherent need of the manufacturing sector – the seamless operation of its production machines.
COREbeat is an all-in-one predictive maintenance solution, encompassing compact hardware, AI-infused software, and a web and mobile UI. COREbeat operates through collecting data using its built-in sensors in real-time, then employing machine learning algorithms to detect behavioural anomalies and and provide early notifications of upcoming failures.
The collaboration
Since last summer, COREbeat has been the driving force behind EP.AL.ME.’s predictive maintenance capabilities.
A subsidiary of MYTILINEOS, EP.AL.ME. is an Aluminium Recycling company that specializes in the processing and sorting of scrap metal and the production (smelting) of recycled Aluminium billets. Their collaboration with CORE Group started as part of the e-CODOMH cluster, whose mission is to upgrade entrepreneurship and create an added value in the Greek construction sector.
Installation
The initial installation of beatBox, COREbeat’s hardware component comprising IoT and Computing Edge devices, takes only 2 hours. Upon installation, employees can monitor the behaviour of their machine right away, through COREbeat’s intuitive User Interface. The predictive maintenance capabilities begin at 4 to 6 weeks, and that’s when COREbeat’s Deep Learning magic comes in at full force. After this initial training period, factory employees receive instant notifications through the app for any anomalies. This way, they know immediately whenever the operational health of a machine asset starts declining.
Below, we have some photos of the EP.AL.ME. installation by the COREbeat team.
How COREbeat helped EP.AL.ME.
EP.AL.ME. came to appreciate COREbeat’s predictive maintenance capabilities soon after installation. Maintenance employees at the facility were notified that one of the fans in the aluminum recycling facility was in critical condition, a few days after scheduled maintenance had already taken place. The factory staff, upon inspection, could not find the source of the malfunction, so they continued operating the fan normally.
In the COREbeat interface, the fan kept appearing to be in critical and worsening condition over the span of 2 weeks, which led the staff to pause its operation for an unscheduled maintenance check. During this check, they found an issue with the motor belt of the fan, which they would have missed without COREbeat. This saved the facility from unexpected downtime, delays in the factory’s production pipeline, and substantial losses due to production delays.
Find out more
COREbeat’s success relies on CORE Group’s long experience in the field of machine learning for manufacturing. With a long list of over 40 EU projects, CORE Group is turning into a household name in AI technologies and their industrial applications.
If you are interested in COREbeat’s predictive maintenance capabilities, you can reach out to our team and share more information on the needs of your manufacturing facility through info@core-beat.com.
The InComEss project wraps up
Authors: Clio Drimala, Dimitris Eleftheriou
19th March 2024
Having successfully completed 4 years of operations, the InComEss project officially wrapped up its activities last month, and held the project’s Final Review Meeting with the European Commission’s Project Officer on March 13, in Brussels, at the premises of SONACA.
With a core team of 18 partners from 10 countries, InComEss entered into force in March 2020. Now, after a four-year lifespan, the project has yielded remarkable results, including more than 17 open-access academic publications, and has driven outstanding research on the development of polymer-based smart materials with energy harvesting and storage capabilities in a cost-efficient manner for the widespread implementation of the Internet of Things (IoT).
CORE Group was involved in various tasks within the project framework to expand InComEss’s impact. In particular, we were responsible for devising and managing the consortium’s exploitation strategy, as well as leading the dissemination and communication strategy.
Project overview
Besides our involvement, overall achievements of the project include the development of:
Piezoelectric and thermoelectric energy harvesters with a proven ability to generate electricity through mechanical vibrations and temperature differences.
Monolithic printed supercapacitors that demonstrated their efficacy to store the harvested energy when integrated with a conditioner circuit and generators.
A power conditioning circuit that enhances energy transfer efficiency between generators and end-use electronics.
A miniaturized Fibre Optic Sensors (FOS) interrogator, with reduced power consumption, was showcased for its utility in energy harvesting.
Furthermore, Bluetooth Wireless MEMS and FOS communications were optimized and seamlessly integrated into an IoT platform, offering data monitoring capabilities. Among the research highlights being implemented within InComEss are also three impactful use-cases within the aeronautic, automotive, and smart buildings sectors.
Exploitation activities
The exploitation activities encompass an exhaustive market analysis targeting the consortium’s end users and other markets that could potential leverage the project’s innovations. The specific markets addressed were: 1) Smart Buildings, 2) Aeronautics, 3) Automotive, 4) Oil & Gas Pipelines, 5) Sports Environment, 6) Pacemakers, 7) Railway, and 8) GPS tracking devices. We identified market barriers that would slow down the adoption of the project’s technologies, which we categorized in regard to their nature (Sociopolitical, Economic, Environmental, Technological, Organisational). Based on the information provided, unique selling points of the results with commercial orientation were discerned.
Moreover, results were identified with a clear IPR protection path and exploitation route option. Partners decided whether they would use their results for further research or commercially. We developed business models for the more marketable results based on sustainability-oriented archetypes. The business model included the list of partners participating in the commercial exploitation and their associated activities and resources required to bring the system to the abovementioned market segments. Potential avenues such as ΣEureka and InnoEnergy were considered to reduce the initial investment costs and improve access to market.
The activities were manifested in the development of business plans for the Automotive and Aeronautics use cases. The analysis considered the potential benefits that the route-to-market partners would receive, namely Photonfirst and Smart Material and specifically the point where they would expect a return on their initial investment if they further progressed their results. Based on the activities and resources needed, an appropriate revenue model was in place to perform a financial analysis for both use cases. Moreover, we worked on a cost-benefit analysis for the end users to understand their benefit of acquiring the commercialized version of the InComEss system. Specifically, the aeronautics scenario included an installation in the wing slats, while the automotive scenario in the exhaust systems.
Dissemination and Communication Activities
When it comes to dissemination and communication (D&C), CORE devised and oversaw the dissemination and communication strategy, working hand-in-hand with the entire consortium to maximize the project's impact and resonance.
The InComEss team generated 17 open-access scientific articles, an important legacy of the project, and plans to publish 7 more in the upcoming months. Partners also participated in 32 events delivering 50 presentations and a lecture, presenting 5 posters and promoting InComEss through 2 exhibit booths and a stand in landmark industry-related events.
Beyond that, 2 workshops were organized namely, Mid-Term Workshop on InComEss EU Project and the InComEss Final Workshop. Video recordings from the workshops are available to watch here and on YouTube [Part 1], [Part 2]. 11 more short, engaging videos introducing the InComEss concept and recapping its research activities are also available on the project’s YouTube Channel.
The project’s website, designed and maintained by CORE, will continue as a central hub for useful information and resources. Visitors can learn more about important research activities performed and results through 11 press-releases, 10 newsletter issues, open-access scientific papers, public deliverables and training materials that can be found on the website.
The project has also shaped significant online communities, with more than 1000 followers on LinkedIn and 700 on X, another reflection of the overall effectiveness of the InComEss D&C strategy.
It has been a pleasure working with all our partners for the InComEss project.
Until the next one.
The MOSES project reaches a highly succesful conclusion
Authors: Pantelis Papachristou, Konstantinos Nikolopoulos
13th March 2024
After 3 and a half years, the MOSES project has reached its culmination, marked by a closing conference held online. The central goal of the project was to enhance the Short Sea Shipping (SSS) component of the European container supply chain by implementing the following three groundbreaking innovations:
The development of a hybrid electric feeder vessel, equipped with a robotic container-handling system, to increase the utilisation rate of small ports.
The establishment of a digital collaboration and matchmaking platform to match demand and supply of cargo volumes, utilising Machine Learning (ML) to maximise Short Sea Shipping traffic.
The introduction of an autonomous vessel maneuvering and docking scheme, based on the cooperation of a swarm of autonomous tugboats coupled with an automated docking system.
Our role in the project
As part of the project, CORE has been involved in the third innovation concept, pioneering the transition from traditional docking procedures to an autonomous swarm of tugboats. These advancements were facilitated by creating a sophisticated simulation environment and the application of ML techniques, which refined docking strategies. This digital twin technology, coupled with an AutoPilot control system, exemplifies a significant leap forward in maritime operations, reducing docking time and enhancing port service availability and environmental sustainability.
Machine learning approach
Initially, our team created a virtual environment to simulate the real-life components, such as the port, water mass, tugboats and containership. To ensure fidelity to actual conditions, the virtual environment integrated results from hydrodynamic simulations conducted by MOSES partners, analysing the navigation and evaluating the hydrodynamic parameters, such as the friction resistances for each ship object separately. Additionally, Finite Element Model simulations (FEM) were employed to assess the interactions between the tugboats and the containership, by evaluating force-reactions and stresses.
MOSES Unity test scene displayed during training of 3 push agents next to the “Advanced Ship Controller” and “Behaviour parameters” component.
The simulation environment served as a training environment for the developed swarm intelligence machine learning algorithm, allowing agents to learn from their experiences. Specifically, the agents (tugboats) were trained using deep reinforcement learning techniques, where the learning procedure is based on the interaction of the agents with the environment and the accumulation of feedback (rewards or penalties), while the agents collected observations through LiDAR and GPS sensors. The goal was to discover optimal strategies that maximise cumulative rewards over time. The developed digital twin was deployed at the edge, along with an AutoPilot system to control the steering and thrust of the tugboats based on the digital twin’s inference.
The digital twin was successfully demonstrated and validated in relevant environment (TRL6) at the Faaborg port in Denmark, employing a swarm of two tugboats pushing a bargue towards the dock. The accompanying video below illustrates the precision of the simulation outcomes (displayed on the left-hand side) compared with the actual real-world demonstration (on the right-hand side). This live demonstration underscored a remarkable achievement: more than a 25% reduction in manoeuvring and docking times, leading to a corresponding significant decrease in port emissions and a notable increase in the availability of port services.
Comparison of the simulation outcomoes (left-hand side) with the real demonstration in Faaborg port (right-hand side) considering the scenario where two tugboats push a bargue to the dock.
The commercilisation phase
To ensure successful commercialisation, CORE developed an Innovation Strategy, focusing on clear value propositions and competition mapping. Additionally, CORE developed a model for profit simulation, with a focus on the innovations introduced by the autonomous tugboat system, which is the only technology solution combining autonomous operation, sustainability and safety, with the highest TRL and exposed in real conditions.
Understanding our pilot
For more information on Pilot 1 of the MOSES Project, where our technical team was heavily involved, our consortium partners have created a comprehensive video explaining the AutoDock System and how it works. You can watch it below.
Over the past 42 months, we were very happy to work closely with our consortium partners to successfully deliver an autonomous vessel manoeuvring and docking system which has the potential to completely transform the Short Sea Shipping and container supply chain of the European Union.
We look forward to future, even more fruitful collaborations.
CORE Group contributes to new publication
Author: Dimitris Eleftheriou
6th March 2024
The latest open access book by Springer, titled “Artificial Intelligence in Manufacturing”, includes our work as part of the Teaming.AI consortium.
The Teaming.AI project
Teaming.AI is an Horizon 2020 project which aims to drive industry-wide change in smart manufacturing, through the introduction of higher customisation and personalisation of products and services in AI technologies. The main instrument is the development of an AI-driven Decision Support System, where decisions can be either taken by a human stakeholder or automatically by an AI service.
“Artificial Intelligence in Manufacturing”
The research findings of the project have led to a contribution in an academic book published recently by Springer, titled “Artificial Intelligence in Manufacturing”, edited by John Soldatos, which gathers findings across different EU research projects to provide comprehensive coverage of AI technologies and their various use cases in manufacturing, encompassing both Industry 4.0 and Industry 5.0 solutions.
This book, a part of the AI4MANUFACTURING cluster, features 27 chapters from the XMANAI, STAR, Teaming.AI, AI-PROFICIENT, Knowledge, MAS4AI, COALA, and KITT4SME EU projects. It presents a diverse range of innovative solutions for artificial intelligence (AI) in manufacturing. Our work for Teaming.AI is presented in Chapters 4, 5 and 23 of the book.
CORE Group’s contribution to the publication
CORE Group has contributed to Chapter 23, where we discuss insights and recommendations identified through the project, which can be used to design effective human-AI collaboration systems that enhance productivity, innovation, and social welfare.
Our main contribution lies in identifying existing issues when implementing human-AI collaboration systems in an industry setting. Through this demand-driven approach, our team was able to identify areas where further technological development was needed to ensure successful implementation of Industry 4.0 initiatives.
This process enabled the TEAMING.AI consortium to drive tech development with a market-first mindset. Our key findings show that end-users have a preference for easy-to-integrate, pilot-validated solutions with short-term value potential.
This publication is an important milestone for the project and the CORE Group team, which continues to produce cutting-edge research in the area of AI in manufacturing.
You can access the full book here. For more information on Teaming.AI, you can visit the dedicated project website, and follow us on LinkedIn and X.
The CAPRI project reaches its conclusion
Authors: Konstantina Tsioli, Ioannis Maimaris, Ilia Kantartzi
7th November 2023
After four years, the CAPRI project has come to an end with its final results and an online closing conference. CAPRI’s main goal was to develop cognitive solutions to the Process Industry, in order to facilitate its Digital Transformation. CORE Innovation Centre has been involved in various tasks as part of the project, and we are excited to see project outcomes reach their maturity.
As part of the project, our team developed advanced deep learning models for anomaly detection and Remaining Useful Life estimation of critical components in the asphalt use case. We were also responsible for delivering some of the consortium’s exploitation activities, as well as fully managing all project-related communication tasks.
The goal of CAPRI was to develop, test, experiment and deliver an innovative Cognitive Automation Platform which incorporates cognitive technologies, such as artificial intelligence, machine learning, and advanced automation, to enhance the operations within the Process Industry. Critical outcomes include actions to enhance the flexibility of operations, making the processes more adaptive and responsive, as well as actions to improve operational performance by reducing costs, improving maintenance efficiency, optimising resource utilisation and more.
Deep learning models
The deep learning tools developed by our team have been applied to a use case for asphalt production (EIFFAGE), aiming to reduce maintenance and spare parts costs related to critical operations and to enhance the reliability and robustness of the maintenance system. More specifically:
Anomaly Detection: By leveraging deep learning techniques, this model excels at identifying potential malfunctions in the machinery of asphalt use case. It acts as an ever-watchful guardian, constantly monitoring the baghouse system to alert for anomalies before they become critical issues. This proactive approach allows maintenance to be optimised and to minimise unexpected disruptions.
Remaining Useful Life (RUL) Estimation: Extending the anomaly detection model, our team went one step further by estimating the remaining useful life of critical components. In the EIFFAGE use case, the critical sensor is located at the entrance of the baghouse. This component is essential for maintaining the efficiency and quality of the involved processes. As an outcome, through our RUL estimations, we can accurately predict the time until the next failure of this critical component, offering manufacturers with the foresight needed to plan maintenance activities effectively.
CORE Anomaly Detection model for critical component constant monitoring and for providing possible alerts prior malfunctions.
These solutions have implications for other industries and, once applied, can potentially increase cost efficiency in the steel, aluminum and copper, cement, pharmaceuticals and glass manufacturing industries. More information on the EIFFAGE use case can be found here.
The commercialisation phase
Our innovation team contributed to the exploitation of CAPRI project outcomes by analysing the financial sustainability of the applicable exploitable results, utilising our custom Profit Simulation Tool. This endeavor aimed to gain insights into the financial requirements and resources necessary to introduce the solutions to the market and identify feasible scenarios for the commercialization phase.
This involved estimating Revenues and Costs for a 5-year post-project horizon. The knowledge accumulated throughout the project, which involved the analysis of market conditions and customer segments, was further developed, projecting this analysis into the future for the market. Initially, the analysis focused on customer segments related to the project use case industries, with plans to later expand to include industries identified through the replication analysis. Various scenarios were examined, to pinpoint a pragmatic and viable strategy for partners to implement so they can successfully bring their solutions to market and deliver a sizeable impact for CAPRI on the EU process industry, while also developing their business.
Communication activities
On the communication side, our consortium participated in 32 events over the years, with a total of 13 publications and 16 articles published. The project performed exceptionally well on social media, garnering over 900 followers on X (formerly known as Twitter) and over 1300 on LinkedIn. Additionally, our team ran a YouTube account, which hosts 21 videos with over 2000 views in total. The project website, which was designed by CORE IC and officially launched in the early months of the project, has gained 8400 visits in the three-year run of the project, and it will continue to serve as a central hub for all project deliverables.
It's been a pleasure working with our consortium to deliver cognitive solutions to the European process industry. To stay in touch with the project and its partners, you can visit the dedicated website, or follow the CAPRI accounts on LinkedIn and X.
The Level-Up project has wrapped up with its Final Conference
Authors: Dimitris Eleftheriou, Ioannis Meintanis, Yianna Sigalou
17th October 2023
After four fruitful years, the Level-Up Project has wrapped up with its final deliverables and a Final Conference held in Brussels, Belgium. The project aimed to develop a platform that extends the useful life of major capital investments.
As part of the project, CORE Innovation Centre developed a range of models using ML-based algorithms across different use cases. Our team was also responsible for exploitation and communication activities for the Consortium.
The aim of Level-Up was to offer a scalable platform covering the overall life-cycle of critical role, big industrial machines or their components, starting from the initial digital twins setup to facilitate predictive maintenance, modernisation actions to diagnose and predict the operation of physical assets, even to the refurbishment and re-manufacturing activities towards the end of a machine’s life.
Different machine learning models
The machine learning (ML) based tools developed by our team have been applied to 4 different manufacturing lines (ESMA, LUCCHINI, TOSHULIN, and IPC) at the component, work-station & shopfloor level, with different technologies used depending on the specific needs, and available data for each pilot line.
For ESMA’s cold forming press, our team implemented AI based anomaly detection (AD) algorithms using state-of-the-art Deep Learning (DL) architectures, such as Auto-Encoders and proprietary unsupervised learning algorithms. By utilizing vibration signals and a variety of IoT sensors placed in the equipment, the models can look for patterns in data that indicate failure modes for specific components (e.g. bearings) and provide insights in real-time for the machine operator.
The TOSHULIN production line consists of a large industrial vertical lathe (SKIQ16-v2), with the workpiece clamped on a clamping plate which rotates when in operation. The end-user requirement was to focus on the lubrication system of the cutting tool, to detect anomalies and assess its operation capabilities. To achieve this, we developed a combination of tools, which utilizes a forecasting model to predict the future machine states and the behavior of the oil particles, together with a flexible monitoring mechanism which utilises dynamic thresholds to detect anomalies.
LUCHINI is a full production line for machining railway axles, and for Level-Up we developed an AD procedure using multi-sensorial vibration data. The goal was to facilitate predictive maintenance for the two most critical machines of the production line. The AD procedure is currently at the on-line/production stage, and we continue to monitor the performance and accuracy of the models used.
For IPC/CRF’s pultrusion pilot line, machine learning algorithms for AD and quality control have been developed and integrated with their upgraded monitoring dashboard to assist the operator in decision making and process monitoring procedures.
A go-to-market pathway for the consortium
Our team also led on exploitation activities for the project, to maximise the impact of its results. We developed a detailed exploitation plan for 26 of the project results, across 6 different sectors for our Consortium partners. For each use case, a detailed business plan was developed, which included:
Innovation Management Activities: We analysed the external ecosystem through which Level-Up can evolve, using different strategic tools, like SWOT and Porter’s Five Forces. We analysed the market for each sector, as well as potential market barriers that might slow the adoption of the technologies developed.
Business Models: For each use case, we developed a detailed business model using the Business Model Canva tool, identifying unique selling points, customer personas, costs, potential revenue streams, and key go-to-market activities.
Exploitation Roadmap: We developed detailed exploitation roadmaps and commercialization analyses for the project technologies, accompanied by a 5-year financial plan, which partners can use as a reference in their go-to-market journey.
Reaching out to the community
Finally, CORE Innovation Centre was responsible for handling the communication and dissemination activities of Level-Up.
The project excelled on social media, attracting 904 followers on Twitter and 981 on LinkedIn. During the project, we created 22 videos overall, which collectively received over 3,600 views.
With support from our partners at AIMEN and Innovalia, we carried a series of summer workshops to showcase the project's final results. We also co-organized the project's final conference in Brussels, with more than 70 people in attendance.
Through its four-year run, our Consortium submitted over 7 papers in open access journals, with 4 more pending approval for publication, and attended over 60 industry or scientific events.
Level-Up has been a major milestone for CORE Innovation Centre & CORE Group, setting us on a mission to transform the way digital technologies are implemented in manufacturing & beyond. It has been a pleasure collaborating with our partners across Europe.
The new era of autonomous tugboats and intelligent docking for large ports | MOSES project
MOSES project
The new era of autonomous tugboats and intelligent docking for large ports
Author: Manthos Kampourakis
January 11th 2023
Nowadays, hub port operations are becoming less efficient due to congested waterways, manoeuvring and berthing processes that are error-prone, time-consuming, costly, vulnerable to disruptions (e.g., strikes), and accidents with significant environmental impact. The MOSES project tries to tackle all these challenges by adopting an autonomous vessel manoeuvring and docking scheme that provides operational independency from the availability of port services. This innovative scheme is based on the cooperation of a coordinated swarm of autonomous tugboats that automates manoeuvring and docking where CORE Innovation is in charge.
The first part for which CORE Innovation was responsible was the design and development of a swarm of intelligent virtual tugboat agents, capable of both performing accurate docking of a large containership and coordinating their actions. The goal of the swarm was to pull off simulations of two virtual scenarios which demonstrate the added swarm intelligence factor. For the first scenario, starting from an initial distance of about 80m from the dock, the push tugboat agent’s mission, was to assist on berthing while the large containership would apply corrective movements using its bow thruster. The pull tugboat agent aimed at maintaining the mother vessel’s yaw angle close to zero. For the second scenario, a swarm of four tugboat agents in total learnt to assist on the berthing operation (two push agents) while again maintaining the mother vessel’s yaw angle (two pull agents).
In this video, the sped-up demonstration of the first simulation scenario, the accurate docking of a large containership, is displayed. The upper right corner displays the distance between a marked position on the tip of the dock and a point at approximately the middle of the starboard of the vessel. The bow thruster applies corrective movements to the yaw angle using a custom script. The whole system covers about 70m until it reaches the dock at a predefined distance. At this point, the two tugboats decelerate and then an automated system can take over for docking.
To achieve this, the Unity3D simulation environment and the 3D models of all involved actors were used. Real-world performance was achieved by calibrating the environment physics and the development of custom Reinforcement Learning algorithms led to the successful swarm training. All relevant information needed to accomplish each agent’s task were given as inputs; the location, acceleration, and distance of virtual LiDAR sensors. Propulsion and steering control outputs enabled agents’ navigation. Learning was achieved by employing tailor-made reward signals that directed the learning process in their policies and at the same time penalized undesired tugboat actions e.g., collisions.
The second part for which CORE Innovation is responsible within MOSES project, will be completed in 2023. The goal is to repeat the above-described process in a virtual environment using pilot-specific components and train a swarm of agents in a real-life pilot demonstration.
CORE's technical results in the iQonic project
iQonic project
CORE's technical results
Author: Spiros Fourakis
22nd September 2022
Figure 1: Original wafer image (left) and corresponding defect detection result from CORE defect detection model (right).
The iQonic project is entering its last few months and the project’s final webinar will take place in the morning of September 22nd. The project centers around a scalable zero-defect manufacturing platform covering the overall process chain of optoelectrical parts, facing the challenge of dealing with the evolution of the equipment, instrumentation and manufacturing processes they support. CORE’s efforts focused on deep learning algorithms to ensure strong prediction and detection skills and respective reactions to achieve zero defects.
More specifically, within the iQonic project, CORE has developed a new complete framework for defect detection and quality prediction of final assembled product in two demo cases AlPES and Prima. In particular, CORE’s contribution for Alpes Demo Case concerns the development of a machine learning-based defect detection solution which is focused on defect identification on the wafer parts (Figure 1).
Figure 2: Validation in early anomaly detection
Concerning the Prima Demo Case, a new and complete framework for prediction quality of a multi-laser emitter product, based on deep learning models was developed. The framework consisted of two stages: (1) early anomaly detection, focused on investigating the suitability of the final assembled product during early production stages, and (2) accurate prediction, which focused on estimating the quality index of the final product from its’ early production stage. Both models were successfully validated with real offline data from the production line. Especially, the anomaly detection model correctly predicted all the normal assemblies and nearly all defective assemblies, with only 3 false negatives (Figure 2).
Figure 3: Validation in accurate prediction of total power
Similarly, the quality prediction model demonstrated considerably low prediction errors and good generalisation performance (Figure 3).