DEMO4GREEN - Made

Key information

Budget

€ 379.922

European countries

10

Partner

10

DEMO4GREEN

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Problem

The project addresses the issue of carbon footprint in high-tech industries and infrastructure in Europe. Demo4Green, following the concept of “test before invest,” will start with a training module to implement demonstrators in Teaching and Learning Factories (TLFs), aimed at industries, technology companies, and research teams. In addition, it will support SMEs, start-ups, and entrepreneurs with coaching, commercial and technological roadmap development, financing, and IP strategies to bring sustainable technologies to market and improve high-tech infrastructure in RIS countries.

The specific objectives of the project

Initial training: Create a module on how to implement demonstrators in Teaching and Learning Factories (TLFs), aimed at companies and research teams.

Demonstrator selection: Identify demonstrators to be implemented in the consortium’s TLFs.

Business support: Providing coaching to SMEs, start-ups, and entrepreneurs, developing commercial and technological roadmaps, financing, and IP strategies to bring green technologies to market.

The results

Demo4Green will have a positive impact on several sectors:

Employees/workforce: development of digital skills through TLF, with a focus on digital literacy and technology adoption.

Social and economic development: stimulating growth in regions by applying practical collaboration schemes between academia and industry.

Academic solutions: transfer of knowledge and innovative concepts for businesses.

Partnership: promoting new collaborations between industry and universities.

SMEs and start-ups: support for digital transformation and promotion of sustainable cooperation models in production.

The role of MADE4.0

Task Leader for Innovation: MADE manages the Technology Demonstration task in T&L Factories as part of Demo4Green.

Open Call Management: Organize and launch an open call to select innovative technologies to be demonstrated in the partners’ TLFs.

Evaluation criteria: Select technologies based on their potential to contribute to the fight against climate change, assessing various indicators such as reduction in consumption and increase in renewable resources.

Financial support: Provides a €10,000 voucher to implement technology demonstrations, with a total of €80,000 distributed to external participants.

Collaborations: Allows the participation of consortia composed of two entities, including industrial companies, technology companies, RTOs, and universities.

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Product and process development

Product and Process development can be optimized through the combined use of advanced digital technologies, which improve design and industrialization. The digital twin allows for the creation of a virtual replica of the product or production process to analyze performance and identify issues before physical implementation. Virtual commissioning enables the digital testing of production systems, reducing startup times and the risk of malfunctions. Advanced simulation and virtual reality enhance design and testing, allowing for more accurate planning. With PLM (Product Lifecycle Management), it is possible to manage the entire product lifecycle in an integrated manner, optimizing resources and reducing waste.

Artificial Intelligence

Artificial Intelligence optimizes every stage of production, reducing costs and improving operational quality. In quality control, artificial vision systems and machine learning detect defects before they impact production. Predictive maintenance analyzes critical parameters to reduce machine downtime. Autonomous guided systems manage material handling with optimized routes. Advanced industrial data analysis improves planning, energy efficiency, and supply chain management. Indeed, AI for operator assistance provides support with intelligent chatbots and augmented reality, enhancing safety and efficiency.

Connected Infrastructure

The Connected Infrastructure is the cornerstone of industrial digital transformation, enabling the collection and utilization of data from machines, sensors, and software. The digital backbone ensures a continuous flow of information between departments and plants, allowing for predictive analytics and faster decision-making. The use of Big Data Analytics transforms large volumes of data into actionable insights to improve quality, energy efficiency, and productivity. Industrial cybersecurity protects sensitive data from cyber threats, ensuring operational continuity. With this digital infrastructure, companies can reduce waste, anticipate failures, and make processes more sustainable.

Logistics

The digital transformation of logistics and production enables intelligent control of the supply chain and production processes. Thanks to IoT and smart sensors, it is possible to collect real-time data to optimize transportation routes and inventory management. Cloud platforms and predictive analytics support dynamic planning, reducing operational costs. The process Digital Twin allows for the simulation of production scenarios and the identification of inefficiencies before they occur. This integrated approach enables more flexible, responsive production that can quickly adapt to market demands.

Quality

Quality 4.0 represents an evolution in industrial quality control, leveraging advanced digital technologies. Computer vision systems and AI monitor every stage of production, detecting anomalies before they become critical defects. Integration with IoT sensors ensures complete product traceability throughout the supply chain, enhancing the safety and reliability of supplies. Techniques such as X-ray tomography and other non-destructive inspection methods allow for the verification of internal quality without compromising component integrity. This approach reduces waste and rework, optimizing production processes.

Smart Maintenance

Smart Maintenance represents an innovative approach to industrial plant management, leveraging digital tools to prevent failures and reduce operational costs. Predictive maintenance uses IoT sensors and artificial intelligence algorithms to monitor machine conditions and anticipate potential anomalies. Augmented Reality (AR) supports operators with detailed visual instructions, making repairs faster and more accurate. Maintenance planning, based on data analysis and predictive models, optimizes resource usage and reduces downtime. These technologies improve operational continuity and enhance plant safety.

Smart Product and Process

The Intelligent Product and Process enables a new industrial paradigm, where products and machinery are connected and capable of exchanging information in real-time. Thanks to IoT and Smart Connected Products, companies can collect operational data, monitor product conditions, and improve predictive maintenance. Advanced process monitoring allows for the optimization of production and the reduction of waste, while servitization transforms business models, shifting from product sales to the provision of services based on actual use. These technologies enhance operational efficiency and promote more flexible and sustainable production.

Smart Production

Modern industry requires flexible and optimized production systems. Smart Manufacturing integrates advanced digital solutions to improve performance and efficiency. Cobots support operators in repetitive tasks, reducing errors and fatigue. 3D printing enables on-demand production and material optimization. Digital machine monitoring, thanks to IoT and AI, allows anomalies to be identified and consistent quality to be guaranteed. The digital twin of the process allows production scenarios to be simulated and informed decisions to be made. Thanks to these tools, production becomes more responsive, efficient, and sustainable.