Projects
Our ongoing projects
3DAIQ
AI that recognizes industrial defects
TEC Eurolab, Concept Reply
Through the 3DAIQ project, TEC Eurolab and Concept Reply have developed an AI-powered software capable of automatically detecting defects in any component. The system, based on industrial computed tomography and Additive Manufacturing, analyzes 3D images obtained through X-rays to identify and measure anomalies. The platform also offers a user-friendly interface and detailed reports, applicable across various manufacturing sectors.
Building on a previous solution — developed in collaboration with Blue Tensor — which could autonomously recognize defects in a single specific component analyzed through industrial tomography, the 3DAIQ project enabled TEC Eurolab and Concept Reply to introduce a significant improvement by expanding the software’s analysis capabilities to any industrial component. The goal was to make this technology more objective and standardized, while reducing analysis time. The project also leveraged Additive Manufacturing: within a collaboration with the University of Padua, aluminum alloy samples were 3D-printed and subjected to CT scanning to train the AI model.
In its latest version, the new software has achieved the expected results: the system is equipped with a simple and effective user interface that allows automatic and accurate detection of defects in any type of component. The solution, suitable for different inspection needs in the manufacturing field, can also determine the size of the identified defects — a crucial parameter in engineering — and their position, subsequently providing a detailed report of the results.
Contain
Digital Twin on the shelf for chemical inventories.
TEC Eurolab, Centiloc, SOLID, Thinkin
Centiloc, a pioneering hardware innovation startup, proposes a solution aimed to develop a Digital Twin “on the shelf’” for chemical inventories, using Centiloc’s NFC microgeolocation and filling-level sensors. This technology will provide real-time tracking and data analytics, improving production efficiency, quality control, workforce optimization, waste reduction (15-30%), and worker safety.
Key representative industrial partners will test and implement the solution ensuring practical validation and business expansion:
- One of the biggest watch manufacturer represented by its integrator SOLID, that uses chemicals for watch manufacturing and quality control
- Thinkin, an Italian based company specialized in creating Digital Twins through IoT technologies for shop floor operations management and optimization
TEC Eurolab’s role will be to represent the chemical lab which faces chemical storage issues and tests the Digital Twin
DSRRD
DataSpace for Rapid R&D
TEC Eurolab, Collins Aerospace, SUPSI, IndustryApps
SSEGAI
Structurescope EG + Artificial Intelligence
TEC Eurolab, Santer REPLY, Aleistyn, ABF LT, InTechCentras
The Structurescope EG + Artificial Intelligence (SSEGAI) technology is designed to determine by eddy current (EC) non-destructive testing (NDT) methods the chemical-physical, mechanical properties of electrically conductive, mostly metal alloys, carbon-fiber reinforced polymers (SRFP) and metal ceramics, semiconductor materials in the Industrial 4.0/Industrial 5.0 paradigm. The technology consists of the Qu-Scope measuring device and the Nanoinspect AI Solution software suite.
The technology consists of the Qu-Scope measurement device and the Nanoinspect AI Solution software suite.
SSEGAI is based on three innovative components:
- Methods of determining the structure of materials by measuring the amplitude-phase-amplitude characteristics (APAС) of the poly-harmonic EC NDT response signal.
- APAC precision measurement methods.
- Artificial Intelligence (AI) methods for the interpretation of measured APAC into consumer-acceptable values of quantities characterizing material properties.
SSEGAI can replace up to 80% of traditional metallurgical tests (MT), allows to determine the properties of materials of finished products without making test samples, and can be applied where traditional methods are not available.
Determination of the materials properties with the help of SSEGAI is carried out within 30 seconds, and its application significantly reduces the use of financial, time, material and technical and human resources of production processes.
TEC EUROLAB’S ROLE: TEC Eurolab is going to test the SSEGAI technology and compare it with our accredited testing methods. The goal is to see whether the lab activity can benefit from this solution in terms of reduced time, materials, technical and human resources.
Energy Master
AI technology to optimize energy efficiency and sustainability
TEC Eurolab, Santer REPLY, Industry Innovation Center, GridDuck, Diversey
The Energy Master project represents a significant advancement in energy efficiency and sustainability. By leveraging AI technology, the project aims to optimize the self-consumption of energy generated by TEC Eurolab’s rooftop solar panels. The AI system will precisely predict power usage and production, aligning the operation of high-energy-consuming machinery with peak solar production periods. This strategic synchronization enhances the utilization of solar energy, thereby advancing TEC Eurolab’s sustainability goals and reducing reliance on external energy sources. This innovative approach not only maximizes the benefits of renewable energy but also significantly contributes to the company’s overarching objectives of environmental responsibility and operational efficiency.
Predictive Q+
Advanced Software Platform for Streamlined Production Processes
PredictiveDataScience, TEC Eurolab, NV Bekaert SA, INNOVAPLAST
Predictive Q+ is an advanced software platform for optimizing manufacturing processes. Originating from the automotive and alloy industries, it acts as a comprehensive process control station, monitoring, detecting, and predicting operational and quality issues.
Developed by the startup PredictiveDataScience in collaboration with industry partners and powered by artificial intelligence, it provides tools for detailed analysis, disruption prediction, and optimization—improving both quality and resource efficiency. Its flexible interface allows management of multiple data sources, making it adaptable to any manufacturing company. Through collaboration with consortium partners, Predictive Q+ is extending its capabilities to additional sectors. TEC Eurolab leverages the platform to enhance predictive maintenance of industrial CT scanners. NV Bekaert SA benefits from predictive maintenance applied to patenting and galvanizing lines, as well as quality control of wire products. INNOVAPLAST will use the platform to optimize process parameters in the production of bio-based bioplastic raw materials.
Through these collaborations, Predictive Q+ demonstrates its ability to drive innovation and address sector-specific challenges. Its versatile and powerful features, combined with a user-friendly interface, enable manufacturing companies to achieve agility, efficiency, and competitiveness. It enables companies to optimize their production processes, respond to market demands, and remain competitive in a dynamic economic environment.
HCP-bO
Industrial process optimization algorithms based on operator preferences
TEC Eurolab, Santer Reply, SUPSI, Industry Innovation Cluster, Smartzavod
Many industrial processes are difficult to optimize due to the lack of performance index definition, unavailability of sensors (and, indeed, measurements), and difficulties in setting up objective functions. In such scenarios, expert operators’ knowledge drives the tune-up phase of the industrial processes/applications. Indeed, a programming-free approach to transfer such human knowledge to the production plant can be implemented to allow any operator to naturally/intuitively transfer his/her expertise to the target machine/robot.
The HCP-bO project exploits preference-based optimization algorithms to address such needs. By adopting such an approach, it is possible to train an algorithm by means of experiments performed by an expert operator, guiding the optimization process. The optimization algorithm can then elaborate a machine configuration depending on different objective functions. The system provides suggestions to the human operator, assisting him/her in the optimization activities. In addition, an enhanced version of this algorithm (including both qualitative and quantitative optimization capabilities) will be developed to maximize the flexibility of the optimization toolbox.
The developed algorithms (SUPSI + Santer Reply SpA) will be tested in two relevant use cases:
[Tec-Eurolab]: optimization of industrial computed tomography scan parameters;
[SMARTZAVOD]: optimization of polymer printing and automatic post-processing parameters for a hybrid 3D printer.
CAMPRES
Innovative composite materials for containing battery elements
TEC Eurolab, GHEPI, XBW Lithium Battery REvolution, ENEA, Democenter-Sipe Foundation, CertiMaC Materials Energy Innovation
The Kick-off Meeting of the CAMPRES Industrial Research Project was conducted as part of the “Call for strategic industrial research projects aimed at the priority areas of the Smart Specialization Strategy 2023-2024”, Action 1.1.2 “Support for collaborative research of research laboratories and universities with companies” of the Emilia-Romagna Region (PR-FESR EMILIA ROMAGNA 2021-2027).
CAMPRES focuses on the development of a family of innovative composite materials for the containment of battery elements for static and/or portable energy storage suitable for mass production with advanced injection molding technologies.
Kick-off meeting of the GIMCANA project
CAMPRES defines a modular architecture that provides flexibility in the design of energy storage systems, enabling it to meet the specific requirements of different use scenarios while supporting more efficient and optimized installation. Mass production technology enables cost reduction and improves the efficiency of the manufacturing process, ensuring a fast time-to-market. The proposed concept promotes the definition of standards that favor the interoperability of battery elements between different areas of use in line with the principles of the CIRCULAR ECONOMY and SECOND-LIFE management of Lithium Batteries
HEATBETA
Design and fabrication of a turbine blade for high-temperature applications made by a Laser Engineered Net Shaping technique using a high entropy alloy.
TEC Eurolab, Poggipolini, EMAG Group, University of Bologna, BI-REX Competence Center, Democenter-Sipe Foundation
The development of innovative alloys with high-temperature resistance properties remains imperative in the field of materials engineering research. Since they are intended for use in the construction of turbine components, these alloys must exhibit high fatigue, creep, oxidation, and corrosion resistance at extreme temperatures. The new generation of high entropy alloys shows potential for achieving optimal performance in such components. These alloys are based on five or more main elements, forming stable solid solution structures at elevated temperatures. To date, components made from high entropy alloys are mostly produced using conventional techniques, which require additional post-fabrication treatments.
These processes are characterized by long production times and high operational costs, making them cost-effective only for mass production. Laser additive manufacturing offers a solution to these issues, as the production of full or near-net-shape products with reduced surface finishing requirements is made possible in a short time. Products manufactured using laser-based additive processes generally develop better properties compared to those obtained through conventional methods, thanks to the rapid cooling associated with these processes. In this research project, the high entropy alloy (HEA) AlCrFeNiCu is referenced, which is intended to be used to fabricate a turbine blade for high thermomechanical loads through an additive process. The first stage of the project, and a prerequisite for the design, involves the static and fatigue experimental characterization of the HEA material obtained through the laser manufacturing process, to be conducted on specimens. The blade is then designed based on a well-established aerodynamic profile, applying numerical methods such as topological optimization to define the optimal arrangement of internal channels in terms of structural response. Since fatigue and fretting fatigue are the dominant failure modes for this type of component, a proper design assessment must be performed on both fronts. The blade is then fabricated using the Laser Engineered Net Shaping (DED) technique, followed by a finishing operation using a 5-axis machine. A 3D scan will be conducted to verify compliance with the imposed dimensional and geometric tolerances. Finally, the thermomechanical properties will be evaluated and compared to those of a turbine blade produced using conventional materials and manufacturing processes.
GIMCANA
High-Resistance Metal-Composite Joints
TEC Eurolab, Future Technology Lab (UniPr), Interdepartmental Center for Advanced Mechanics and Materials Industrial Research (UniBo), CRIT, BEAMIT, Blacks, Mind Composites, Bercella
GIMCANA is a European Union co-funded project that will develop a new multi-material joining technology, to be applied to a wide range of structural components in any industrial sector, in order to promote the substitution of metal in favor of a fiber-reinforced polymer (PFR) and the resulting extreme weight reduction. The innovative technology, namely SLIM2CORE (Standalone Lattice Insert for Metal-COmposite COnnection REinforcement), is based on inserts made by low-cost, high-value Additive Manufacturing (AM), which will enable a stronger connection between metal and PFR. The project covers the entire value chain of the innovative technology, from materials, design, manufacturing, testing and quality control, evaluating its potential on a use case and the great impact of its adoption. GIMCANA will also be developed consistent with Circular Economy principles, showing how a high-performance metal-PFR component can be achieved by using recycled carbon fibers for the latter, and performing a life cycle analysis (LCA). The project has a duration of 30 months.
Our R&D Manager Fabio Esposito and TEC Eurolab’s Tomographic Center will contribute to the project for the testing and quality control part of the new technology, evaluating its overall performance through CT scans.
X-RAI
X-RAI – AI based X-ray analysis for quality prediction of casted products
TEC Eurolab, Volkswagen, Fraunhofer, Loamics, Tvarit & Technische Universität Braunschweig
AMULET
3DMgO – 3D Printing materials based on magnesium oxide binder
TEC Eurolab & ParaStruct
AMULET is a HORIZON 2020 project that aims to harness the innovation potential of SMEs in the field of light construction by creating new value chains through cross-sectoral knowledge exchange in the automotive, aerospace, aviation, energy and construction sectors. During 2022, TEC Eurolab presented, in collaboration with the Austrian company ParaStruct, the project “3DMgO – 3D Printing materials based on magnesium oxide binder.” The project assumes that many raw mineral materials are becoming rare due to over-exploitation of resources. In the 3DMgO project, ParaStruct and TEC Eurolab want to show how magnesium oxide residues produced during the magnesite calcination process and/or obtained from refractory ceramics residues from the steel industry can be made usable through additive manufacturing. The project aims to contribute to the circular economy and waste valorization in the construction world.
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