LAY2FORM is a multimaterial processing learning hub available at M-NEST by INEGI. This testbed integrates into a single platform 3 manufacturing stages: lay-up, in-situ consolidation and hot forming. Firstly, tailored lay-ups of composite prepregs with metal foils are performed in an automatic way, in contrast with the conventional approach of manual layup. Following this process, applied pressure and temperature assists the in-situ consolidation of composite prepregs. In the third stage, the pre-heating of composite-metal tailored blanks and its 3D hot forming is supported by selective heating. This unique industry-scale equipment able to process multimaterial systems enables the production of hybrid metal-composite components with optimized efficiency and properties.
The robotized metal forming platform developed by ENSAM (Metz campus) for M-NEST activities allows to provide knowledge, models, simulations, and remote access to testbed.
This technological platform is used for industrial applications as well as research projects and teaching activities in order to answer to the critical challenge due to the increasingly fast development of new alloys and multi-metal products. Simulations and experimental data allow predicting the product quality as well as equipment’s behavior.
The manufacturing islands in the Aalto Factory of the Future (AFoF) consist of an assembly line, AGV mobile robots, collaborative robots, a 3D scan station and Monitoring stations. This empowers learning and teaching factories in the M-NEST project
AFoF is a facility for innovation, research and education and comprises a space shared by humans, robots and production stations. The facility serves as a platform for projects in the area of advanced information technologies applied to future production systems. It focuses on achieving revolutionary high flexibility by exploiting the architecture of modular autonomous intelligent production units.
The SUPSI Mini-Factory is an open environment for applied research and teaching. The aim of the Mini-Factory is to create a platform where researchers, students and industries meet, develop and empower the transfer of knowledge, thus applying modern and advanced production technologies and methods in the context of Industry 4.0.
JointDesigner is a M-NEST- I software testbed that automates and simplifies the design process of a structural joint, reducing time, chance of error and need to recheck calculated values and, hence, improving the overall quality and precision of the desired results. With this digital tool, it is possible to share the analysis with co-workers, save joint data for future use, export utility to transfer data to a spreadsheet or other software, printing and e-mailing of results and calculate the problem at hand with various analytical methods simultaneously.
Dinasore is a digital-twin software developed by FEUP and available as learning hub of M-NEST-RIS. In an industrial context, this tool allows to understand the past (by tracking historical context and data), to monitor present conditions (being regularly updated with sensor data) and to predict the future (by synthesizing and contextualizing historical and real-time data to give insights into potential future states).
WELDPRINT is a machine tool available as a learning hub of M-NEST-RIS by CTU. This hybrid manufacturing testbed combines additive WAAM and subtractive milling processes. The patented procedure uses machining after every AM step/layer for alignment of the produced part geometry. The subsequent AM operation is done on well-defined geometric surface. The process is suitable for production of thin walled parts with closed geometry, parts with internal channels or parts with added geometrical features.
Robotont robots is testbed available by University of Tartu (http://robotont.ut.ee). Robotont is an omnidirectional mobile platform that can be used to demonstrate, teach and test computer vision, artificial intelligence, and collaborative robotics capabilities and solutions. Robotont combines hardware and software for highly engaging experience-based learning and makes the full use of ROS (Robot Operating System) support. Depth camera, omniwheels, transparent and modular design are some of the key features of Robotont hardware.
LAY2FORM is a multimaterial processing learning hub available at M-NEST by INEGI. This testbed integrates into a single platform 3 manufacturing stages: lay-up, consolidation and hot forming. Firstly, tailored layups of composite prepregs with metal foils are performed in an automatic way, in contrast with the conventional approach of manual layup. Following this process, applied pressure and temperature assists the in-situ consolidation of composite prepregs. In the third stage, the pre-heating of composite-metal tailored blanks and its 3D hot forming is supported by selective heating. This unique industry-scale equipment able to process multimaterial systems enables the production of hybrid metal-composite components with optimized efficiency and properties.
CogniWeld (LMS testbed) is the MNEST tool to approach welding, automation, cognitive control, Quality Asseessment, even Cell Safety. This testbed is used to aggregate data related to the robotized welding process and raise discussions about cognitive control & automation.
iiLAB is an extensive research infrastructure adapted to INESC TEC diversified areas of activity, where it is possible to carry out demonstration of concepts and advanced technologies in the areas of robotics, automation, industrial cyber-physical systems (Internet of things) in the form of a show-room and experimentation and prototyping space for technological companies.
The iiLab has the objective of disseminating the state-of-the-art in advanced production technologies through the demonstration of research, experimentations and advanced training results. Furthermore, iiLab supports technology-based innovation in public and private organizations, thus contributing to the development of their skills in the development, adoption, and implementation of advanced leading to sustainable competitiveness in the circular context.