Strategic Line: 4.0 Digital Manufacturing
PREDICAI project
Prediction by Artificial Intelligence
FUNDING: DIH4AI OPEN CALL 2, European Union’s Horizon 2020 research and innovation programme under grant agreement N. 101017057.
CONSORTIUM: Alchemy Machine Learning S.L. (coordinator) - IMH Campus.
OBJECTIVE: PREDICAI will be centered on monitoring bearings health state based on information from accelerometers and electrical variables of the motor. This will be accomplished b the combination of supervised and un-supervised machine learning techniques that will include a classification model such as LSTM and the use of time series with the development of a Recurrent Neural Network. Data will be generated by attaching an accelerometer to the bearing. Data will be generated on a test bench where these failures can be caused, both by simulating the errors with a tool with unbalanced weights, and by putting defective bearings on the bench.
DIH2 FACTOR PROGRAMME
FOR: Companies in the Manufacturing and/or Logistics sector offering robotics, automation or artificial intelligence products or services, and companies wishing to increase their investment in the development of such products or services.
OBJETIVE: DIH² believes in the potential of robotics to transform manufacturing agility in small and medium-sized enterprises (SMEs) and drive economic growth across the European Union. In short, the goal is to accelerate factories through robotics.
FIELD OF ACTIVITY: DIH² Factor is aimed at start-up and scale-up companies (up to Series A) specialising in: robot hardware, artificial intelligence and data analytics, human-robot interaction, automation for increased sustainability, intralogistics or warehouse management in factories, and decentralised production.
MEMBERS: IMH Campus is member of DIH² Factor network. See more members...
FUNDING: DIH² Factor is a financing programme for SMEs promoted by the DIH² and BLUMORPHO networks.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824964
WEBSITE: https://www.dih-squared.eu/
If you think your company is willing to invest in Start Up or Scale Up, please apply: DIH² Factor application
AIMAN project
Taking steps forward in the automation of machining using artificial intelligence
FUNDING: AI REGIO, European Union’s Horizon 2020 research and innovation programme under grant agreement N. 952003.
CONSORTIUM: WOLCO (coordinator) - IMH Campus (technical management) - ZITU (partner)
OBJECTIVE:
The development of the Artificial Intelligence necessary to control the real wear of the tools and be able to predict the useful lifetime would allow for an optimal use of the tools. In this way, problems such as discarding tools before the end of their useful life, quality in the parts due to worn tools, having to re-produce a part due to the poor quality of it due to an unsuitable or deteriorated tool increasing the production expense, will be reduced to a minimum. On the other hand, a company that manufactures the tools will have the ability to advise the end user in making decisions regarding the tools. This AI-driven solution also allows for a structural business model change, moving from a traditional marketing model to a more sustainable one related to servitisation.
DIGIVACH Project
Data science for collaborative exploitation in the advanced manufacturing VALUE CHAIN through intelligent and interoperable management of DIGITAL models.
FUNDING: HAZITEK/ ZE-2021/00026
CONSORTIUM: ETXETAR (coordination) - AINGURA IIOT SLU, FAGOR AUTOMATION S.COOP., FAGOR EDERLAN S.COOP., INDUSTRIAS MAIL S.A., IZADI MECANIZADOS, S.L., MICRODECO S.A., SAVVVY DATA SYSTEMS, S.L., TALLERES ARATZ S.A., ZUBIOLA S.COOP., AFM. Subcontracted entities: FAGOR AOTEK S. COOP., EDERTEK, IKERGUNE, A.I.E., IMH CAMPUS, INVEMA, MICRODECO INN, MONDRAGON GOI ESKOLA POLITEKNIKOA (MGEP), VICOMTECH.
OBJECTIVE: To research and generate knowledge on the exploitation of data in the value chain by means of interoperable Hybrid Digital Models, fed by both internal and external data, which are managed intelligently, and which offer solutions to real problems that occur during production, significantly increasing the competitiveness of companies and giving rise to the development of innovative advanced products and services that position these and their value chains as leaders in their sectors.
BERREKIN project
BERREKIN Project, in-circuit operators for natural language interaction in advanced manufacturing environments
EKIN project
Towards the interaction in Natural Language with Industrial Production Machines
FINANCING: ELKARTEK / file number KK-2020/00055.
CONSORTIUM:
Vicomtech (Coordinator) - IMH - TEKNIKER - IKOR Technology Center - UZEI - UPV / EHU (Department of Electricity and Electronics)
EDGE4FoF project
Research on balanced hybrid architectures EDGE and Cloud for the Factory of the Future
FINANCING: HAZITEK / file number ZE-2020/00017
CONSORTIUM / OUTSOURCED ENTITIES:
ETXE-TAR S.A. (LIDER) - AINGURA IIOT - AFM - FAGOR AUTOMATION - GAINDU - IKUSI - TITANIUM INDUSTRIAL SECURITY /FAGOR AOTEK - VICOMTECH - INVEMA - IMH - IKERGUNE - UNIVERSIDAD DE DEUSTO
Retro Connect
Project It addresses the obsolescence problem faced by the oldest machine tools in industrial parks, and which affects the productive needs of its user companies.
FINANCING: HAZITEK / file number ZL-2019/00874.
CONSORTIUM / OUTSOURCED ENTITIES:
FAGOR AUTOMATION (líder) AFM - METALÚRGICA DE BOLUETA - SAVVY DATA SYSTEMS - FAGOR AOTEK - INVEMA - IMH
TDCON4.0
Technologies and solutions for the digitization of the 4.0 construction sector
FINANCING: ELKARTEK / file number / KK-2019/00075.
CONSORTIUM:
UPV/EHU -Department of Thermal Machines and Motors (Coordinator) - IMH - SABICOLABS S.A
