To remain competitive, European industrial production and assembly lines have to be transformed following the concept of the “Factory of the Future” (FoF) – a manufacturing environment of inter-connected devices and with an autonomous flow of information leading to automated decisions. This requires reliable and formally described information about the manufacturing processes, derived from trustworthy measurement data at every stage of data analysis. Currently there is no corresponding metrological infrastructure to support these developments. An international metrology, measurement and data analysis infrastructure is needed to support the digitisation of European industry.
Metrology is an essential part of the backbone of today's quality infrastructure with the provision and further development of high-precision measurements, calibrations and conformity assessment as well as standardization and harmonization. These key areas are facing enormous challenges resulting from the digital transform in industry:
- New algorithm-based manufacturing methods, such as additive manufacturing, and the fast development of 5G communication technologies require new measurement capabilities in order to enable traceability.
- Digitally transformed industries require machine-readable, automated certification processes.
- Intelligent sensors with pre-processed data output require new frameworks and standardization to enable traceability and allow quality assessments.
- Automated data communication and interpretation requires reliable knowledge about dimension, unit, type and quality of the data being transferred.
- Intelligent data analysis methods, such as deep neural networks, need to be assessed regarding their reliability in order to gain trust in their results.
The proposed project in this regard aims at bridging "traditional" metrology and the concepts of a "factory of the future", in which data from large networks intelligent sensors is analyzed and used automatically. For the individual sensors methodologies are required to calibrate their digital pre-processed outputs with the aim to utilize the built-in computing power of today's sensors to allow for an "advanced traceability", for which calibration information is used reliably on the sensor itself and the corresponding (dynamic) uncertainties are being communicated together with the processed values. Based on such a framework a corresponding data communication protocol is to be developed which takes into account the communicated sensor uncertainties, network communication issues and allows for reference sensors in the network for "self-calibration". With industrial sensor networks reporting data quality (e.g. uncertainties), data analysis methods could take advantage of this information in order to produce more reliable results. Therefore, an assessment of the uncertainty propagation abilities of the machine learning tools in use is required.
The consortium of the first stage, namely the co-authors of the PRT (download here), were:
- from Germany: PTB, HBM, Endress+Hauser, Imagineon, Univ. Saarland, TU Ilmenau, Univ. Kassel, Fraunhofer FOKUS, Fraunhofer IPK
- from France: LNE, CEA-LIST-LADIS
- from the UK: NPL, Strathclyde Univ., Cambridge Univ.
- from Finland: VTT, Mapvision, Aalto Univ.
- from Italy: INRIM, SPEA, CSP
- from the Netherlands: VSL, NEN, TNO, Sioux LIME
- from Turkey: TUBITAK UME
- from Romania: Romanian Measurement Society
- from the USA: National Instruments
- from Belgium: Vrije Univ. Brussel
The EMPIR Programme
Joint research projects in EMPIR are coordinated by a staff member of a partner NMI (national metrology institute) elected during the partnering meetings. The project duration is 36 months and average funding for the Industry Call 2017 is approx. 2.8 Mio Euros, from which 30% is for external (non NMI/DIs) partners. Interested parties can join the project either as funded partner (if eligible), collaborator or unfunded partner. Details about funding and types of collaboration can be found here.