Metrology for the Factory of the Future

17IND12 Met4FoF

This project is a joint research project within the European Metrology Research Programme EMPIR. The major goals and objectives of the project are given in the downloadable Publishable Summary. A brief outline is given below.

In order to get in contact for more details, email the Coordinator.

Invitation to join virtual project workshop

In a virtual workshop on 15th July 2020, the project team will present recent advances and results in sensor network metrology and the calibration of digital sensors.

To find out more, read the blog entry here and register simply by using our contact form.

The “Factory of the Future” (FoF) as an inter-connected production environment with an autonomous flow of information and decision-making constitutes the digital transformation of manufacturing to improve efficiency and competitiveness. Transparency, comparability and sustainable quality all require reliable measured data, processing methods and results. This project will establish a metrological framework for the complete lifecycle of measured data in industrial applications: from calibration capabilities for individual sensors with digital pre-processed output to uncertainty quantification associated with machine learning (ML) in industrial sensor networks. Implementation in realistic testbeds will demonstrate the practical applicability and provide templates for future up-take by industry.

The objectives of the project are:

  1. To develop calibration methods for industrial sensors of dynamic measurements such as acceleration, force and pressure with digital data output (data streams) and internal digital pre-processing, including the extrapolation of the measurement uncertainty from individually calibrated sensors to other individuals of the same type by means of co-calibration and statistical modelling.
  2. To develop and demonstrate methods enabling digital sensors to provide uncertainty and/or data quality information together with the measurement data.
  3. To develop a cost-efficient in-situ calibration framework for MEMS sensors measuring ambient temperature for their integration into an industrial sensor network with metrological quality infrastructure.

  4. To develop and assess data aggregation methods for industrial sensor networks based on machine learning and efficient software architectures, addressing synchronisation of measurements, making use of redundancies of measurements, taking into account uncertainty from calibration and network communication issues, including strategies for balancing cost versus uncertainty and explore methods to identify the measurement coverage and accuracy required for process output targets.

  5. To improve existing industry-like testbeds for sensor networks in manufacturing environments towards the implementation of a metrological quality infrastructure and to facilitate the take up of the project outputs by the stakeholders, especially the manufacturing industry.

Creating Impact

This project has formed a joint stakeholder advisory board (SAB) together with the project 17IND02 SmartCom. The SAB so far includes 10 partners from industry, academia and standardisation, who have helped to decide on the most suitable sensors and interfaces for the project. Moreover, together with the SAB member TNO, the project presented a joint publication at the 2019 IMEKO TC10 conference, about the implementation of continuous quality concepts in research. At invited presentations at the “19th International Metrology Congress”, “MathMet Conference 2019” and at the “Dresdner Sensorsymposium” the project has also been disseminated  to a wide community in science and industry.

In order to increase the uptake of the mathematical methods developed in the project, a public GitHub repository has been launched (https://github.com/Met4FoF). This repository connects all software developments from the project and employs modern software quality principles with CI technologies. All partners who write software code are regularly adding to this repository. The joint repository is set up such that the individual repositories of the partners can be organised separately and was recently presented at the German conference “deRSE19 - Conference for Research Software Engineers in Germany” in June 2019. 

 

Latest News

Virtual Stakeholder Workshop 15th July 2020

The project team will present recent advances and results in sensor network metrology and the calibration of digital sensors.

Read more

Project outline

The “Factory of the Future” (FoF) as an inter-connected production environment with an autonomous flow of information and decision-making constitutes the digital transformation of manufacturing to improve efficiency and competitiveness. Transparency, comparability and sustainable quality all require reliable measured data, processing methods and results. This project will establish a metrological framework for the complete lifecycle of measured data in industrial applications: from calibration capabilities for individual sensors with digital pre-processed output to uncertainty quantification associated with machine learning (ML) in industrial sensor networks. Implementation in realistic testbeds will demonstrate the practical applicability and provide templates for future up-take by industry.

Project structure