Robust Intelligent Metrology

Metrology plays a critical role in all high value manufacturing processes. There is an increasing demand for ever more data to control how complex products are manufactured, and ensure they consistently meet quality standards. Our aim is to provide more metrology solutions that can be deployed in a factory environment thereby reducing the reliance on specialist metrology facilities – reducing cost and measurement process time, and increasing the usefulness of the data.

The Robust Intelligent Metrology Lab is focused on providing metrology systems that function as close to the manufacturing action as possible; this can be at the line side, in the cell, in the machine or even within the manufacturing process. We focus on three core activities:

  • Creating and developing new robust sensor technology by designing sensors that are better able to cope with challenging industrial applications.

  • Defining new ways to automate and enhance the use of existing sensors with greater intelligence, by providing tools and algorithms that assist in the selection, placement and configuration and optimisation of sensors.

  • Integrating sensor systems as part of collaborative metrology networks to provide more robust measurements, with greater coverage of the measurands, and increased confidence in the data.

Automation is at the heart of the technology we create, and it is a strong theme within all our metrology activity. We aim to embed intelligence within our technology that allows it to function robustly in an automatic way, allows the measurement to be configured with less human intervention, or enables other processes to run more autonomously in a robust and reliable way.

Staff and Students involved in this Research:


Research Associates:

  • Dr Istvan Biro
    "Robot Deployed High Performance Metrology for Flexible and Automatic Surface and Part Geometry Inspection and Measurement"

  • John Hodgson
    “3D Vision Technologies and their Application to Industrial Manufacturing”

    3D vision technologies are becoming cheaper and more commonly used by the manufacturing industry. To utilise any technology it is essential to understand and predict its performance limitations in new applications. The characterisation of 3D sensors is currently limited by the variety of technologies available and quantity of variables, which affect performance. Current methods do not take into account, for instance, performance degradation due to surface finish and work piece angle. These variables are crucial to the successful scanning of components, yet no method exists to characterise their effects. This project aims to develop sensor characterisation methods to provide predictive capability. The ability to firstly characterise scanner performance, and subsequently model ideal scanning orientations is beneficial to a wide range of autonomous industrial applications from bin picking to freeform robotic assembly and reverse engineering.


PhD Students:

  • Peter Wilson
    "Modelling and Understanding the Performance of Complex Industrial Processes using Large Data Sets"


MSc Students:

  • Tom Hovell
    "In-Process Fibre Based Surface Metrology for Micro-Scale Metrology in Liquid and Harsh Environments"
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