<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-22T07:57:45Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/72F04C83-E451-424F-8030-C6C65D88F1AC" ns1:id="72F04C83-E451-424F-8030-C6C65D88F1AC"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/4CF8ACE5-DC83-45DD-AF5D-CD223B5BA65C" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/647A6577-555F-4367-93F2-9ADDEF797CD5" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/647A6577-555F-4367-93F2-9ADDEF797CD5" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2023-11-30T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/868BC9AE-8A1C-4985-BD47-1B4630C238FE" ns1:rel="FUND" ns1:start="2023-05-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10073076</ns2:identifier></ns2:identifiers><ns2:title>Machine Learning for Improving Product Quality in Advanced Manufacturing</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Grant for R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>The project we are proposing is the development of a set of machine learning models for the prediction of quality failures within advanced manufacturing.

Through mitigative, rather than reactive action, the risk of non-conforming product (waste) can be reduced. This runs in line with the steppingstones of the industry 4.0 roadmap, where applications of AI are the last step towards building the world class production facilities of the future. By reducing the risk of waste products firms will be able to produce product right first time, thus reducing supply chain shortages and increasing their ability to pivot to the demands of other sectors quickly.</ns2:abstractText></ns2:project>