<?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="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/F179F618-48A7-4D73-98A7-8728D02D6CD0" ns1:id="F179F618-48A7-4D73-98A7-8728D02D6CD0"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/BA2BA45F-F248-4D6F-8D88-442C820A49A7" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/0A1F9EC5-8082-4EA4-9237-DFED2552E05D" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/0A1F9EC5-8082-4EA4-9237-DFED2552E05D" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/FAA1BF44-E24B-4356-B622-D7392CB690EF" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2025-02-28T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/B924D193-E041-4AD8-A4B0-2A3DEDE107BF" ns1:rel="FUND" ns1:start="2024-08-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10120398</ns2:identifier></ns2:identifiers><ns2:title>Pullmaflex Enhancing Automotive Seat Comfort</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>Pullmaflex-UK, specialises in custom-engineering DC motors and power actuators for automotive applications globally. Shape memory alloys (SMAs) are pivotal in automotive applications, comprising around 200 components, such as thermostatic valves. They offer benefits like noise reduction, enhanced comfort, and energy-autonomous operation in compact spaces, making them indispensable for revolutionising automotive systems.

Our project, in collaboration with A4I ASTUTE partner, aims to revolutionise our SMA actuator manufacturing quality control by introducing machine learning technology. This strategic initiative targets efficiency optimisation, defect reduction, and product quality enhancement. Leveraging ASTUTE's expertise in machine learning, we aim to transition from manual data review to real-time analysis. With faster cycle times and data from 36 stations, our comprehensive solution will promptly identify maintenance and quality issues, minimising downtime, enhancing operational efficiency, and reducing waste by identifying faulty parts early in the production line. The integration of machine learning enables proactive maintenance and provides valuable insights for continuous improvement and innovation in our existing SMA product line. This initiative underscores our commitment to technological advancement and excellence in automotive manufacturing, ensuring sustained competitiveness in the industry.</ns2:abstractText></ns2:project>