Dr Jon Stammers

MEng, PhD MIET

Advanced Manufacturing Research Centre (AMRC)

Theme Lead - Connectivity & AI

Dr Jon Stammers
j.stammers@sheffield.ac.uk

Full contact details

Dr Jon Stammers
Advanced Manufacturing Research Centre (AMRC)
Factory 2050
Sheffield Business Park
Sheffield
S9 1ZA
Profile

Jon has completed a MEng and PhD in Electronic Engineering. His doctoral research focused on the automated identification of urban and natural audio signals, testing a novel time-domain feature extraction method, alongside researching ensemble neural network classifiers.

Jon has been part of the Process Monitoring and Control (PM&C) team at the AMRC since 2013, and a Technical Fellow since 2017. He has lectured at the AMRC Training Centre, assisting with the design of the Biomanufacturing (BMan) course. Prior to working at the AMRC he taught Physics at a local secondary school.

Research interests

Jon’s research interests include: The Application of Machine Learning Techniques to Identify Significant Performance Indicators in Large Data Sets; Correlation of Data Sources from Across the Shop Floor; Data Visualisation Optimisation.

The Connectivity & AI theme are concerned with all elements of enabling Smart Factories, including defining open-source architectures for data collection, data visualisation, computer vision, application of AI and learning algorithms, and data science.

Publications

Journal articles

Chapters

Conference proceedings papers

  • Moore J, Mantle A & Stammers J (2019) Machine tool health verification. European Society for Precision Engineering and Nanotechnology, Conference Proceedings - 19th International Conference and Exhibition, EUSPEN 2019 (pp 190-193) RIS download Bibtex download
  • Dominguez-Caballero J, Stammers J & Moore J (2019) Development and testing of a combined machine and process health monitoring system. Procedia CIRP, Vol. 86 (pp 20-25), 16 October 2019 - 18 October 2019. View this article in WRRO RIS download Bibtex download
  • Bunting O, Stammers J, Chesmore D, Bouzid O, Tian GY, Karatsovis C & Dyne S (2009) Instrument for soundscape recognition, identification and evaluation (ISRIE): Technology and practical uses. 8th European Conference on Noise Control 2009, EURONOISE 2009 - Proceedings of the Institute of Acoustics, Vol. 31(PART 3) RIS download Bibtex download
  • Stammers J & Chesmore D (2008) Instrument for soundscape recognition, identification and evaluation (ISRIE): Signal classification. Proceedings - European Conference on Noise Control (pp 105-109) RIS download Bibtex download
  • Stammers J & Chesmore D (2008) Instrument for soundscape recognition, identification and evaluation (ISRIE): Signal classification. Proceedings of the Institute of Acoustics, Vol. 30(PART 2) (pp 356-363) RIS download Bibtex download
  • ROOKER T, STAMMERS J, WORDEN K, POTTS G, KERRIGAN K & DERVILIS N () Automated Fault Diagnosis with Calibrated Artefact Probing. Structural Health Monitoring 2019, 24 October 2018 - 26 October 2018. RIS download Bibtex download
Research group

Integrated Machining Group (IMG)

Grants

Jon has been involved in a number of research projects, spanning various TRL’s, during his time at the AMRC. Most recent projects include: 

  • ATI, Securing Aerospace Manufacture in the UK, (Co-I), October 2020 - September 2023, £974,000 (£295,000 to AMRC): Grant reference: 47052
  • Innovate UK, Data-driven manufacturing, (Technical Lead), April 2021 - March 2023, £111,000. Grant reference: KTP 12279
  • EPSRC, Autonomous Method for Detecting Cutting Tool and Machine Tool Anomalies in Machining, (Co-I), February 2020 - January 2023, £1.02M: Grant reference: EP/T024291/1
  • Innovate UK, AI for Machining Design (ARMADa), (PI), February 2020 - January 2022, £190,000 (£40,000 to AMRC): Grant reference: 105626
  • Innovate UK, Machine Tool Inspection Best Practice and Data Capture, (Technical Lead), June 2016 - June 2018, £91,000
Professional activities

Member of the IET.

Sit on the executive committee for the IET Manufacturing Technical Network.