Dr Jon Stammers
MEng, PhD MIET
Advanced Manufacturing Research Centre (AMRC)
Theme Lead - Connectivity & AI
Full contact details
Advanced Manufacturing Research Centre (AMRC)
Sheffield Business Park
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.
- Error motion trajectory-driven diagnostics of kinematic and non-kinematic machine tool faults. Mechanical Systems and Signal Processing, 164. View this article in WRRO
- Machining centre performance monitoring with calibrated artefact probing. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 235(10), 1569-1587. View this article in WRRO
- The application of machine learning to sensor signals for machine tool and process health assessment. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 235(10), 1543-1557. View this article in WRRO
- Comparing approaches for multi-axis kinematic positioning in machine tools. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 235(10), 1698-1711.
- Predicting Geometric Tolerance Thresholds in a Five-Axis Machining Centre, 93-100. View this article in WRRO
- Instrument for Soundscape Recognition, Identification and Evaluation (ISRIE): Signal Classification. The Journal of the Acoustical Society of America, 123(5), 3081-3081.
- Smart Manufacturing, Smart Connected World (pp. 141-169). Springer International Publishing View this article in WRRO
- Predicting Geometric Tolerance Thresholds in a Five-Axis Machining Centre In Niezrecki C & Baqersad J (Ed.), Structural Health Monitoring, Photogrammetry & DIC (pp. 93-100). Switzerland: Springer.
Conference proceedings papers
- Machine tool health verification. European Society for Precision Engineering and Nanotechnology, Conference Proceedings - 19th International Conference and Exhibition, EUSPEN 2019 (pp 190-193)
- 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
- 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)
- Instrument for soundscape recognition, identification and evaluation (ISRIE): Signal classification. Proceedings - European Conference on Noise Control (pp 105-109)
- Instrument for soundscape recognition, identification and evaluation (ISRIE): Signal classification. Proceedings of the Institute of Acoustics, Vol. 30(PART 2) (pp 356-363)
- Automated Fault Diagnosis with Calibrated Artefact Probing. Structural Health Monitoring 2019, 24 October 2018 - 26 October 2018.
- Research group
Integrated Machining Group (IMG)
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.