Professor Jorn Mehnen

Design, Manufacturing and Engineering Management

Personal statement

I am passionate about Advanced Digital Manufacturing. My research aims to deliver new and exciting scientific insights as well as practical technological solutions that help industry and academia alike. Advanced Digital Manufacturing encompasses Industry 4.0 technology, Cyber Physical Systems (CPS), Industrial Internet of Things (IIoT) and utilises latest developments in Cloud Manufacturing and Big Data Analytics. My work around Design for Industry 4.0 and Digital Manufacturing is aiming to improve existing Manufacturing Systems to make them smarter, more autonomous and agile, cost efficient, better connected and well informed Through-Life. These efforts are supported by research into Additive Manufacturing, Data Analytics, Computational Intelligence and Visualisation. My national and international projects aim at high value industrial applications with impact.

Publications

Three-dimensional subsurface defect shape reconstruction and visualisation by pulsed thermography
Sirikham Adisorn, Zhao Yifan, Liu Haochen, Xu Yigeng, Williams Stewart, Mehnen Jorn
Infrared Physics and Technology Vol 104 (2020)
https://doi.org/10.1016/j.infrared.2019.103151
Green supplier evaluation and selection using fuzzy multi-criteria decision making in Thai tire rubber industry
Srinual Napat, Mehnen Jorn, Sinrat Sittichok
NEUNIC 2019, 6th National and International Conference 2019 on “Educational Innovation for Sustainable Society Development” (2019)
A design approach to IoT endpoint security for production machinery monitoring
Tedeschi Stefano, Emmanouilidis Christos, Mehnen Jörn, Roy Rajkumar
Sensors Vol 19 (2019)
https://doi.org/10.3390/s19102355
Smart sensing and adaptive reasoning for enabling industrial robots with interactive human-robot capabilities in dynamic environments - a case study
Zabalza Jaime, Fei Zixiang, Wong Cuebong, Yan Yijun, Mineo Carmelo, Yang Erfu, Rodden Tony, Mehnen Jorn, Pham Quang Cuong, Ren Jinchang
Sensors Vol 19 (2019)
https://doi.org/10.3390/s19061354
Swarm eye : a distributed autonomous surveillance system
Khan Faisal, Mehnen Jorn, Sreenuch Tarapong, Alam Syed, Townsend Paul
International Journal of Advanced Computer Science and Applications Vol 9 (2018)
https://doi.org/10.14569/IJACSA.2018.091283
Modelling the influence of laser drilled recast layer thickness on the fatigue performance of CMSX-4
Morar Nicolau Iralal, Roy Rajkumar, Gray Simon, Nicholls John, Mehnen Jörn
Procedia Manufacturing Vol 16, pp. 67-74 (2018)
https://doi.org/10.1016/j.promfg.2018.10.173

more publications

Professional activities

EPSRC Peer Review College (External organisation)
Advisor
30/1/2020
Advanced Digital Manufacturing - Stage 2
Speaker
22/1/2020
Computer-Aided Design (Journal)
Peer reviewer
2/12/2019
AI, Robotics, Autonomous Systems and Industry 4.0 – and thanks for all the fish
Speaker
30/9/2019
IET Scottish Workshop on Design and Manufacture for Productivity
Participant
4/9/2019
An Overview of Industry 4.0 for the Information and Industry Development Department of the PRC
Speaker
2/9/2019

more professional activities

Projects

Foreign Higher Expert Collaboration Programme
Li, Yun (Principal Investigator) Mehnen, Jorn (Co-investigator)
14-Jan-2019 - 12-Jan-2021
Autonomous Scheduling and Control of Machines and Robots for Smart Factories
Mehnen, Jorn (Principal Investigator) Li, Yun (Co-investigator) Yang, Erfu (Co-investigator)
01-Jan-2019 - 31-Jan-2021
Automating Process Optimisation from A Metrology Digital Twin
Mehnen, Jorn (Principal Investigator) Fitzpatrick, Stephen (Principal Investigator)
Manufacturing processes have variation of input conditions and environmental and operational disturbances such as material properties or equipment condition changing the behaviour of machining processes. This means that existing models cannot take the next step of in-process control without being dynamic, adapting to the specific conditions of each part. The aim of this project is to investigate new dynamic process models that act as a "digital twin" for forming and machining processes. Every part that passes through the manufacturing process will then carry its own unique set of model parameters derived from metrology data. The work will focus on part machinability, cutting forces, stability and residual stress distortions. Data will be monitored from forming and machining processes such as temperatures, forces, part dimensions and machining vibrations. This data will inform models that will be running in real time, driving predictions which in turn drive forming and machining parameters. The study will provide a strong case for further research into dynamic models that apply active automatic learning to optimise multi-stage manufacturing processes.
15-Jan-2018 - 14-Jan-2019
Automating Process Optimisation from a metrology informed digital twin
Mehnen, Jorn (Principal Investigator)
01-Jan-2018 - 14-Jan-2019
INDUSTRIAL DOCTORATE CENTRE IN ADVANCED FORMING AND MANUFACTURE | See, Chee Keong
Mehnen, Jorn (Principal Investigator) Fitzpatrick, Stephen (Co-investigator) See, Chee Keong (Research Co-investigator)
01-Jan-2017 - 01-Jan-2021
Flexible and Intelligent Path Planning and Control of Industrial Robots towards Autonomous Hot Forging in the Digital Manufacturing Age
Yang, Erfu (Principal Investigator) Mehnen, Jorn (Co-investigator) Mineo, Carmelo (Co-investigator) Rodden, Tony (Co-investigator)
The proposed project aims to enhance the autonomous manufacturing capability of UK industry in metal forming and forging. The project brings together two departments of the University of Strathclyde, namely DMEM and EEE. It augments this knowledge with the experts of Strathclyde’s global strategic partner Nanyang Technology University (NTU, Singapore). With Industry 4.0 being currently widely acknowledged as a key driver of industrial advancement, a strong technologic shift has become apparent within industry to move towards both, more intelligence and more autonomy. Currently, hot forging and forming has benefitted only little from this shift beyond traditional automation. There is a vast opportunity to systematically transform the inherently challenging technologies, namely forming and forging into truly smart and flexible manufacturing systems.
The AFRC offers an outstanding practical background for the applied transformation of Industry 4.0 theories. This project aims at delivering practical demonstrators at TRL 6 through implementing advanced knowledge into intelligent robot behaviour and simulation environments for robotic manipulation and flexible automation into the hot forging area considering the “living” and “dirty” environment of such industries, which requires the consideration of humans, hazardous, dynamic, hot and noisy conditions which did not experience much smart automation yet.
01-Jan-2017 - 30-Jan-2018

more projects

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Design, Manufacturing and Engineering Management
James Weir Building

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