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Professor Darek Ceglarek profile photo

Professor Darek Ceglarek

Co-Investivator

University of Warwick

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Professor Darek Ceglarek is EPSRC Star Recruit Research Chair at WMG, University of Warwick, United Kingdom (UK) and a CIRP Fellow. Previously, he was Professor with tenure in the Department of Industrial and Systems Engineering at University of Wisconsin, Madison, USA. He received his Ph.D. in Mechanical Engineering from University of Michigan-Ann Arbor, USA in 1994. His research focusses on smart manufacturing, closed-loop quality control; and data mining/AI for root cause analysis across design, manufacturing, and service.  He has mentored numerous post-doctoral fellows, and PhD and master’s students.   Several of his former post-doctorate fellows and graduated PhD students are professors or associate professors at universities in the US, UK, Australia, Korea, and India. His research has created impact on digital manufacturing technologies, predictive modelling in automotive assembly systems and quality control. He has been PI/co-PI on research grants of over $41M funded by: US (NSF, NIST); United Kingdom (UK) (EPSRC, InnovateUK, Advanced Propulsion Centre (APC) and High Value Manufacturing (HVM) Catapult); and European Union (EU) (Framework Programme, Marie Curie) and global industry (more than 30 OEMs and SMEs). He has published over 250 papers, received several Best Paper Awards, most recently the 2022 IEEE Outstanding Paper Award for the IEEE Transactions on Industrial Informatics, IF=11.648 (2021); and is listed by Stanford University among Top 2% of the world’s leading scientists and among Top-1.1% within subfield ‘Industrial Engineering & Automation’.    Solutions and technologies developed from his research has been implemented by industry, for example, (i) quality improvement by reducing Six sigma variation (Stream-of-variation) as part of Chrysler Operating Systems and led to the 1st US automotive factory to achieve Toyota benchmark in dimensional quality of automotive body assembly; (ii) remote root cause diagnostics of CT/MRI scanners maintenance and service by General Electric-Healthcare worldwide as InSite OnWatch®; (iii) Six-sigma variation root cause analysis by General Motors (GM) as a BIW Analyzer Software; (iv) Real-time process adjustments to ensure weld consistency and high process capability for scaling up and rapid deployment of remote laser welding (RLW) for high volume production by Jaguar Land Rover which led to the 1st UK application of RLW in production of lightweight aluminium door assembly; (v) Process scale-up from concept to full scale prototype methodology for RLW of Battery Enclosure assembly for Electric Vehicles (EVs) by Constellium and BMW which led to the pre-production prototype build and showcase at the annual industrial exhibition CENEX Low Carbon Vehicle (Cenex-LCV) and Connected Automated Mobility (Cenex-CAM) events  He has received numerous awards including the 2018 Jaguar LandRover’s (JLR) ‘Innovista’ Award for the WMG-JLR collaborative project ‘Laser Welded Lightweight Aluminium Door for SUV’ as the most innovative project in category of ‘piloted technologies’ (selected out of 75 entries); and his EU Factory-of-the-Future RLW Navigator programme was selected as a success story by the EC in 2015. He received the 2007 UK EPSRC Star Award given to ‘exceptional senior faculty, recognised international leader in his research field’, the US NSF 2003 CAREER Award (NSF's ‘most prestigious awards in support of junior faculty’); 1999 Outstanding Research Scientist Award from University of Michigan; the 1998 Dell K. Allen Outstanding Young Manufacturing Engineer of the Year Award from the SME. He has served on numerous Editorial Boards and is an Associate Editor (Europe) of the ASTM Smart and Sustainable Manufacturing Systems Journal.  Prof. Ceglarek served as Chair of the Quality, Statistics and Reliability Section of INFORMS; Program Chair for the ASME Design-for-Manufacturing Life Cycle Conferences, Assoc Editor of the IEEE Transactions on Automation Science and Engineering, and of the ASME Trans, Journal of Manufacturing Science & Engineering.  
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