Dr Peter Matthews, MA DipCS PhD MIET FRSA
(email at firstname.lastname@example.org)
Dr Peter Matthews is a Lecturer in Design Informatics at the School of Engineering and Computing Sciences. His core interests lie in supporting the earliest phases of the design process (concept generation and initial detailing). The early phases of the design process benefits from great freedom, but this freedom comes at the cost of being able to objectively and quantitatively determine the potential performance of these ideas. It is this uncertainty that forms the core of Dr Matthews’ research.
The uncertainty in the early phases of the design process is due to a number of reasons: lack of understanding, or knowledge, of the theoretical aspects (epistemic uncertainty), lack of information about what the customer wants (exogenous uncertainty), and the random variation that occurs in any process (aleatory uncertainty). By gaining a better handle on these uncertainties, a designer is able to make better informed decisions that will result in more robust designs. Part of the solution here is to incorporate the product’s service life data, and to use this to support design decisions for new products. The resulting designs are more robust and are better able to perform in environments that were not originally anticipated.
The implementation of these decision support tools has strong parallels with the Artificial Intelligence community.
In addition to the ability to decide under uncertainty, Dr Matthews’ research also speaks to the need to support transparent decision-making in the design process. This is achieved through a combination of extracting designers’ knowledge and being able to explicitly demonstrate the driving factors for a given decision outcome.
Complementary to Dr Matthews’ primary research in design decision support, he is also actively investigating process monitoring methods. Similar to uncertainty in design, there is also epistemic and aleatory uncertainty in the monitoring process. Here, the aim is to understand what is happening in a process, either within a manufacturing facility or a biological process, with the ultimate aim of being able to make well informed, or robust decisions on what action to take to control the process.
Dr Matthews’ current research is centred around industrial data analysis. This involves collecting and analysing data obtained either from production process monitoring (eg, SCADA logs from various production machinery) or service life data (eg maintenance logs). This information can give a good picture of how a system or product is currently performing, but it only provides a suggestion of how it might perform in the future. This uncertain data forms part of the knowledge foundation for future design and operation decisions.
Current techniques that are being used to tackle this problem are: Monte Carlo simulations, Evolutionary Algorithms, Bayesian Belief Networks, and interval probabilities (p-boxes).
- Artificial Intelligence and Machine Learning
- Data mining
- Design process
- Engineering Design
- Engineering Uncertainty modelling and management
- Game theory
- Knowledge Management
- Monte Carlo methods
L1 Computer Aided Drawing(20 hours/year.)
L1 Manufacture(11 hours/year.)
L3 BEng Mechanical CAD(8 hours/year.)
L3 Management (Product Life Cycle and Game Theory for Engineers)(9 hours/year.)
L4 Advanced Engineering Design(10 hours/year.)
- Aldinger, Lars, Alzaga, Aitor, Baguley, Paul, Bittner, Thomas, Boër, Claudio, Bossin Donna, Bramley, Alan, Brissaud, Daniel, Bünting, Frank, Bufardi, Ahmed, Chryssolouris, George, Colledani, Marcello, Dinkelmann, Max, Dori, Dov, Draghici, Gheorge, Draghici, Anca, Du Preez, Nicolaas Deetlefs, Enparantza, Rafael, Fischer, Anath, Giess, Matt, Grozav, Ion, Haag, Holger, Hayka, Haygazun, Jovane, Francesco, Kals, Hubert, Kind, Christian, Kjellberg, Torsten, Komoto, Hitoshi, Krause, Frank-Lothar, Lutters, Eric, Maropoulos, Paul, Matthews, Peter C., Mavrikios, Dimitris, Molcho, Gila, Monostori, László, Niemann, Jörg, Noel, Frédéric, Nyqvist, Olof, Paris, Henri, Rogstrand, Victoria, Romero, Ricardo, Rothenburg, Uwe, Roucoules, Lionel, Sacco, Marco, Salonitis, Konstantinos, Schneor, Ronit, Shpitalni, Moshe, Shtub, Avraham, Sivard, Gunilla, Stavropoulos, Panagiotis, Stolz, Marcus, Te Riele, Freek L.S., Tichkiewitch, Serge, Tolio, Tullio, Tomiyama, Tetsuo, Toxopeus, Marten, Turc, Cristian, Urgo, Marcello, Van Driel, Otto P., Van Houten, Fred J.A.M., Váncza, József, Westkämper, Engelbert & Xirouchakis, Paul (2009). Design of Sustainable Product Lifecycles. Berlin: Springer.
- Lomas, CDW, Maropoulos, PG & Matthews, PC (2007). Implementing Digital Enterprise Technologies for Agile Design in the Virtual Enterprise. In Digital Enterprise Technology. Cunha, PF & Maropoulos, PG Springer. 177-184.
- Ullah, B, Trevelyan, J & Matthews, PC (2012), Structural optimisation using boundary element based level set method, Proceedings of the 20th UK conference of the Association for Computational Mechanics in Engineering (ACME). University of Manchester, Manchester, ACME, 333-336.
- Matthews, PC & Philip, ADM (2011), Bayesian Project Monitoring, in Culley, S.J., Hicks, B.J., McAloone, T.C., Howard, T.J. & Clarkson, P.J. eds, 1: Proceedings of the 18th International Conference on Engineering Design (ICED11). Copenhagen, Design Society, 69-78.
- P.C. Matthews (2010), Comparing Stochastic Design Decision Belief Models: Pointwise versus Interval Probabilities, in J.S. Gero eds, Design Computing and Cognition 4th International Conference on Design Computing and Cognition DCC'10. Stuttgart, Germany, Springer, 327-345.
- Matthews, PC & Coates, G (2007), Pre-Emptive Concurrent Design Planning and Scheduling, Proceedings of the International Conference on Engineering Design 16th International Conference on Engineering Design. Paris, Design Society, Glasgow.
- Matthews, PC (2006), Bayesian Networks for Design, in Gero, JS eds, Design Computing and Cognition'06. Eindhoven.
Journal papers: academic
- Matthews, PC & Philip, ADM (2012). Bayesian project diagnosis for the construction design process. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26(4): 375-391.
- P.C. Matthews (2011). Challenges to Bayesian decision support using morphological matrices for design: empirical evidence. Research in Engineering Design 22(1): 29-42.
- Peter C. Matthews & Chris D. W. Lomas (2010). A methodology for quantitative estimates for the work and disturbance transformation matrices. Journal of Engineering Design 21(4): 413-425.
- Cheung, WM, Maropoulos, PG & Matthews, PC (2010). Linking design and manufacturing domains via web-based and enterprise integration technologies. International Journal of Computer Applications in Technology 37(3/4): 182-197.
- Matthews, PC (2008). A Bayesian support tool for morphological design. Advanced Engineering Informatics 22(2): 236-253.
- Armoutis, N., Maropoulos, P. G. Matthews, P. C. & Lomas, C. D. L. (2008). Establishing agile supply networks through competence profiling. International Journal of Computer Integrated Manufacturing 21(2): 166-173.
- Lomas, C. D. L. & Matthews, P. C. (2007). Meta-Design for Agile Concurrent Product Design in the Virtual Enterprise. International Journal of Agile Manufacturing 10(2): 77-87.
- Matthews, PC & Chesters, PE (2006). Implementing the Information Pump using Accessible Technology. Journal of Engineering Design 17(6): 563-585.
- Matthews, P. C., Standingford, D. W. F., Holden, C. M. E. & Wallace, K. M. (2006). Learning inexpensive parametric design models using an augmented genetic programming technique. Artificial intelligence for engineering design, analysis and manufacturing 20(1): 1-18.
- Matthews, P. C., Blessing, L. T. M. & Wallace, K. M. (2002). The introduction of a design heuristics extraction method. Advanced engineering informatics 16(1): 3-19.
Journal papers: online
- Cheung, WM, Matthews, PC, Gao, J & Maropoulos, PG (2007). Advanced product development integration architecture: An out-of-box solution to support distributed production networks. International Journal of Production Research Advance online publication.
- Matthews, PC, Standingford, DWF & Holden, CME (2003). Method of Design using Genetic Programming. 03812612.4-2211-GB0305175. Application filed: 2 December 2003. Granted: 30 November 1999
- Matthews, PC, Standingford, DWF & Holden, CME (2002). Method of design using genetic programming. GB0228751.4. Application filed: 10 December 2002. Granted: 30 November 1999
- 2012: KTP - Icona Solutions Ltd (£91146.00 from Icona Solutions Ltd)
- 2011: EPSRC Centre for Innovative Manufacturing in Through-life Engineering Services
- 2011: Machine learning of process production monitoring (£25859.00 from 5G Technologies Europe Ltd)
- 2010: EPSRC Summer studentship (in collaboration with BioInnovel)
- 2008: Agent meta-learning for uncertain domains (£18620.09 from Royal Academy of Engineering)
- 2008: Durham University Institute of Advanced Study
- 2004: Framework 6: Virtual Reseach Lab for a Knowledge Community in Production (VRL-KCiP: FP6-507487-2)
- 2004: MACHINE LEARNING OF PROBABILISTIC (£4500.00 from The Nuffield Foundation)
- 2004: NDI RESEARCH (£13000.00 from Northern Defence Industries Ltd)