Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-28T13:02:25.753Z Has data issue: false hasContentIssue false
  • English
  • Français

Constraint capture and maintenance in engineering design

Published online by Cambridge University Press:  18 September 2008

Suraj Ajit ,
Derek Sleeman ,
David W. Fowler  and
David Knott
Suraj Ajit
Affiliation:
Department of Computing Science, University of Aberdeen, Aberdeen, Scotland
Derek Sleeman
Affiliation:
Department of Computing Science, University of Aberdeen, Aberdeen, Scotland
David W. Fowler
Affiliation:
Department of Computing Science, University of Aberdeen, Aberdeen, Scotland
David Knott
Affiliation:
Rolls-Royce plc, Derby, United Kingdom
Get access Rights & Permissions [Opens in a new window]

Abstract

The Designers' Workbench is a system developed by the Advanced Knowledge Technologies Consortium to support designers in large organizations, such as Rolls-Royce, to ensure that the design is consistent with the specification for the particular design as well as with the company's design rule book(s). In the principal application discussed here, the evolving design is described using a jet engine ontology. Design rules are expressed as constraints over the domain ontology. Currently, to capture the constraint information, a domain expert (design engineer) has to work with a knowledge engineer to identify the constraints, and it is then the task of the knowledge engineer to encode these into the Workbench's knowledge base. This is an error-prone and time-consuming task. It is highly desirable to relieve the knowledge engineer of this task, so we have developed a system, ConEditor+, that enables domain experts themselves to capture and maintain these constraints. Further, we hypothesize that to appropriately apply, maintain, and reuse constraints, it is necessary to understand the underlying assumptions and context in which each constraint is applicable. We refer to them as “application conditions,” and these form a part of the rationale associated with the constraint. We propose a methodology to capture the application conditions associated with a constraint and demonstrate that an explicit representation (machine interpretable format) of application conditions (rationales) together with the corresponding constraints and the domain ontology can be used by a machine to support maintenance of constraints. Support for the maintenance of constraints includes detecting inconsistencies, subsumption, redundancy, fusion between constraints, and suggesting appropriate refinements. The proposed methodology provides immediate benefits to the designers, and hence, should encourage them to input the application conditions (rationales).

Keywords

Application ConditionsCaptureConstraintsDesignMaintenanceRationales
Type
Research Article
Information
AI EDAM , Volume 22 , Issue 4: Design Rationale , November 2008 , pp. 325 - 343
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Ajit, S. (2008). Capture and maintenance of constraints in engineering design. PhD thesis, Department of Computing Science, University of Aberdeen, Aberdeen, UK.Google Scholar
Ajit, S., Sleeman, D., Fowler, D.W., Knott, D., & Hui, K. (2005). Acquisition and maintenance of constraints in engineering design. Proc. 3rd Int. Conf. Knowledge Capture, KCAP 2005, pp. 173–174, Banff, Canada.CrossRefGoogle Scholar
Ajit, S., Sleeman, D., & Knott, D. (2007). Analysis of Design Rule Books of Part of the Rolls-Royce Domain, Technical Report. Department of Computing Science, University of Aberdeen, Aberdeen, UK.Google Scholar
AKA. (2006). American Kite Association. Accessed at http://www.aka.org.au/kites_in_the_classroom/index.htm on June 28, 2006.Google Scholar
Bahler, D., & Bowen, J. (1992). Design rationale management in concurrent engineering. Workshop on Design Rationale Capture and Use, 10th National Conf. Artificial Intelligence (AAAI-92)San Jose, CA.Google Scholar
Barker, V.E., & O'Connor, D.E. (1989). Expert systems for configuration at digital: XCON and beyond. Communications of the ACM 32 (3), 298–318.Google Scholar
Barnum, C.M. (2002). Usability Testing and Research. Upper Saddle River, NJ: Allyn & Bacon.Google Scholar
Bassiliades, N., & Gray, P. (1995). CoLan: a functional constraint language and its implementation. Data & Knowledge Engineering 14 (3), 203–249.CrossRefGoogle Scholar
Borning, A., Maher, M., Martindale, A., & Wilson, M. (1989). Constraint hierarchies and logic programming. Int. Conf. Logic Programming (ICLP), PP. 149–164, Lisbon, Portugal.Google Scholar
Bowen, J., O'Grady, P., & Smith, L. (1990). A constraint programming language for life-cycle engineering. Artificial Intelligence in Engineering 5 (4), 206–220.CrossRefGoogle Scholar
Bracewell, R.H., & Wallace, K.M. (2003). A tool for capturing design rationale. Proc. Int. Conf. Engineering Design (ICED 03)Stockholm.Google Scholar
Brown, D.C. (2006). Assumptions in design and design rationale. Design Rationale Workshop, DCC’06, Eindhoven, The Netherlands.Google Scholar
Bultman, A., Kuipers, J., & Harmelen, F.V. (2000). Maintenance of KBS's by domain experts: the Holy Grail in practice. Thirteenth Int. Conf. Industrial & Engineering Applications of Artificial Intelligence & Expert Systems IEA/AIE′00.Google Scholar
Burge, J., & Brown, D.C. (2003). Rationale support for maintenance of large scale systems. Workshop on Evolution of Large-Scale Industrial Software Applications (ELISA), ICSM ‘03, Amsterdam.Google Scholar
CEKS. (2006). Cutting Edge Kite Shop. Accessed at http://www.cuttingedgekites.com/faq.htm on June 28, 2006.Google Scholar
Coenen, F.P. (1992). A methodology for the maintenance of knowledge based systems. EXPERSYS-92 (Proc.), IITT-Int. (Niku-Lari, A., Ed.), pp. 171–176.Google Scholar
Dumas, J.S., & Redish, J.C. (1999). A Practical Guide to Usability Testing. Bristol: Intellect Books.Google Scholar
Eden, M. (1998). The Magnificient Book of Kites: Explorations in Design, Construction, Enjoyment and Flight. New York: Black Dog & Levanthal Publishers.Google Scholar
Eriksson, H., Puerta, A., Gennari, J., Rothenfluh, T., Tu, S., & Musen, M. (1995). Custom-tailored development tools for knowledge-based systems. Proc. Ninth Banff Knowledge Acquisition for Knowledge-Based Systems WorkshopBanff, Canada.Google Scholar
Felfernig, A., Friedrich, G., Jannach, D., & Stumptner, M. (2004). Consistency-based diagnosis of configuration knowledge bases. Artificial Intelligence 152, 213–234.CrossRefGoogle Scholar
Fletcher, D., & Gu, P. (2005). Adaptable design for design reuse. Second CDEN Int. Conf. Design Education, Innovation, and Practice.Google Scholar
Fowler, D.W., Sleeman, D., Wills, G., Lyon, T., & Knott, D. (2004). Designers’ Workbench. Proc. 24th SGAI Int. Conf. Innovative Techniques and Applications of Artificial Intelligence, pp. 209–221, Cambridge.Google Scholar
Frayman, F., & Mittal, S. (1987). COSSACK: a constraints-based expert system for configuration tasks. Knowledge Based Expert Systems in Engineering: Planning and Design (Sriram, D., & Adey, R.A., Eds.), pp. 143–166. Southampton: Computational Mechanics Publications.Google Scholar
Goonetillake, J.S., & Wikramanayake, G.N. (2004). Management of evolving constraints in a computerised engineering design environment. Proc. 23rd National IT Conf.Colombo, Sri Lanka.Google Scholar
Gray, P., Hui, K., & Preece, A. (2001). An expressive constraint language for semantic web applications. E-Business and the Intelligent Web: Papers From the IJCAI-01 Workshop, pp. 46–53, Seattle, WA.Google Scholar
Gray, P., & Kemp, G. (2006). Capturing quantified constraints in FOL, through interaction with a relationship graph. 15th Int. Conf. Knowledge Engineering and Knowledge Management (EKAW 2006)Podebrady, Czech Republic.Google Scholar
Gruber, T.R. (1995). Towards principles for the design of ontologies used for for knowledge sharing. International Journal of Human–Computer Studies 43 (5–6), 907–928.CrossRefGoogle Scholar
Grudin, J. (1996). Evaluating opportunities for design rationale capture. In Design Rationale: Concepts, Techniques, and Use (Carroll, J.M., Ed.). Mahwah, NJ: Erlbaum.Google Scholar
Harary, F. (1962). A graph theoretic approach to matrix inversion by partitioning. Numerische Mathematik 4, 128–135.CrossRefGoogle Scholar
Hicks, R.C. (2003). Knowledge base management systems—tools for creating verified intelligent systems. Knowledge-Based Systems 16, 165–171.CrossRefGoogle Scholar
Hooey, B.L., & Foyle, D.C. (2007). Requirements for a design rationale capture tool to support NASA's complex systems. In Int. Workshop on Managing Knowledge for Space MissionsPasadena, CA.Google Scholar
Junker, U. (2001). Quickxplain: conflict detection for arbitrary constraint propagation algorithms. IJCAI'01 Workshop on Modelling and Solving Problems with Constraints (CONS-1)Seattle, WA.Google Scholar
Junker, U., & Mailharro, D. (2003). The logic of ilog(j) configurator: combining constraint programming with a description logic. Proc. IJCAI’03 Workshop on ConfigurationAcapulco, Mexico.Google Scholar
Leigh, D. (2006). Delta kite designs. Accessed at http://www.deltas.freeserve.co.uk/home.html on June 28, 2006.Google Scholar
Lin, L., & Chen, L.C. (2002). Constraints modelling in product design. Journal of Engineering Design 13 (3), 205–214.CrossRefGoogle Scholar
Lords, D. (2006). Kite, kite buggy and land yacht page. Accessed at http://users.techline.com/lord/index.html on June 28, 2006.Google Scholar
McGuinness, D.L., & Harmelen, F.v. (2004). OWL Web Ontology Language overview, W3C recommendation February 10, 2004. Accessed at http://www.w3.org/TR/owl-features/ on August 29, 2006.Google Scholar
McKenzie, C., Gray, P., & Preece, A. (2004). Extending SWRL to express fully-quantified constraints. Workshop on Rules and Rule Markup Languages for the Semantic Web (RuleML 2004), Int. Semantic Web Conf., pp. 139–154, Hiroshima, Japan.Google Scholar
McMahon, C., Lowe, A., & Culley, S. (2004). Knowledge management in engineering design: personalization and codification. Journal of Engineering Design 15 (4), 307–325.CrossRefGoogle Scholar
Meseguer, P., & Preece, A.D. (1995). Verification and validation of knowledge-based systems with formal specifications. Knowledge Engineering Review 10, 331–343.Google Scholar
Nguyen, T.A., Perkins, W.A., Laffey, T.J., & Pecora, D. (1985). Checking an expert systems knowledge base for consistency and completeness. IJCAI ‘85, Vol. 1, pp. 375–378, Los Angeles.Google Scholar
Noy, N.F., Fergerson, R.W., & Musen, M.A. (2000). The knowledge model of Protege-2000: combining interoperability and flexibility. Int. Conf. on Knowledge Engineering and Knowledge Management (EKAW 2000)Juan-les-Pins, France.Google Scholar
Preece, A.D., Shinghal, R., & Batarekh, A. (1992). Verifying expert systems: a logical framework and a practical tool. Expert Systems with Applications 5 (3/4), 421–436.CrossRefGoogle Scholar
Qian, Y., Zheng, M., Li, X., & Lin, L. (2005). Implementation of knowledge maintenance modules in an expert system for fault diagnosis of chemical process operation. Expert Systems with Applications 28, 249–257.Google Scholar
Regli, W.C., Hu, X., Atwood, M., & Sun, W. (2000). A survey of design rationale systems: approaches, representation, capture and retrieval. Engineering with Computers: An International Journal for Simulation-Based Engineering 16, 209–235.CrossRefGoogle Scholar
Rubin, J. (1994). Handbook of Usability Testing. New York: Wiley Technical Communication Library.Google Scholar
Seaborne, A. (2004). RDQL—a query language for RDF. Accessed at http://www.w3.org/Submission/RDQL/ on August 29, 2006.Google Scholar
Selpi. (2004). An FDM prototype for pathway and protein interaction data. Master's Thesis, Chalmers University of Technology, Goteborg, Sweden.Google Scholar
Serrano, D., & Gossard, D. (1992). Tools and techniques for conceptual design. In Artificial Intelligence in Engineering Design (Tong, C. & Sriram, D., Eds.), Vol. 1, pp. 71–116. San Diego, CA: Academic.CrossRefGoogle Scholar
Soloway, E., Bachant, J., & Jensen, K. (1987). Assessing the maintainability of XCON-in-RIME: coping with problems of a very large rule-base. In Proc. AAAI-87, pp. 824–829, Seattle, WA.Google Scholar
Sriram, D., & Maher, M.L. (1986). The representation and use of constraints in structural design. In Applications of Artificial Intelligence in Engineering Problems, Vol. 1, pp. 355–368. Southampton: Computational Mechanics Publications.Google Scholar
Steward, D.V. (1962). On an approach to techniques for the analysis of the structure of large systems of equations. SIAM Review 4.CrossRefGoogle Scholar
Streeter, T. (1980). The Art of the Japanese Kite. Tokyo: Charles E. Tuttle Company.Google Scholar
Suwa, M., Scott, A.C., & Shortliffe, E.H. (1982). An approach to verifying completeness and consistency in a rule-based system. AI Magazine 3 (4), 16–21.Google Scholar
Wardley, A. (2006). Basics of stunt kite design. Accessed at http://www.kfs.org/~abw/kite/rec.kites/skdesign1.html on June 28, 2006.Google Scholar
Wielinga, B., & Schreiber, G. (1997). Configuration-design problem solving. IEEE Expert 12 (2), 49–57.CrossRefGoogle Scholar
Yolen, W. (1976). The Complete Book of Kites and Kite Flying. New York: Simon and Schuster Trade.Google Scholar
Zlatareva, N.P. (1998). A refinement framework to support validation and maintenance of knowledge-based systems. Expert Systems with Applications 15, 245–252.CrossRefGoogle Scholar