CBR and Machine Learning for combustion system design

  • Jutta Stehr
Application Sessions
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1010)


Nowadays the automotive industry has to face two major challenges. First products must meet continually increasing government requirements on fuel economy and low exhaust emission. Second the market demands product variety and short production cycles. The automobile's combustion system determines the exhaust emission rate, combustion system engineering is one of the crucial steps in the development process. Cylinder head design is a good example of showing how enhanced AI technologies like CBR and Machine Learning support high-level engineering design tasks.

The work described was coordinated in a joint project between the Daimler-Benz research group on Thermo and Fluid Dynamics and our reasearch group on Machine Learning with the aim of improving of cylinder head engineering. This paper proposes how Machine Learning and specifically Case-based Reasoning (CBR) transform a traditional database containing both geometry and air-motion data into a so called experience memory for cylinder head design. We will present the initial steps of our database analysis in terms of different learning algorithms, then use the extracted knowledge to develop case-based design retrieval and quality prediction modules.


Case-based Reasoning Engineering Design Support Knowledge Discovery in Databases 


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  1. [ACO92]
    T.Acorn, S.Walden. SMART: Support Management cultivated reasoning technology for Compaq customer service. Proceedings of AAAI-92. MIT Press. 1992.Google Scholar
  2. [AHA91]
    D. Aha, D. Kibler, M. Albert. Instance-Based Learning Algorithms. Machine Learning 6 (1). 1991.Google Scholar
  3. [AHA94]
    D. Aha, R. Bankert. Feature Selection for Case-Based Classification of Cloud Types: An empirical comparison. Workshop Notes on Case-Based Reasoning. AAAI-94. 1994.Google Scholar
  4. [BAR89]
    R. Barletta, D. Hennessy. Case Adaptation in Autoclave Layout Design. DARPA Case-Based Reasoning Workshop. Morgan Kaufmann Publishers. 1989.Google Scholar
  5. [BOE90]
    C. D. de Boer, R. J. R. Johns, D. W. Grigg. B. M. Train. I. Denbratt. J.R. Linna. Refinement with perforamnce and economy for four-valve automotive engines. Society of Automotive Engineers. 1990.Google Scholar
  6. [BRO89]
    D. C. Brown, B. Chandrasekaran. Design Problem Solving: Knowledge Structures and Control Strategies. Pitman. 1989.Google Scholar
  7. [CAR93]
    C. Cardie. Using Decision Trees to Improve Case-Based Learning. Proc. of the 10th Int. Conference on Machine Learning. Morgan Kaufman. 1993.Google Scholar
  8. [CHE94]
    Cheshire Engineering Corporation. Neuralyst. User's Guide. 1994.Google Scholar
  9. [COS93]
    S. Cost, S. Salzberg. A weighted Nearest Neighbor Algorithm for Learning with Symbolic Features. Machine Learning 10 (1). 1993.Google Scholar
  10. [DÖR76]
    D. Dörner. Problemlösung als Informationsverarbeitung. Verlag W. Kohlhammer. 1976.Google Scholar
  11. [FAB92]
    FABEL-Consortium. Survey of FABEL, Fabel Report No. 2, GMD, 1992.Google Scholar
  12. [HAM89]
    K. Hammond. Chef: A Model of Case-Based Planning. Proceedings of the Nat. Conference on Artificial Intelligence (AAAI 86). MIT Press 1986.Google Scholar
  13. [KEL91]
    J. D. Kelly, L. Davis. A Hybrid Genetic Algorithm for Classification. Proc. of the 12zh Int. Joint Conference on Artificial Intellicence. 1991.Google Scholar
  14. [KOL93]
    J. Kolodner. Case-Based Reasoning. Morgan Kaufmann Publishers. 1993.Google Scholar
  15. [LIN77]
    P. H. Lindsay, D. A. Norman. Human Information Processing. Academic Press. 1977.Google Scholar
  16. [MIC94]
    D. Michie, D. J. Spiegelhalter, C. C. Taylor (eds.). Machine Learning, Neural and Statistical Classification. Ellis Horwood. 1994.Google Scholar
  17. [PEA92]
    M. Pearce, A. K. Goel, J. L. Kolodner, C. Zimring, L. Sentosa, R. Billington. Case-Based Design Support. IEEE Expert. October 1992.Google Scholar
  18. [PIA91]
    G. Piatetsky-Shapiro, W. J. Frawley (eds.). Knowledge Discovery in Databases. MIT Press. 1991.Google Scholar
  19. [POL49]
    G. Polya. Schule des Denkens. Francke Verlag. 1949.Google Scholar
  20. [SIM92]
    E. Simoudis. Using Case-Based Retrieval for Customer Technical Support. IEEE Expert. October 1992.Google Scholar
  21. [QUI93]
    J. R. Quinlan. C4.5 Programs For Machine Learning. Morgan Kaufmann Publishers. 1993.Google Scholar
  22. [TAK87]
    H. Takahashi, T. Ishida, K. Sato. Improvement of Diesel Emgine Performance by Variable Swirl System. Int. Off-Highway & Powerplant Congress. Society of Automotive Engineers. 1987.Google Scholar
  23. [WAL91]
    J. P. Walsh, G. R. Ungson. Organizational Memory. Academy of Management Review. 16 (1). 1991.Google Scholar
  24. [WES91]
    S. Weß. PATDEX/2 — ein System zum adaptiven, fallfokussierenden Lernen in technischen Diagnosesituasitionen. SEKI Working Paper SWP-91-01. Universität Kaiserslautern. 1991.Google Scholar
  25. [WIR95]
    T. Reinartz, R. Wirth. The need for a Task Model for KDD. Accepted by the MLnet Familiarisation Workshop on Machine Learning, Statistics and Knowledge Discovery in Databases. 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Jutta Stehr
    • 1
  1. 1.Dept. F3S/EDaimler Benz AGUlmGermany

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