Skip to main content
Springer Nature Link
Log in

Neural networks and their applications in component design data retrieval

  • Papers
  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Neural networks have gained increased importance in the past few years. One of the basic characteristics of neural networks is the property of associative memory. In this paper we study the possibility of using the ideas of neural networks and associative memory in the manufacturing domain, with specific reference to design data retrieval in group technology. A two-layer feed-forward perceptron with backpropagation is simulated on a Vax-8550 to train example parts. The complete scheme along with the simulation results are explained and future directions indicated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abu-Mostafa, Y. and Jacques, J. St (1985) ‘Information Capacity of the Hopfield Model’,IEEE Transactions on Information Theory,7, pp. 1–11.

    Google Scholar 

  • Amit, D., Gutfreund, H. and Sompolinsky, H. (1985) ‘Storing Infinite Number of Patterns in a Spin-glass Model of Neural Networks’,Physics Review Letters 55, No. 14, pp. 1530–1533.

    Google Scholar 

  • Batra, J. L, and Rajagoplan, R. (1975) ‘Composite Component through Graphs and Fuzzy Clusters’,Proceedings of the Eighteenth International Machine Tool Design and Research Conference UK, pp. 801–807.

  • Baum, E., Moody, J. and Wilczek, F. (1988) ‘Internal Representations for Associative Memory’,Biological Cybernatics 59, No. 4, 5, pp. 217–228.

    Google Scholar 

  • Bow, Sing-Tze (1984)Pattern Recognition: Application to Large Data-Set Problems, Marcel Dekker Inc., New York.

    Google Scholar 

  • Brankamp, K. (1970) ‘Objectives, Layout and Possibilities of The Optiz Workpiece Classification System’,Proceedings of International Seminar on Group Technology, International Centre for Advanced Technical and Vocational Training, Turin, p. 127.

  • Carpenter, G. A. and Grossberg, S. (1986), ‘Neural Dynamics of Category Learning and Recognition: Attention, Memory Consolidation, and Amnesia’, in Davis, J., Newburgh, R. and Wegman, E. (eds),Brain Structure, Learning, and Memory, AAAS Symposium Series.

  • Carpenter, G. A. and Grossberg, S. (1987a) ‘A Massively Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine’,Computer Vision, Graphics, and Image Processing 37, pp. 54–115.

    Google Scholar 

  • Carpenter, G. A. and Grossberg, S. (1987b) ‘ART-2: Self-Organization of Stable Category Recognition Codes for Analog Input Patterns’,Applied Optics,26, No. 23, pp. 4919–4930.

    Google Scholar 

  • Caudill, M. (1987), ‘Neural Network Primer, Part-I’,Al Expert 2, No. 12, pp. 46–52.

    Google Scholar 

  • Chen, H. Chew, H. H., Lee, Y. C., Sun, G. Z. and Lee, H. Y. (1986), ‘Higher Order Correlation Model for Associative Memory’, in Denker, J. S. (ed.),Neural Networks for Computing, AIP Conference Proceedings 151, pp. 87–92.

  • DARPA (Defense Advanced Research Project Agency) (1988)DARPA Neural Network Study, AFCEA International Press.

  • Denkar, J. S. (1986) ‘Neural Networks Models of Learning and Adaption’,Physica 22D, pp. 216–222.

    Google Scholar 

  • Devries, M. F., Harvey, S. M., Tipnis, V. A. and Vijay, A. (1976) ‘Group Technology— An Overview and Bibliograph’,MDC Manufacturing Systems Report, Publication No. MDC 76-101, Metcut Research Associations Inc.

  • Domany, E. and Orland, H. (1987) ‘A Maximum Overlap Neural Network for Pattern Recognition’,Physics Letters A 125, pp. 32–34.

    Google Scholar 

  • Filho, E. V. G. (1988)Computer-Aided Group Technology Part Family Formation Based on Pattern Recognition Techniques, Ph.D. Thesis, Department of Industrial and Management Systems Engineering. The Pennsylvania State University.

  • Fisher, A. D., Fukuda, R. C. and Lee, J. N. (1986) ‘Implementation of Adaptive Associative Optical Computing Elements’,Proceedings of SPIE 625, pp. 196–204.

    Google Scholar 

  • Fukushima, K. (1987) ‘Neural Network Model for Selective Attention in Visual Pattern Recognition and Associative Recall’,Applied Optics 26, No. 23, pp. 4985–4992.

    Google Scholar 

  • Fukushima, K., Miyake, S. and Ito, T. (1983), ‘Neocognitron: A Neural Network Model for a Mechanism of Visual Pattern Recognition’,IEEE Transactions on System, Man and Cybernetics, SMC-13, pp. 826–834.

  • Gallagher, C. C. and Knight, W. A. (1973)Group Technology, Butterworths, London, 1973.

    Google Scholar 

  • Gindi, G., Gmitro, A. and Parthasarathy, K. (1988) ‘Hopfield Model Associative Memory with Nonzero-Diagonal Terms in Memory Matrix’,Applied Optics 27, pp. 129–134.

    Google Scholar 

  • Ham Inyong (1990) ‘Group Technology’,Handbook of Production Engineering to be published by John Wiley and Sons.

  • Ham Inyon, Goncalves, E. V. and Han, C. P. (1988) ‘An Integrated Approach to Group Technology Part Family Data Base Design Based on Artificial Intelligence Technology’,CIRP Ann. Vol. 37, No. 1.

  • Ham Inyong, Hitomi, K. and Yoshida, T. (1985)Group Technology, Application to Production Management, Kluwer Nijhoff Publishing, Hingham, MA.

    Google Scholar 

  • Hecht-Nielsen, R. (1986) ‘Performance Limits of Optical, Electro-Optical, and Electronic Neurocomputers’,Optical and Hybrid Computing, SPIE Vol. 634, pp. 277–306.

    Google Scholar 

  • Hecht-Nielsen, R. (1987)Tutorial material published by Hecht-Nielsen Neurocomputer Corporation, San Diego.

    Google Scholar 

  • Hinton, G. E. and Anderson, J. A. (1981)Parallel Models of Associative Memory, Lawrence Erlbaum Associates Publishers, Hilsdale, New Jersey.

    Google Scholar 

  • Hopfield, J. J. (1982) ‘Neural Networks and Physical Systems with Emergent Collective Computational Abilities’,Proceedings of National Academy of Science, Vol. 79, pp. 2554–2558, April.

    Google Scholar 

  • Hopfield, J. J. (1984), ‘Neutrons with Graded Response have Collective Computational Properties Like Those of Two-State Neurons’,Proceedings of National Academy of Science 81, pp. 3088–3092, May.

    Google Scholar 

  • Hopfield, J. J. and Tank, D. W. (1986) ‘Computing with Neural Circuits: A Model’,Science 233, pp. 625–633.

    PubMed  Google Scholar 

  • Howarth, E. A. (1968) ‘Group Technology Using the Opitz System’,Production Engineer 25, p. 25.

    Google Scholar 

  • Kanerva, P. (1988)Self-Propagating Search: A Unified Theory of Memory, Bradford Books, MIT Press, Cambridge, Mass.

    Google Scholar 

  • Keeler, J. D. (1986) ‘Basis of Attraction of Neural Network Models’, in Denkar, J. S. (ed.), Neural Networks for Computing: AIP Conference 151.

  • Khotanzad, A. and Lu, J. H. (1989) ‘Invariant Shape Recognition Using a Neural Network’,IEEE Transactions on Acoustics, Speech, and Signal Processing 37, No. 5, p. 783.

    Google Scholar 

  • Kohonen, T. (1984)Self-Organization and Associative Memory, Springer Verlag, New York.

    Google Scholar 

  • Kosko, B. (1987) ‘Adaptive Bidirectional Associative Memories’,Applied optics,26, No. 23, pp. 4947–4959.

    Google Scholar 

  • Lippmann, R. P. (1987) ‘An Introduction to Computing With Neural Nets’,IEEE ASSP Magazine 4, No. 2, pp. 4–22.

    Google Scholar 

  • McClelland, J. L., Rumelhart, D. E. and Hinton, G. E. (1986) ‘The Appeal of Parallel Distributed Processing’, in Rumelhart, D. E. and McClelland, J. L. (eds),Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, MIT Press, Cambridge, Mass.

    Google Scholar 

  • McEliece, R. and et al., ‘The capacity of the Hopfield Associative Memory’,Transactions on Information Theory 1, pp. 34–45.

  • Merchant, M. E. (1982)Modern Material Handling,37, No. 1, 6W.

    Google Scholar 

  • Mitrofanov, S. P. (1966)The Scientific Principles of Group Technology, (English translation), in Grayson, J. (ed.),National Lending Library for Science and Technology, United Kingdom.

  • Peklenik, J. and Grum J. (1980) ‘Investigation of the computer aided classification of Parts’,Annals of the CIRP,29, pp. 319–323.

    Google Scholar 

  • Rosenblatt, F. (1962)Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Spartan Books.

  • Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986) ‘Learning Internal Representations by Error Propagation’, in Rumelhart, D. E. and McClelland, J. L. (eds),Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, MIT Press, Cambridge, Mass.

    Google Scholar 

  • Rumelhart, D. E. and Zipser, D. (1986) ‘Feature Discovery by Competitive Learning’, in Rumelhart, D. E. and McClelland, J. L. (eds),Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, MIT Press, Cambridge, Mass.

    Google Scholar 

  • Sejnowski, T. J. and Rosenberg, C. R. (1987) ‘Parallel Networks that Learn to Pronounce English Text’,Complex Systems 1, No. 1, pp. 145–168.

    Google Scholar 

  • Steinbuch, K. and Piske, U. (1963) ‘Learning Matrices and Their Applications’,IEEE Transactions on Electronic Computers, pp. 846–862.

  • Tangerman, E. J. (1966) ‘It only takes a minute to retrieve a design’,Production Engineering 37, No. 4, pp. 83–86.

    Google Scholar 

  • Wassermann, P. D. (1989)Neural Computing: Theory and Practice, Van Nostrand Reinhold, New York.

    Google Scholar 

  • Taylor, W. (1964) ‘Cortico-thalmic Organization and Memory’,Proc. of Royal Society of London B, Vol. 159, pp. 466–478.

    Google Scholar 

  • Wisnosky, D. and Harris, W. A. (1977), ‘An Overview of the Air Force Program for integrated Computer Aided Manufacturing’,SME Engineering Technical Paper, MF No. MS77-254.

Download references

Author information

Authors and Affiliations

  1. Industrial and Management Systems Engineering Department, The Pennsylvania State University, 16802, University Park, PA

    Sagar V. Kamarthi & Soundarr T. Kumara

  2. Electrical Engineering Department, The Pennsylvania State University, 16802, University Park, PA

    Francis T. S. Yu (Professor)

  3. Industrial and Management Systems Engineering Department, The Pennsylvania State University, 16802, University Park, PA

    Inyong Ham (Professor)

Authors
  1. Sagar V. Kamarthi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Soundarr T. Kumara
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Francis T. S. Yu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Inyong Ham
    View author publications

    You can also search for this author inPubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kamarthi, S.V., Kumara, S.T., Yu, F.T.S. et al. Neural networks and their applications in component design data retrieval. J Intell Manuf 1, 125–140 (1990). https://doi.org/10.1007/BF01472509

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01472509

Keywords