Machine Learning in Engineering Design and Manufacturing

Reich, Y. and Steven, J. Fenves (1988), Integration of Generic Learning Tasks, Technical Report EDRC 12-28-89, Engineering Design Research Center, Carnegie Mellon University, Pittsburgh, PA 15213.
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Reich, Y. (1991), Constructive Induction by Incremental Concept Formation, In Artificial Intelligence and Computer Vision, Feldman, Y. A. and Bruckstein, A. (eds.), pp. 191-204, Elsevier Science Publishers, Amsterdam.
(Postscript file, 62K)

Reich, Y., Konda, S., Levy, S. N., Monarch, I., and Subrahmanian, E. (1993), New roles for machine learning in design, Artificial Intelligence in Engineering, 8(3):165-181.
This paper is related to the topic of knowledge extraction from databases even if not a typical paper on the subject. This paper reviews the present way of using machine learning in design and criticize it as being restrictive. The underlying assumptions under present use are discussed and new ways in which machine learning can be used in design are discussed. The use of natural language processing (NLP) techniques is discussed as well as the use of multiple machine learning tools (multistrategy). The integration framework for all the techniques is, as you have already guessed ... n-dim. (Postscript file, 783K)

Reich, Y. (1994), Micro and Macro Perspectives of Multistrategy Learning, In Machine Learning: A Multistrategy Approach, Michalski, R. S. and Tecuci, G. (eds.), pp. 379-401, Morgan Kaufmann, San Fransico.
(Postscript file, 392K, one figure missing)

Reich, Y. (1994), Towards Practical Machine Learning Techniques, In Proceedings of the First Congress on Computing in Civil Engineering (Washington, DC), pp. 885-892, ASCE, New York.
(Postscript file, 127K)

Reich, Y., Fenves, S. J., and Subrahmanian, E. (1994), Flexible Extraction of Practical Knowledge from Bridge Databases, In Proceedings of the First Congress on Computing in Civil Engineering (Washington, DC), pp. 1014-1021, ASCE, New York.
(Postscript file, 185K)

Reich, Y. (1996), Modeling engineering information with machine learning, Artificial Intelligence for Engineering Design, Analysis, and Manufacturing, 10(2):171-174.
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Reich, Y., Medina, M. Jr., Shieh, T.-Y., and Jacobs, T. (1996), Modeling and Debuging Engineering Decision Procedures with Machine Learning, Journal of Computing in Civil Engineering, 10(2):157-166.
(Postscript file, 290K)

Reich, Y. and Travitzky, N. (1995), Machine learning of material behavior knowledge from empirical data, Materials & Design, 6(5):251-259.
(Postscript file, 330K)

Reich, Y. (1997), Machine Learning Techniques for Civil Engineering Applications, Microcomputers in Civil Engineering, 12(4):295-310.
Abstract: The growing volume of information databases presents opportunities for advanced data analysis techniques from machine learning (ML) research. Practical applications of ML are very different from theoretical or empirical studies, involving organizational and human aspects, and various other constraints. Despite the importance of applied ML, little has been discussed in the general ML literature on this topic. In order to remedy this situation, we studied practical applications of ML and developed a proposal for a seven-steps process that can guide practical applications of ML in engineering. The process is illustrated by relevant applications of ML in civil engineering. This illustration shows that the potential of ML has only begun to be explored, but also cautions that in order to be successful, the application process must carefully address the issues related to the seven-step process.
Postscript file, 430K)

Reich, Y. (1998), ``Learning in design: From Characterizing Dimensions to Working Systems,'' Artificial Intelligence for Engineering Design, Analysis (AI EDAM), and Manufacturing, 12(2)2:161-172.
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