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Using a manufacturing constraint network to identify cost-critical areas of designs

Published online by Cambridge University Press:  27 February 2009

Carolyn C. Hayes
Affiliation:
Department of Computer Science, Beckman Institute, University of Illinois at Champaign-Urbana, Urbana, IL 61801, U.S.A.
Harold C. Sun
Affiliation:
Department of Computer Science, Beckman Institute, University of Illinois at Champaign-Urbana, Urbana, IL 61801, U.S.A.

Abstract

A difficult task in concurrent engineering tasks is to provide appropriate manufacturability feedback to the designer in a timely manner. Many tools provide designers with cost and manufacturability evaluations, but they do not necessarily help the designer to identify what aspects of the design to change in order to improve it from a manufacturing perspective. A computer-assisted concurrent-engineering technology is described which identifies cost-critical tolerances in the design and generates cost-reducing design suggestions. The purpose of the system is to help focus the designer’s attention on the specific aspects of the design that influence manufacturing cost. This can aid the designer in optimizing the manufacturing costs of prismatic, one-off parts created on a CNC machining center. This work uses a program called the Manufacturing Evaluation Agent to produce cost-reducing design suggestions. The Manufacturing Evaluation is made up of two portions: a manufacturing planner and a suggestion generator. The manufacturing planner, P3, takes a design and generates a manufacturing constraint net that represents all manufacturing steps and their sequencing. Each constraint in the network has a label indicating the portions of the design (or manufacturing environment) that gave rise to that constraint. The Suggestion Generator analyzes the manufacturing constraint net to find the cost-critical areas, and uses the labels to find the portions of the design responsible for these cost-critical parts of the manufacturing plan. By pinpointing the cost-critical areas of the design and suggesting alternatives, the Manufacturing Evaluation Agent can help the de-signer to more quickly develop a superior design. This results in more rapid turnaround time, lower cost designs, and fewer engineering change orders once the design is sent to the factory floor.

Type
Articles
Copyright
Copyright © Cambridge University Press 1995

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References

REFERENCES

Brooks, S.L., Hummel, K.E., & Wolf, M.L. (1987). Xcut: A rule based expert system for the automated process planning of machined parts. Technical Report BDX-613–3768. Bendix Kansas City Division, Kansas City, MO.Google Scholar
Barkocy, B.E., & Zdeblick, W.J. (1984). A knowledge-based system for machining operation planning. Autofact 6(1), 114.Google Scholar
Chang, T.C., & Anderson, D.C. (1992). Quick turnaround cell–an integration of feature based design and process planning. 1992 First Industrial Engineering Res. Conf. Proc, 8385.Google Scholar
Cutkosky, M.R., & Tenenbaum, J.M. (1987). CAD/CAM Integration Through Concurrent Process and Product Design, pp. 110. American Society of Mechanical Engineers, New York.Google Scholar
Descotte, J.L. (1981). Gari, Y.: A problem solver that plans how to machine mechanical parts. Proc. IJCAI, 766772.Google Scholar
Das, D., Gupta, S.K., & Nau, D.A. (1994). Reducing cost set-up by automated generation of redesign suggestions. ASME Comput. in Engineering Conf., 159170.Google Scholar
Finger, S., & Dixon, J.R. (1989). A review of research in mechanical engineering design. Res. Eng. Des. 1, 5167.CrossRefGoogle Scholar
Fox, M., Finger, S., Gardner, E., Navinchandra, D., Safier, S.A., & Shaw, M.A. (1992). Design fusion: An architecture for concurrent design. Knowledge-Aided Des. 157195.Google Scholar
Hayes, C. (1990). Machining planning: A model of an expert level planning process. Ph.D. Thesis. Carnegie Mellon University, Pittsburgh, PA.Google Scholar
Hayes, C.C. (1994). Quern: A method for measuring the solution quality and experience level of knowledge-based systems. IEEE Trans. Data Knowledge Eng. (Submitted).Google Scholar
Hayes, C.C, Desa, S., & Wright, P.K. (1989). Using process planning knowledge to make design suggestions concurrently. Concurrent Prod. Process Des. 8792.Google Scholar
Hetem, V. (1994). Concurrent cost estimating. Ph.D. Thesis. Department of Mechanical and Industrial Engineering, University of Illinois at Champaign-Urbana, Urbana, IL.Google Scholar
Hayes, C.C., & Sun, H. (1993). Modeling manufacturing tolerances for process planning of aerospace parts. AIAA Conf. Computing in Aerospace 9, October 1921, pp. 1224–1230.CrossRefGoogle Scholar
Hayes, C.C, & Sun, H. (1994). Reasoning about manufacturability prior to reaching the shop floor. SIGMAN Workshop on Reasoning about the Shop Floor, Seattle, WA.Google Scholar
Kramer, T.R., & Jau shi Jun. (1986). Software for an automated machining workstation. Proc. Third Biannual Int. Machine Tool Tech. Conf. Session 12, 1244.Google Scholar
Lu, S. (1992). Knowledge-based engineering systems research laboratory: Annual report. Technical Report. Department of Mechanical Identifying cost-critical tolerance specifications 87 and Industrial Engineering, University of Illinois at Champaign-Urbana, Urbana, IL.Google Scholar
Mantyla, M., Opas, J., & Puhakka, J. (1989). Generative process planning of prismatic parts feature relaxation. Advances in Design Automation, (Ravani, , Ed), pp. 4960. ASME.CrossRefGoogle Scholar
Okada, N., Matsushima, K., & Sata, T. (1982). The integration of cad and cam by application of artificial intelligence techniques. Ann. CIRP 31(1), 329332.Google Scholar
Nau, D.S. (1987). Automated process planning using hierarchical abstraction. 1987 Texas Instruments call for papers on Al for Industrial Automation.Google Scholar
Pan, J.Y.C., & Tenenbaum, J. (1991). An intelligent agent framework for enterprise integration. IEEE Trans. Syst. Man Cybernet. 21(6), 13911408.CrossRefGoogle Scholar
Sakurai, H. (1990). Automatic set-up planning and fixture design for machining. Ph.D. Thesis. Massachusetts Institute of Technology, Cambridge, MA.Google Scholar
Smith, R.G., & Davis, R. (1988). Frameworks for Cooperation in Distributed Problem Solving. Morgan Kaufman Publishers, San Mateo, CA.Google Scholar
Schmitz, J.M., & Desa, S. (1994). Development and implementation of a design for producibility method for precision planar stamped products. J. Mech. Des. 116, 349356.CrossRefGoogle Scholar
Sikora, R., & Shaw, M.J. (1994). A multi-agent framework for the cordination and integration of information systems. Technical Report UIUC-BI-AI-DSS-94–01. The Beckman Institute and the Department of Business Administration, University of Illinois at Champaign-Urbana, Urbana, IL.Google Scholar
Tsang, J.P., & Brissaud, D. (1989). A feature-based approach to process planning. Proc. ASME. Internat. Computers in Eng. Conf. and Expo., Anaheim, CA, July 30-Aug. 3, pp. 419430.Google Scholar