Abstract

In this study, a part of a complete Expert System (ES) for powder technology is presented. It deals with the powder selection according to predetermined recommended or required material properties and specific powder characteristics. This leads to the determination of the most common powder production (i.e., processing) method, which satisfies the specified powder requirements for a specific application. For this purpose a detailed functional prototype expert system is developed using a rule-based knowledge representation model. The expert system is designed to acquire knowledge from the user then give recommendations. The inference engine, EXSYS-CORVID, is used as the development tool. The forward and backward chain technique is utilized, thus the reverse process is also be possible. Therefore, the system will determine or assign some primary powder characteristics and production method to satisfy or achieve the required product and vice versa. The developed expert system is flexible, easy to be implemented, modified and extended. The system displayed excellent performance and typical illustrative examples are shown. The proposed system is believed to assess in increasing productivity in the field of powder technology and increases the profitability and competitiveness of the process in a modern ever increasing industries.

Keywords:

Expert system, EXSYS-CORVID, powder selection, powder technology,

References

1. Aguilar-Díaz, J.E., E. García-Montoy, P. Pérez-Lozano, J. M. Suñe-Negre, M. Miñarro and J.R. Ticó, 2009. The use of the SeDeM diagram expert system to determine the suitability of diluents-disintegrants for direct compression and their use in formulation of ODT. Eur. J. Pharm. Biopharm., 73: 414-423.
   CrossRef    PMid:19602435    
2. Ahmadi, F.F. and H. Ebadi, 2010. Design and implementation of an expert interface system for integration of photogrammetric and geographic information systems for intelligent preparation and structuring of spatial data. Exp. Syst. Appl., 37: 8006-8013.
   CrossRef    
3. Chu, H.C. and G.J. Hwang, 2008. A Delphi-based approach to developing expert systems with the cooperation of multiple experts. Exp. Syst. Appl., 34: 2826-2840.
   CrossRef    
4. Codd, E.F., 1990. The Relational Model for Database Management Version 2. Addison-Wesley, Reading, Mass.
   PMCid:PMC1663139    
5. Date, C.J., 1995. An Introduction to Database Systems. Addison Wesley, Boston, Mass.
   
6. Date, C.J. and H. Darwen, 1993. A Guide to the SQL Standard. 3rd Edn., Addison-Wesley, Reading. Mass.
   
7. Dowson, G., 1990. Powder Metallurgy the Process and its Products. Adam Hilger, Bristol, New York.
   PMCid:PMC54428    
8. Dymova, L., P. Sevastianov and P. Bartosiewicz, 2010. A new approach to the rule-base evidential reasoning: Stock trading expert system application. Exp. Syst. Appl., 37: 5564-5576.
   CrossRef    
9. Es-Saheb, M.H., 1992. Uniaxial strain rate in pharmaceutical powders during cold compaction. J. Mat. Sci., 27: 4151-4159.
   CrossRef    
10. Es-Saheb, M.H. and A. Radwan, 2003. Towards an expert system for powder production processes. JKAU Eng. Sci., 14: 115-123.
    
11. Exsys CORVID, 1989. Rule-Based Expert System Development Tool. Paperback Software International.
    
12. Fan, K.C., S.H. Chen, J.Y. Chen and W.B. Liao, 2010. Development of auto defect classification system on porosity powder metallurgy products. NDT&E Int., 43: 451-460.
    CrossRef    
13. Farag, M.M., 1989. Selection of Materials and Manufacturing Processes for Engineering Design. Prentice Hall, New York, pp: 533.
    
14. Fayed, M.A.E. and I. Otten, 1984. Handbook of Powders Science and Technology. Von Nostrand Reinhold Co., New York.
    
15. Goetzel, C.G., 1949. Treatise on Powder Metallurgy. Interscience Pubs., INC., New York.
    
16. Guessasma, S., G. Montavon, P. Gougeon and C. Coddet, 2003. Designing expert system using neural computation in view of the control of plasma spray processes. Mater. Design, 24: 497-502.
    CrossRef    
17. Hausner, H.H. and M.M. Kumar, 1982. Handbook of Powder Metallurgy. 2nd Edn., Chemical Publishing Co., Inc., Neq York.
    
18. Jackson, P., 1999. Introduction to Expert Systems. 3rd Edn., Addison Wesley Longman Ltd., Harlow, England.
    
19. Liao, S.H., 2005. Expert system methodologies and applications-a decade review from 1995 to 2004. Exp. Syst. Appl., 28: 93-103.
    CrossRef    
20. Lyu Jr, J. and M.N. Chen, 2009. Automated visual inspection expert system for multivariate statistical process control chart. Exp. Syst. Appl., 36: 5113-5118.
    CrossRef    
21. Papic, V., N. Rogulj and V. Pleština, 2009. Identification of sport talents using a web-oriented expert system with a fuzzy module. Exp. Syst. Appl., 36: 8830-8838.
    CrossRef    
22. Park, Y.C., J.K. Lee and G.C. Lee, 2007. Development of an expert system for evaluation of the strength of matrix shape memory composites. Compos. Struct., 77: 241-248.
    CrossRef    
23. Pereza, P., J.M. Negrea, S. Minarro, M. Roiga, M. Garcia-Montoya, R.F.E. Hernandez, C. Ruh and R.J.R. Tico, 2006. A new expert systems (SeDeM Diagram) for control batch powder formulation and preformulation drug products. Eur. J. Pharma. Biopharm., 64: 351-359.
    CrossRef    PMid:16949262    
24. Qian, Y., L. Xua, X. Li, L. Lin and A. Kraslawski, 2008. LUBRES: An expert system development and implementation for real-time fault diagnosis of a lubricating oil refining process. Exp. Syst. Appl., 35: 1252-1266.
    CrossRef    
25. Radwan, A. and M. Es-Saheb, 1995. Knowledge based powder selection system. Proceeding of International Conference Advances in Materials and Processing Technologies (AMPt'95). Dublin, Ireland, August 8-12, pp: 839-847.
    
26. Radwan, A. and M.H. Es-Saheb, 1998. Computerized relational models for powder technology: Powder and process selection. J. Int. Manuf., 9: 467-473.
    CrossRef    
27. Rao, M.P., D.M. Miller and B. Lin, 2005. PET: An expert system for productivity analysis. Exp. Syst. Appl., 29: 300-309.
    CrossRef    
28. Ruiz-Mezcua, B., A. Garcia-Crespo, J.L. Lopez-Cuadrado and I. Gonzalez-Carrasco, 2011. An expert system development tool for non AI experts. Exp. Syst. Appl., 38: 597-609.
    CrossRef    
29. Singha, R. and G.S. Sekhon, 1999. An expert system for optimal selection of a press for a sheet metal operation. J. Mater. Process. Technol., 86: 131-138.
    CrossRef    
30. Sirilertworakul, N., P.D. Webster and T.A. Dean, 1993. A knowledge base for alloy and process selection for casting. Int. J. Mach. Tools Manufact., 33(3): 401-416.
    CrossRef    
31. Smith, L.N., R.M. German and M.L. Smith, 2002. A neural network approach for solution of the inverse problem for selection of powder metallurgy materials. J. Mater. Process. Technol., 120: 419-425.
    CrossRef    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.