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The dynisco injection molders handbook
The dynisco injection molders handbook




the dynisco injection molders handbook

Zhang, J., (1992) Selecting typical instances in instance-based learning. (1995) Multiple Attribute Decision Making: An Introduction, Sage Publications, CA. (1991) The Dynisco Injection Molders Handbook. (1990) Injection Molding of Thermoplastics Material. Rubber and Composites Processing and Application, 21, 211-217. (1994) The effect of processing parameters on the quality of injection molded parts by using the Taguchi parameter design method. Ph.D Thesis, The Hong Kong University of Science and Technology. (1997) Determination of Injection Moulding Processing Parameters for Defect-Free Parts. (1994) Advanced methods for monitoring injection moulding processes. Journal of Material Processing Technology, 63, 458-462. (1997) An intelligent system for plastic molding process design. Advanced in Polymer Technology, 12(4), 403-418. (1993) Development of an expert system for injection molding operations. (1980) The Analytical Hierarchical Process, Wiley, New York. (1990) Plastics Processing Data Handbook, van Nostrand Reinhold. (1991) Artificial Intelligence in Chemical Engineering, Academic Press, Inc., United Kingdom. Journal of Engineering Design, 3(4), 307-324. (1992) Design for injection molding: a group-technology based approach. Part II: Molding conditions optimisation, Polymer Engineering and Science, 30(15), 883-892. (1990) Optimisation of injection molding design. NeuroShell2 (1997) Ward Systems Group, Inc. Proceeding of the 12th triennial would congress of the international federation of automatic control, 1993, vol. (1993) An operation assisted system for injection molding machines based on constraints processing. (1996) Basic Injection Molding Trouble Shooting Guides, Hong Kong Plastics Technology Centre, Hong Kong. (1988) How Neural Nets Work, Neural Information Processing Systems, American Institute of Physics, New York, pp. (1997) Application of case based reasoning in injection molding, Journal of Material Processing Technology, 63, 463-467. International Journal of Advanced Manufacturing Technology, 14, 239-246. (1998) A computational system for process design of injection molding: combining blackboard-based expert system and case-based reasoning approach. (1996) Optimisation of injection molding conditions using genetic algorithm. International Journal of Production Research, 34(2), 299-311. (1996) Framework of a fuzzy quality function deployment system. (1997) An integrated expert system for injection-molding process. Journal of Intelligent Manufacturing, 9, 17-27. (1998) Automated process parameter resetting for injection molding. (1996) Optimisation of the injection molding process.

#THE DYNISCO INJECTION MOLDERS HANDBOOK MANUALS#

F., (1990) Market-driven customer manuals using QFD, Proceedings of the AUTOFACT'90 Conference, Michigan, USA, pp. (1994) Optimisation of process parameters of injection molding with neural network application in a process simulation environment, Annals of the CIRP, 43, 449-452.Ĭole, G. (1996) Expert system aided troubleshooting in polymer engineering. (1989) The selection and setting of injection molding machines by means of process simulation.

the dynisco injection molders handbook

(1994) Applying design of experiment analysis techniques to the injection molding process. The preliminary validation tests of the system have indicated that the system can determine a set of initial process ’meters for injection molding quickly without relying on experienced molding personnel, from which good quality molded parts can be produced.īlyskal, P. A computer-aided system for the determination of initial process ’meter setting for injection molding based on the proposed techniques was developed and validated in a simulation environment. Artificial neural network was introduced in the case adaptation while fuzzy logic was employed in the case indexing and similarity analysis. In this paper, application of artificial neural network and fuzzy logic in a case-based system for initial process ’meter setting of injection molding is described. Facing with the global competition, the current trial-and-error practice becomes inadequate. Determination of initial process ’meters for injection molding is a highly skilled job and based on skilled operator’s “know-how” and intuitive sense acquired through long-term experience rather than a theoretical and analytical approach.






The dynisco injection molders handbook