Dissertation
Dissertation > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Machine-assisted technology > Machine -aided design (CAD), aided drawing

Research on Key Techniques of Computer Aided Process Planning for Resin Composite Material

Author WangGongDong
Tutor LiuWenJian
School Harbin Institute of Technology
Course Aviation Aerospace Manufacturing Engineering
Keywords resin composite CAPP product clustering decision of process method stacking sequence parameter optimization knowledge mining
CLC TP391.72
Type PhD thesis
Year 2007
Downloads 366
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Computer aided process planning (CAPP) is the key techniques of CAD/CAM, which may shorten the period between product design and product manufacture effectively, and is import to rapid product design, but CAPP system has not applied to the advance resin composite material. According to the features of six methods of molding process, such as RTM (Resin Transfer Moulding) process, hot compressing molding process and so on, the automatic process and intelligent design of composite materials were studied thoroughly in this paper, based on the home and aboard research work of the computer aided composite material process planning techniques.The automatic level of manual-operated composite molding technology was completely improved by adopting the latest achievements of computer information processing technique and hence batch productivity is increased and batch cost is reduced. The following research works were included:The overall plan of CM-CAPPS (Computer Aided Composite Material Process Planning System) is proposed by analyzing of resin composite process. The information models of CM-CAPPS are established and the custom-built technique is studied.The resin composite material part clustering method based on the part code system is presented. According to the analysis of resin composite part features,a classification and coding system, which has eighteen codes, is set up. Firstly, reduction theory of rough set is utilized to reduce the features of composite part. Secondly, the nearest neighborhood clustering model of composite part is proposed. Finally, the AIC criterion is used to evaluate the clustering results. The method improves the clustering speed and solves the problem that the sort number is difficult to make certain when build the class center of clustering model.The decision method of resin composite process is studied. The comprehensive evaluation model is established through analyzing the factors of resin composite process. Reduction theory of rough set is utilized to reduce the evaluate features and the weight is determined by using equivalence closure and entropy. Finally, the case study shows that the decision method is feasible and efficient.The composite laminate stacking sequence parameter optimization based on design heuristics is studied. Through analyzing of composite laminate strength, a fitness function of adaptive genetic algorithm based design heuristics is established by using Tsai-Hill rule. Design heuristics are expressed by fuzzy IF-THEN with believe ratio, design heuristics arithmetic operators are defined in adaptive genetic algorithm and the arithmetic operators is performed in each iteration. An example is given to verify that the convergence speed is faster and the optimization result is valid.The resin composite process knowledge database is established. The structure and expression of decision and case knowledge are discussed. Based on the rough set and decision tree algorithm, the composite decision mining algorithm is presented by using knowledge dependence.The case reasoning mechanism is studied and the case retrieving algorithm is improved by using feature importance of rough set to reduce the case index features and determine the weights of case index, thus the case retrieving speed and preciseness is improved.Base on the above-mentioned key technologies; prototype system was developed via Borland Delphi, VC and Microsoft SQL server, which validated the feasibility of the technologies.

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