Optimization of Extrusion Blow Molding Processes using Soft Computing and Taguchi's Method

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DOIResolve DOI: http://doi.org/10.1023/B:JIMS.0000037712.33636.41
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Journal titleJournal of Intelligence Manufacturing
Pages625634; # of pages: 10
SubjectBlow molding; multidisciplinary design optimization; neural network; genetic algorithm; fuzzy; soft computing
AbstractThe objective of this study is to present a new numerical strategy using soft-computing techniques to determine the optimal die gap programming of extrusion blow molding processes. In this study, the design objective is to target a uniform part thickness after parison inflation by manipulating the parison die gap openings over time. To model the whole process, that is, the parison extrusion, the mould clamping and the parison inflation, commercial finite element software (BlowSim) from the National Research Council (NRC) of Canada is used. However, the use of such software is time-consuming and one important issue in a design environment is to minimize the number of simulations to get the optimal operating conditions. To do so, we proposed a new strategy called fuzzy neural–Taguchi network with genetic algorithm (FUNTGA) that establishes a back propagation network using a Taguchis experimental array to predict the relationship between design variables and responses. Genetic algorithm (GA) is then applied to search for the optimum design of die gap parison programming. As the number of training samples is greatly reduced due to the use of orthogonal arrays, the prediction accuracy of the neural network model is closely related to the distance between sampling points and the evolved designs. The extrapolation distance concept is proposed and introduced to GA using fuzzy rules to modify the fitness function and thus improving search efficiency. The comparison of the results with commercial optimization software from NRC demonstrates the effectiveness of the proposed approach.
Publication date
PublisherKluwer Academic Publishers
AffiliationNRC Industrial Materials Institute; National Research Council Canada
Peer reviewedNo
NRC number45309
NPARC number18476843
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Record identifier132e9440-610b-4300-bb84-982dc3035223
Record created2011-10-11
Record modified2016-05-09
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