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Derivation of optimal processing parameters of polypropylene foam thermoforming by an artificial neural network
Author(s) -
Chang YauZen,
Wen TingTing,
Liu ShihJung
Publication year - 2005
Publication title -
polymer engineering and science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.503
H-Index - 111
eISSN - 1548-2634
pISSN - 0032-3888
DOI - 10.1002/pen.20287
Subject(s) - thermoforming , artificial neural network , polypropylene , materials science , inverse , nonlinear system , process (computing) , finite element method , mechanical engineering , computer science , composite material , artificial intelligence , engineering , structural engineering , mathematics , geometry , physics , quantum mechanics , operating system
The effects of processing parameters on the thermoforming of polymeric foam sheets are highly nonlinear and fully coupled. The complex interconnection of these dominant processing parameters makes the process design a difficult task. In this study, the optimal processing parameters of polypropylene foam thermoforming are obtained by the use of an artificial neural network. Data from tests carried out on a lab‐scale thermoforming machine were used to train an artificial neural network, which serves as an inverse model of the process. The inverse model has the desired product dimensions as inputs and the corresponding processing parameters as outputs. The structure, together with the training methods, of the artificial neural network is also investigated. The feasibility of the proposed method is demonstrated by experimental manufacturing of cups with optimal geometry derived from the finite element method. Except the dimension deviation at one location, which amounts to 17.14%, deviations of the other locations are all below 3.5%. POLYM. ENG. SCI., 45:375–384, 2005. © 2005 Society of Plastics Engineers