Optimisation of Plastic Injection Moulding Process with Soft Computing

The paper presents a hybrid strategy in a soft computing paradigm for the optimisation of the plastic injection moulding process. Various plastic injection molding process parameters, such as mold temperature, melt temperature, injection time and injection pressure are considered. The hybrid strateg...

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Bibliographic Details
Published inInternational journal of advanced manufacturing technology Vol. 21; no. 9; pp. 656 - 661
Main Authors Shi, F., Lou, Z.L., Zhang, Y.Q., Lu, J.G.
Format Journal Article
LanguageEnglish
Published Heidelberg Springer Nature B.V 01.06.2003
Subjects
Back propagation networks
Computer simulation
Genetic algorithms
Injection molding
Mathematical models
Melt temperature
Multilayers
Neural networks
Optimization
Process parameters
Rapid prototyping
Soft computing
Software
Strategy
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Summary:The paper presents a hybrid strategy in a soft computing paradigm for the optimisation of the plastic injection moulding process. Various plastic injection molding process parameters, such as mold temperature, melt temperature, injection time and injection pressure are considered. The hybrid strategy combines numerical simulation software, a genetic algorithm and a multilayer neural network to optimise the process parameters. An approximate analysis model is developed using a Back-propagation neural network in order to avoid the expensive computation resulting from the numerical simulation software. According to the characteristic of the optimisation problem, a nonbinary genetic algorithm is applied to solve the optimisation model. The effectiveness of the improved strategy is shown by an example.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-002-1374-3