A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters

► A clear multi-objective model was formulated for process optimization during PIM. ► Proposed a hybrid of BP/GA optimization method for process optimization during PIM. ► Energy consumption and other factors during PIM were taken into consideration. ► Efficiency and flexibility of the proposed opti...

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Bibliographic Details
Published in:Materials and Design 2011-06, Vol.32 (6), p.3457-3464
Main Authors: Yin, Fei, Mao, Huajie, Hua, Lin
Format: Article
Language:English
Subjects:
A. Polymers
Back propagation
C. Moulding
Computer simulation
F. Defects
Genetic algorithms
Mathematical analysis
Mathematical models
Neural networks
Optimization
Powder injection molding
Process parameters
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Summary:► A clear multi-objective model was formulated for process optimization during PIM. ► Proposed a hybrid of BP/GA optimization method for process optimization during PIM. ► Energy consumption and other factors during PIM were taken into consideration. ► Efficiency and flexibility of the proposed optimization method was confirmed. This paper presents a hybrid optimization method for optimizing the process parameters during plastic injection molding (PIM). This proposed method combines a back propagation (BP) neural network method with an intelligence global optimization algorithm, i.e. genetic algorithm (GA). A multi-objective optimization model is established to optimize the process parameters during PIM on the basis of the finite element simulation software Moldflow, Orthogonal experiment method, BP neural network as well as Genetic algorithm. Optimization goals and design variables (process parameters during PIM) are specified by the requirement of manufacture. A BP artificial neural network model is developed to obtain the mathematical relationship between the optimization goals and process parameters. Genetic algorithm is applied to optimize the process parameters that would result in optimal solution of the optimization goals. A case study of a plastic article is presented. Warpage as well as clamp force during PIM are investigated as the optimization objectives. Mold temperature, melt temperature, packing pressure, packing time and cooling time are considered to be the design variables. The case study demonstrates that the proposed optimization method can adjust the process parameters accurately and effectively to satisfy the demand of real manufacture.
ISSN:0261-3069
0264-1275
DOI:10.1016/j.matdes.2011.01.058