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Optimisation of Plastic Injection Moulding Process with Soft Computing

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Abstract

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.

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Correspondence to F. Shi or Z.L. Lou or Y.Q. Zhang or J.G. Lu.

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Shi, F., Lou, Z., Zhang, Y. et al. Optimisation of Plastic Injection Moulding Process with Soft Computing . Int. Journ. Adv. Manufac. Tech. 21, 656–661 (2003). https://doi.org/10.1007/s00170-002-1374-3

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  • Mutilayer neural network
  • Non-binary genetic algorithm
  • Numerical simulation
  • Plastic injection molding
  • Soft computing