Cost estimation of plastic injection molding parts through integration of PSO and BP neural network
Cost estimation of plastic injection molding parts through integration of PSO and BP neural network
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DOI：
10.1016/j.eswa.2012.01.166
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发表时间：
2013-02
期刊：
影响因子：
--
通讯作者：
H. S. Wang;Y. N. Wang;Y. C. Wang
中科院分区：
文献类型：
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作者：
H. S. Wang;Y. N. Wang;Y. C. Wang
塑料注射成型技术已广泛应用于各种高科技产品、汽车零部件和通用家居产品。面对全球化的浪潮，注塑企业必须缩短产品上市时间以提高竞争力，并在所有竞争对手之前推出产品，这样才能快速占领一个大的目标市场并在价格上领先。为了降低传统注塑件成本估算过程的复杂性，采用BP神经网络建立注塑件成本估算模型。由于BP神经网络的参数对结果有很大的影响，而粒子群优化算法（PSO）能够快速找到最优解。将粒子群算法与BP神经网络相结合，通过BP神经网络的最优参数组合进行粒子进化，提高了粒子群算法的收敛速度和精度。
Plastic injection molding technology has been widely used in a variety of high-tech products, auto parts and generic household products. Against the waves of globalization, the plastic injection enterprises must shorten time-to-market to enhancement of competence, and launch products ahead of all other competitors, and thus they can quickly seize a big target market and lead the price. The backpropagation (BP) neural network was used in this study to construct an estimating model for the cost of plastic injection molding parts so as to reduce the complexity in the traditional cost estimating procedures. Because the parameters of BP neural network have a significant influence on results, and particle swarm optimization (PSO) is capable of quickly finding optimal solutions. We integrated PSO and BP neural network so that the convergence rate was improved and precision was relatively enhanced through particle evolutions based on the optimum parameter combination from BP neural network.