摘要
在以人工智能技术为基础的注塑工艺参数优化系统的开发方面进行了研究.构建了以混合神经网络与遗传算法方法为基础,并结合CAE技术的参数优化系统,编制了应用程序.通过工程实例,将参数优化系统的预测结果与CAE模拟结果进行比较和误差分析,显示出优化系统的稳定性和可靠性;优化结果与CAE模拟结果及实验验证的结果具有一致性.证明优化结果是正确的,表明基于混合神经网络与遗传算法方法的注塑工艺参数优化系统具有工程应用价值.
In this paper, research has been done on development of optimization system for injection molding parameters on the basis of artificial intelligence. Based on hybrid neural network and genetic algorithm approach, a parameter optimization system is established and the application program is also developed. The comparison and error analysis are made between the optimization system predicted result and CAE simulated result through engineering examples, that shows the optimization system is stable and reliable. The optimized outcome, being consistent with CAE simulated one and experiment tested one, is proved to be correct, which indicates that the optimization system is valuable in engineering application.
出处
《南昌工程学院学报》
CAS
2005年第1期39-46,共8页
Journal of Nanchang Institute of Technology
基金
江西省科技厅科技基金资助项目(Z1891).
