摘要
着重探讨了基于机器学习,特别是神经网络和遗传算法的智能控制系统在注塑模具设计与制造中的具体应用。该系统通过神经网络对注塑模具的相关数据进行深度学习,同时结合遗传算法优化神经网络的权重和偏置,旨在提高其针对注塑模具设计与制造的预测准确度和泛化能力。实验数据显示,此系统在注塑模具的设计与制造过程中响应迅速、运行稳定,且能有效降低生产成本,显著提升生产效率。总体而言,所研发的智能控制系统为注塑模具设计与制造行业的智能化升级提供了强大的技术支持,显示出明显的优势和实际应用价值,有望为注塑模具行业的创新发展注入新的动力。
This paper focuses on exploring the specific applications of intelligent control systems based on machine learning,particularly neural networks and genetic algorithms,in the design and manufacturing of injection molds.This system employs neural networks for deep learning of relevant data related to injection molds,while optimizing the weights and biases of the neural networks using genetic algorithms.The aim is to enhance the prediction accuracy and generalization ability specifically tailored to the design and manufacturing of injection molds.Experimental data demonstrates that this system responds quickly,operates stably,and can effectively reduce production costs and significantly improve production efficiency in the design and manufacturing process of injection molds.Overall,the intelligent control system developed in this paper provides strong technical support for the intelligent upgrading of the injection mold design and manufacturing industry,showing significant advantages and practical application value,and is expected to inject new momentum into the innovative development of the injection mold industry.
作者
贠娟妮
YUN Juanni(Xi'an High-Tech Vocational College,Xi'an 713700,Shaanxi,China)
出处
《模具技术》
2024年第4期54-62,共9页
Die and Mould Technology
基金
西安理工大学高科学院量子点敏化太阳能电池(编号:20JK0811)。
关键词
机器学习
模具设计与制造
神经网络
遗传算法
智能控制系统
machine learning
mold design and manufacturing
neural networks
genetic algorithms
intelligent control system
