Prediction of processing effect of wire electrode discharge grinding of insulation engineering ceramic with particle swarm optimization fuzzy neural network
-
摘要: 在绝缘工程陶瓷线电极放电磨削加工过程中,加工技术指标与各工艺参数间联系密切。在实际加工中,操作者通常根据以往的加工经验设置工艺参数,并对加工结果进行一定的预判。若工艺参数设置不合理,将极大影响机床的加工效率、加工精度和加工能力。为此,以BP模糊神经网络为基础,提出了一种适用于绝缘工程陶瓷线电极放电磨削加工的技术指标效果预测模型,用粗糙集理论对训练样本集进行属性和规则约简,并用改进的粒子群算法优化模糊神经网络。根据优化前后的模型对碳化硼(B4C)陶瓷加工进行仿真实验对比,发现优化后的模型对技术指标的预测速度快、误差小、精度高。Abstract: In the process of wire electrode discharge grinding (WEDG) of insulating engineering ceramic, there is a close relationship between the technical indexes and process parameters. The operators can only set the process parameters according to their past experience during the practice, and predict the processing results to a certain extent. If the setting of process parameters is unreasonable, it will greatly affect the processing efficiency, accuracy and capacity of machine tools. Therefore, based on BP fuzzy neural network(BPFNN), a prediction model is presented for the effect of technical indicators for WEDG of insulating engineering ceramic. Rough set theory is used to reduce the attributes and rules of training samples and improved particle swarm optimization (PSO) is used to optimize the network. The models before and after optimization are used to simulate the processing of boron carbide (B4C) ceramics and the results are compared. It is found that the optimized model has the advantages of fast prediction speed, small error and high precision.
点击查看大图
计量
- 文章访问数: 86
- HTML全文浏览量: 15
- PDF下载量: 9
- 被引次数: 0