摘要
分析以往建立表面粗糙度预测模型方法的不足,采用自适应神经模糊推理系统(ANFIS)建立了铝合金铣削加工表面粗糙度预测模型。经检验,该模型预测精度高,泛化能力强,且可简便预测铣削参数对已加工表面的表面粗糙度的影响,有助于准确认识已加工表面质量随铣削参数的变化规律,为切削参数的优选和表面质量的控制提供了依据。
Based on analyzing the shortcomings of the past methods of building a surface roughness prediction model, ANFIS (adaptive-network-based fuzzy inference system) was adopted to build a prediction model of surface roughness for milling aluminium alloy. Through the verification of the built prediction model of surface roughness, it has been found that the prediction accuracy and generalization of the model are very high and the model conveniently predicts the effects of milling parameters on surface roughness of machined surface, which contributes to accurately understand the variation law of quality of machined surface following milling parameters and provides the foundation for properly selecting cutting parameters and controlling surface quality.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2005年第6期475-479,共5页
China Mechanical Engineering
基金
国家自然科学基金资助项目(50175051)
