应用基于学习的模糊逻辑智能选择铣削加工参数

分析传统专家系统在铣削加工参数智能选择应用中存在的问题，提出一种可实现铣削用量智能选择的模糊逻辑推理方法。构造了以刀具直径、加工深度和材料硬度为输入，铣削进给量为输出的模糊推理模型，给出基于人工神经网络与k-means聚类相结合的机器学习方法，实现了推理规则知识的自动获取。通过手册数据与模型推理结果的对比实验，表明给出的方法具有较好的铣削用量智能匹配性能，研究成果为实现铣削用量在线智能选择提供了一种新方法。

Application of Learning Based Fuzzy Logic in Intelligent Selection of Machining Parameters for Milling Process

The paper aimed to bring forth a new approach for intelligent determination of milling conditions with machine learning based fuzzy logic.Firstly,some problems about the application of traditional expert system in selection of machining parameters for milling process were analyzed and discussed.Then a fuzzy logic reasoning model was proposed to realize acquisition of feasible milling feedrate according to three machining parameters: tool diameter,machining depth and material hardness.A hybrid method composed with artificial neural network and k-means cluster was designed to acquire the rule knowledge for fuzzy logic reasoning in the model.In the end,an illustration was presented to test the applicability of the proposed approach.The result shows that the proposed approach has good performance in determination of milling conditions.As a result,the fuzzy logic can provide expert system with a new way in intelligent online selection of milling conditions.