Learning fuzzy logic based intelligent determination of feedrate for end milling operation

Abstract

Feasible feedrate plays an important role in improving machining efficiency in end milling operation, but it needs complicated calculation to acquire the value. The paper presents a learning fuzzy logic based approach for intelligent determination of the condition. Limitations about rule-based expert system in selection of machining parameters for milling process are summarized and discussed. Then a learning fuzzy logic based inference model is brought forth as an intelligent feedrate selection engine according to three machining conditions: tool diameter, cutting depth and material hardness. A method composed with artificial neural network and data cluster is applied to obtain the inference knowledge for the fuzzy logic model. In the end, an illustration is given to show the applicability of the proposed approach. The results show good performance in determination of the parameter. A conclusion is reached that the reasoning logic can provide a new measure in intelligent selection of feedrate for end milling process