MERGED NEURAL DECISION SYSTEM AND ANFIS WEAR PREDICTOR FOR SUPPORTING TOOL CONDITION MONITORING

作者：Cus Franci^*; Zuperl Uros

来源：Transactions of Famena, 2011, 35(1): 13-26.

• tool condition monitoring (TCM)
• wear
• tool deflection
• ANFIS
• neural network
• end-milling

The aim of this paper is to present a tool condition monitoring (TCM) system that can detect tool breakage in real time using a combination of a neural decision system, an ANFIS tool wear estimator and a machining error compensation module. The principal presumption was that the force signals contain the most useful information for determining tool condition. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. The trained ANFIS model of tool wear is then merged with a neural network for identifying tool wear condition (fresh, worn). A neural network is used in TCM as a decision making system to discriminate different malfunction states from measured signals. The overall machining error is predicted with very high accuracy by using the deflection module and a large percentage of it is eliminated through the proposed error compensation process. The fundamental challenge to research was to develop a single-sensor monitoring system, reliable as a commercially available system, but much cheaper than the multi-sensor approach.

• 出版日期2011