20.500.12556/DKUM-26948 Intelligent cutting tool condition monitoring in milling Purpose: of this paper is to present a tool condition monitoring (TCM) system that can detect tool breakage in real time by using a combination of neural decision system, ANFIS tool wear estimator and machining error compensation module. Design/methodology/approach: The principal presumption was that the force signals contain the most useful information for determining the tool condition. Therefore, 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). Findings: 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. Research limitations/implications: This study also briefly presents a compensation method in milling in order to take into account tool deflection during cutting condition optimization or tool-path generation. The results indicate that surface errors due to tool deflections can be reduced by 65-78%. Practical implications: The fundamental limitation of research was to develop a single-sensor monitoring system, reliable as commercially available system, but much cheaper than multi-sensor approach. Originality/value: A neural network is used in TCM as a decision making system to discriminate different malfunction states from measured signals. tool condition monitoring TCM wear tool deflection ANFIS neural network end-milling true false false Angleški jezik Angleški jezik Neznano 2012-06-01 08:51:16 2012-06-01 08:51:16 2022-04-15 03:43:00 2021-04-11 03:22:15 2011 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 621.9:004.89 15846422 1734-8412 URN:SI:UM:DK:N8RV1GOJ http://www.journalamme.org/papers_vol49_2/49239.pdf 1 https://dk.um.si/Dokument.php?lang=slv&id=36159 Fakulteta za strojništvo 0 0 0