Optimizing tool service life using tool vibration monitoring

Numerous operations in manufacturing industry include machining activities, e.g. drilling, milling, turning, honing and more. This not only removes material from the work piece, but also wear the tool over time. Service life determines when the tool is replaced or comes to the revision. The service life of the tool is difficult to calculate precisely due to the different influences that the tool is exposed during work and thus results in a certain fluctuation range. Too early or too late intervention increases the setup time or is reflected in lack of part quality in case of too late intervention and is therefore costly. One of the keys to achieving maximum productivity in the use of tools is the individual determination of the condition of the tool over its service life for each individual. This study presents a method how to use the row data from vibration sensors placed on the tool holder. Statistical correlation analysis is used to extract features which show the correlation between the increasing wear of the tool and the produced product quality. Machine learning algorithms are used for automated diagnosis of tool service life to find the optimal time for use of tools.