This thesis addresses the problem of predicting tool wear in machining processes, which is crucial for monitoring and improving production quality. The methodology of using Support Vector Regression (SVR) to accurately predict tool wear based on multi-sensor data is presented. The experimental part of the thesis includes conducting tests where cutting and feed forces, as well as tool wear, were measured. The results showed that the SVR method provides reliable predictions, contributing to better understanding and monitoring of tool wear during the production process.
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