Cutter wear has a great impact on machining quality, which is particularly true when demand for machining accuracy is high. Therefore, cutter wear analysis is critical in assuring high machining quality and long tool life. However, it is highly dangerous and difficult to monitor and determine tool wear conditions during machining. This paper proposes a method of real-time machining status monitoring using the data collected by external sensors without interfering with the machining process. A tool wear forecast model is introduced in this article. Multiple process parameters and sensor data are collected. Due to missing data, however, data preprocessing is done applying the interpolation or extrapolation approach and data are standardized in order to create an artificial intelligence-based model. The said model will then be used to forecast tool wear during different processing stages and be compared with other different models, such as: AdaBoost, Support Vector Machine, Decision Tree, and Random Forest. The model developed in this study is based on a Branched Neural Network, which generates the best prediction results among all publicly available algorithms. This approach helps reduce the mean absolute error and root-mean-square error values and can improve by 0.11 in R2.
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