A self-organizing approach to the prediction and detection of tool wear

A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

“…A decision tree is a flowchart-like structure in which each internal node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf node holds a class label. Jiaa and Dornfeld [43] proposed a decision tree-based method for the prediction of tool flank wear in a turning operation using acoustic emission and cutting force signals. The features characterizing the AE root-mean-square and cutting force signals were extracted from both time and frequency domains.…”

Wear modelling in mild steel orthogonal cutting when using uncoated carbide tools

“…Jiaa and Dornfeld [13] proposed two wear monitoring systems by the decision tree method and group method of data handling (GMDH), measuring acoustic signals and cutting forces in turning operation and then evaluating tool wear. Other experimental studies can be recognized on literature, as flank wear evaluation by cutting force monitoring proposed by Sikdar and Chen [14], or impact diagnostic excitation for monitoring flank wear proposed by Gong et al [15]; as well some Artificial Intelligence techniques, as well as neural network and fuzzy logic, or a mathematical approaches were suggested to describe the tool wear phenomena [16][17][18][19].…”

A brief review: acoustic emission method for tool wear monitoring during turning

“…In [26], the decision tree method and the GMDH were adopted for the recognition and prediction of the tool wear state in a turning operation using acoustic emission and cutting force signals. The GMDH algorithm determines a representation of the real-time machining system interrelationship between tool flank wear and the quantitative measure of sensor variables involved.…”