  In machining processes, monitoring the condition of the tool is a crucial
aspect to ensure high productivity and quality of the product. Using different
machine learning techniques in Tool Condition Monitoring TCM enables a better
analysis of the large amount of data of different signals acquired during the
machining processes. The real time force signals encountered during the process
were acquired by performing numerous experiments. Different tool wear
conditions were considered during the experimentation. A comprehensive
statistical analysis of the data and feature selection using decision trees was
conducted, and the KNN algorithm was used to perform classification.
Hyperparameter tuning of the model was done to improve the models performance.
Much research has been done to employ machine learning approaches in tool
condition monitoring systems, however, a model agnostic approach to increase
the interpretability of the process and get an in depth understanding of how
the decision making is done is not implemented by many. This research paper
presents a KNN based white box model, which allows us to dive deep into how the
model performs the classification and how it prioritizes the different features
included. This approach helps in detecting why the tool is in a certain
condition and allows the manufacturer to make an informed decision about the
tools maintenance.
