Making informed decisions in cutting tool maintenance in milling: A KNN based model agnostic approach

Abstract

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