RESEARCH PAPER
Making Informed Decisions in Cutting Tool Maintenance in Milling: A KNN-Based Model Agnostic Approach
1
Department of Computer Engineering,, Modern Education Society’s Wadia College of Engineering,, India
2
Department of Mechanical Engineering,, COEP Technological University, India
3
Department of Computer and Information Technology,, COEP Technological University, India
4
bInstitute of Fluid Flow Machinery, Polish Academy of Sciences,, Poland
5
Department of Mechanical Engineering,, Cummins College of Engineering for Women, India
These authors had equal contribution to this work
Submission date: 2025-11-22
Final revision date: 2025-12-22
Acceptance date: 2026-01-27
Online publication date: 2026-03-01
Corresponding author
Rohan N Soman
bInstitute of Fluid Flow Machinery, Polish Academy of Sciences,, 14, Fiszera Street, 80-231, Gdansk, Poland
bInstitute of Fluid Flow Machinery, Polish Academy of Sciences,, 14, Fiszera Street, 80-231, Gdansk, Poland
KEYWORDS
tool condition monitoringk-nearest neighborsface milling cuttermodel agnostic approachwhite box model
TOPICS
ABSTRACT
In machining processes, monitoring the condition of the tool is a crucial aspect for high productivity and quality of the product. Using different ML techniques in Tool Condition Monitoring (TCM) enables a better analysis of the large amount of data of different signals acquired during the machining processes. 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 model’s performance. In this research for the first time, a model agnostic approach to increase the interpretability and get an in-depth understanding of decision making is done. 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 informed decision-making.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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