English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In machining processes, monitoring the condition of the tool is a crucialaspect to ensure high productivity and quality of the product. Using differentmachine learning techniques in Tool Condition Monitoring TCM enables a betteranalysis of the large amount of data of different signals acquired during themachining processes. The real time force signals encountered during the processwere acquired by performing numerous experiments. Different tool wearconditions were considered during the experimentation. A comprehensivestatistical analysis of the data and feature selection using decision trees wasconducted, 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 toolcondition monitoring systems, however, a model agnostic approach to increasethe interpretability of the process and get an in depth understanding of howthe decision making is done is not implemented by many. This research paperpresents a KNN based white box model, which allows us to dive deep into how themodel performs the classification and how it prioritizes the different featuresincluded. This approach helps in detecting why the tool is in a certaincondition and allows the manufacturer to make an informed decision about thetools maintenance.

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