@inproceedings{25993053cd114f62becf0c315edfcbe5,
title = "Logistic classification for tool life modeling in machining",
abstract = "This paper describes the application of logistic classification for tool life modeling and prediction in an industrial setting using shop floor data. Tool life is treated as a classification problem since tool wear can only be measured at the time of tool replacement in a production environment. Laboratory tool wear experiments are used to simulate shop floor wear data by two states: not worn (class 0); and worn (class 1). To incorporate non-linearity in logistic classification, a log-transformation of input features is performed. The logistic classification approach, results, and interpretability of the logistic model are presented.",
keywords = "Logistic classification, Machine learning, Tool wear",
author = "Jaydeep Karandikar and Tony Schmitz and Scott Smith",
note = "Publisher Copyright: {\textcopyright} 2020 The Authors. Published by Elsevier B.V.; 9th CIRP Conference on High Performance Cutting, HPC 2020 ; Conference date: 24-05-2021 Through 26-05-2021",
year = "2020",
doi = "10.1016/j.procir.2021.03.125",
language = "English",
series = "Procedia CIRP",
publisher = "Elsevier B.V.",
pages = "106--109",
editor = "Erdem Ozturk and David Curtis and Hassan Ghadbeigi",
booktitle = "9th CIRP Conference on High Performance Cutting, HPC 2020",
}