Estimating tool life from measurements during longitudinal turning process using linear least squares

Abdusalam I. Alkhwaji (1) , Muamar M. BenIsa (2) , Hitem A. Aswihli (3)
(1) Alasmarya Islamic University, Mechanical & Industrial Engineering Department, Zliten, Libya ,
(2) Alasmarya Islamic University, Mechanical & Industrial Engineering Department, Zliten, Libya ,
(3) Alasmarya Islamic University, Mechanical & Industrial Engineering Department, Zliten, Libya

Abstract

This research paper presents a procedure to estimate parameters for the modified Taylor equation using experimental measurement. The end-of-life times for each laboratory test were recorded well. The tool life has been measured by observing an abnormal change in the observed pitch of a sound from the cutting tool. The objective of this research is to understand the wear mechanisms of the insert carbide tool for different speed machining under dry condition using longitudinal turning process. The tool life equation obtained by linearizing the expanded Taylor tool life equation and solving a linear system of equations using linear least squares. In this study, both the experimental data and analytical solution show reasonable fit. The estimated tool life model simulates the measured end of tool life using the sound inspection method. An additional analysis to confirm effects of each cutting parameter on the tool life have been presented. Estimating the Tool life (TL) model using nonlinear least squares could give more accurate results which is going to be considered in the future study. The significance of this study is to save resources in the manufacturing industry by avoiding estimating tool life during turning process. Since that cause delay in the production time and waste of resources.               

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Authors

Abdusalam I. Alkhwaji
alkhwaji@asmarya.edu.ly (Primary Contact)
Muamar M. BenIsa
Hitem A. Aswihli
Alkhwaji, A. I., Muamar M. BenIsa, & Hitem A. Aswihli. (2024). Estimating tool life from measurements during longitudinal turning process using linear least squares. Journal of Pure & Applied Sciences, 23(1), 1–7. https://doi.org/10.51984/jopas.v23i1.2888

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