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Erschienen in: Journal of Intelligent Manufacturing 3/2019

02.06.2017

A novel integrated tool condition monitoring system

verfasst von: Amit Kumar Jain, Bhupesh Kumar Lad

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 3/2019

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Abstract

A tool condition monitoring (TCM) system is vital for the intelligent machining process. However, literature has mostly ignored the interaction effect between product quality and tool degradation and has devoted less attention to the criterion of integrated diagnostics and prognostics to cutting tools. In this paper, we aim to bridge the gap and make an attempt to propose a novel integrated tool condition monitoring system based on the relationship between product quality and tool degradation. First, a cost efficient experimentation concerning high-speed CNC milling machining was implemented. Subsequently, a comprehensive correlation investigation was performed; revealing strong positive relationship exists between product quality and tool degradation. Mapping this relationship, an integrated TCM system pertaining to diagnostics and prognostics was proposed. Herein, the diagnostic reliability was enhanced by researching on the use of a multi-level categorization of degradation. The prognostic competence was enhanced by formulating it explicitly for the tools critical zone as a function of tool life. The system is integrated in a manner that, whenever the degradation curve of the tool reaches the critical zone, prognostics module is triggered, and remaining useful life is assessed instantaneously. To enhance the performance of this system, it is modeled employing support vector machine with optimal training technique. The proposed system was validated based on the experimental data. An extensive performance investigation showed that the proposed system provides a robust problem-solving framework for the intelligent machining process.

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Metadaten
Titel
A novel integrated tool condition monitoring system
verfasst von
Amit Kumar Jain
Bhupesh Kumar Lad
Publikationsdatum
02.06.2017
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 3/2019
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-017-1334-2

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