A novel approach for in-situ characterization and probabilistic prediction of cutting tool fatigue in machining of Ti-6Al-4V

Avery Hartley; Jenna Money; Julius Schoop

doi:10.1016/j.mfglet.2023.10.002

A novel approach for in-situ characterization and probabilistic prediction of cutting tool fatigue in machining of Ti-6Al-4V

Avery Hartley, Jenna Money, Julius Schoop

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

The wear behavior of cutting tools is highly complex due to combined thermal, mechanical, and chemical loads. As a result, most current tool-wear prediction methodologies are either empirical or highly oversimplified analytical or numerical models of stable abrasive and diffusive wear mechanisms. To predict the complex physics of catastrophic tool edge chipping, which in practice bounds feasible process parameters, this manuscript presents a novel approach for in-situ characterization of tool edge fatigue loads and probabilistic prediction of the likelihood of time to fracture. The results of the analysis suggest encouraging possibilities for more physics-informed and data-driven process design.

Original language	English
Pages (from-to)	79-82
Number of pages	4
Journal	Manufacturing Letters
Volume	38
DOIs	https://doi.org/10.1016/j.mfglet.2023.10.002
State	Published - Nov 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s)

Funding

This work was supported by the U.S. National Science Foundation , grant number 2143806 , project title: “CAREER: Thermomechanical Response and Fatigue Performance of Surface Layers Engineered by Finish Machining: In-situ Characterization and Digital Process Twin”.

Funders	Funder number
National Science Foundation (NSF)	2143806

Keywords

In-situ characterization
Machining
Process modeling

ASJC Scopus subject areas

Mechanics of Materials
Industrial and Manufacturing Engineering

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10.1016/j.mfglet.2023.10.002

Cite this

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title = "A novel approach for in-situ characterization and probabilistic prediction of cutting tool fatigue in machining of Ti-6Al-4V",

abstract = "The wear behavior of cutting tools is highly complex due to combined thermal, mechanical, and chemical loads. As a result, most current tool-wear prediction methodologies are either empirical or highly oversimplified analytical or numerical models of stable abrasive and diffusive wear mechanisms. To predict the complex physics of catastrophic tool edge chipping, which in practice bounds feasible process parameters, this manuscript presents a novel approach for in-situ characterization of tool edge fatigue loads and probabilistic prediction of the likelihood of time to fracture. The results of the analysis suggest encouraging possibilities for more physics-informed and data-driven process design.",

keywords = "In-situ characterization, Machining, Process modeling",

author = "Avery Hartley and Jenna Money and Julius Schoop",

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year = "2023",

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doi = "10.1016/j.mfglet.2023.10.002",

language = "English",

volume = "38",

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T1 - A novel approach for in-situ characterization and probabilistic prediction of cutting tool fatigue in machining of Ti-6Al-4V

AU - Hartley, Avery

AU - Money, Jenna

AU - Schoop, Julius

PY - 2023/11

Y1 - 2023/11

N2 - The wear behavior of cutting tools is highly complex due to combined thermal, mechanical, and chemical loads. As a result, most current tool-wear prediction methodologies are either empirical or highly oversimplified analytical or numerical models of stable abrasive and diffusive wear mechanisms. To predict the complex physics of catastrophic tool edge chipping, which in practice bounds feasible process parameters, this manuscript presents a novel approach for in-situ characterization of tool edge fatigue loads and probabilistic prediction of the likelihood of time to fracture. The results of the analysis suggest encouraging possibilities for more physics-informed and data-driven process design.

AB - The wear behavior of cutting tools is highly complex due to combined thermal, mechanical, and chemical loads. As a result, most current tool-wear prediction methodologies are either empirical or highly oversimplified analytical or numerical models of stable abrasive and diffusive wear mechanisms. To predict the complex physics of catastrophic tool edge chipping, which in practice bounds feasible process parameters, this manuscript presents a novel approach for in-situ characterization of tool edge fatigue loads and probabilistic prediction of the likelihood of time to fracture. The results of the analysis suggest encouraging possibilities for more physics-informed and data-driven process design.

KW - In-situ characterization

KW - Machining

KW - Process modeling

UR - https://www.scopus.com/pages/publications/85175618634

UR - https://www.scopus.com/inward/citedby.url?scp=85175618634&partnerID=8YFLogxK

U2 - 10.1016/j.mfglet.2023.10.002

DO - 10.1016/j.mfglet.2023.10.002

M3 - Article

AN - SCOPUS:85175618634

SN - 2213-8463

VL - 38

SP - 79

EP - 82

JO - Manufacturing Letters

JF - Manufacturing Letters

ER -

A novel approach for in-situ characterization and probabilistic prediction of cutting tool fatigue in machining of Ti-6Al-4V

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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