Stochastic Tool Wear Prediction for Sustainable Manufacturing

Peng Wang; Robert X. Gao

doi:10.1016/j.procir.2016.03.101

Stochastic Tool Wear Prediction for Sustainable Manufacturing

Peng Wang
, Robert X. Gao

Research output: Contribution to journal › Conference article › peer-review

21 Scopus citations

Abstract

To provide scientific support for decision-making in critical applications such as maintenance scheduling and inventory management, tool wear monitoring and service life prediction are of significance to achieving sustainable manufacturing. Past research typically assumed time-invariant machining settings in modeling wear progression, hence is limited in accurately tracking varying wear rates. This paper presents a stochastic joint-state-and-parameter model with machining setting as a parameter that affects the state evolution or tool wear propagation. The model is embedded in a particle filter for recursive wear state prediction. Effectiveness of this method is verified through experimental data measured on a CNC milling machine.

Original language	English
Pages (from-to)	236-241
Number of pages	6
Journal	Procedia CIRP
Volume	48
DOIs	https://doi.org/10.1016/j.procir.2016.03.101
State	Published - 2016
Event	23rd CIRP Conference on Life Cycle Engineering, LCE 2016 - Berlin, Germany Duration: May 22 2016 → May 24 2016

Bibliographical note

Publisher Copyright:
© 2016 The Authors.

Funding

This research has been parttially supported by National Science Foundation under grant CCF-1331850 and CMMI-1300999.

Funders	Funder number
National Science Foundation (NSF)	CCF-1331850, CMMI-1300999

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Tool wear prediction
particle filter
stochastic modeling

ASJC Scopus subject areas

Control and Systems Engineering
Industrial and Manufacturing Engineering

Access to Document

10.1016/j.procir.2016.03.101

Cite this

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title = "Stochastic Tool Wear Prediction for Sustainable Manufacturing",

abstract = "To provide scientific support for decision-making in critical applications such as maintenance scheduling and inventory management, tool wear monitoring and service life prediction are of significance to achieving sustainable manufacturing. Past research typically assumed time-invariant machining settings in modeling wear progression, hence is limited in accurately tracking varying wear rates. This paper presents a stochastic joint-state-and-parameter model with machining setting as a parameter that affects the state evolution or tool wear propagation. The model is embedded in a particle filter for recursive wear state prediction. Effectiveness of this method is verified through experimental data measured on a CNC milling machine.",

keywords = "Tool wear prediction, particle filter, stochastic modeling",

author = "Peng Wang and Gao, \{Robert X.\}",

note = "Publisher Copyright: {\textcopyright} 2016 The Authors.; 23rd CIRP Conference on Life Cycle Engineering, LCE 2016 ; Conference date: 22-05-2016 Through 24-05-2016",

year = "2016",

doi = "10.1016/j.procir.2016.03.101",

language = "English",

volume = "48",

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T1 - Stochastic Tool Wear Prediction for Sustainable Manufacturing

AU - Wang, Peng

AU - Gao, Robert X.

PY - 2016

Y1 - 2016

N2 - To provide scientific support for decision-making in critical applications such as maintenance scheduling and inventory management, tool wear monitoring and service life prediction are of significance to achieving sustainable manufacturing. Past research typically assumed time-invariant machining settings in modeling wear progression, hence is limited in accurately tracking varying wear rates. This paper presents a stochastic joint-state-and-parameter model with machining setting as a parameter that affects the state evolution or tool wear propagation. The model is embedded in a particle filter for recursive wear state prediction. Effectiveness of this method is verified through experimental data measured on a CNC milling machine.

AB - To provide scientific support for decision-making in critical applications such as maintenance scheduling and inventory management, tool wear monitoring and service life prediction are of significance to achieving sustainable manufacturing. Past research typically assumed time-invariant machining settings in modeling wear progression, hence is limited in accurately tracking varying wear rates. This paper presents a stochastic joint-state-and-parameter model with machining setting as a parameter that affects the state evolution or tool wear propagation. The model is embedded in a particle filter for recursive wear state prediction. Effectiveness of this method is verified through experimental data measured on a CNC milling machine.

KW - Tool wear prediction

KW - particle filter

KW - stochastic modeling

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UR - https://www.scopus.com/pages/publications/84985946047#tab=citedBy

U2 - 10.1016/j.procir.2016.03.101

DO - 10.1016/j.procir.2016.03.101

M3 - Conference article

AN - SCOPUS:84985946047

SN - 2212-8271

VL - 48

SP - 236

EP - 241

JO - Procedia CIRP

JF - Procedia CIRP

T2 - 23rd CIRP Conference on Life Cycle Engineering, LCE 2016

Y2 - 22 May 2016 through 24 May 2016

ER -

Stochastic Tool Wear Prediction for Sustainable Manufacturing

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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