Cutting-tools degradation assessment for structural-steel machining centers

Tsung Ren Huang; Hao Ting Chen; Min Chun Pan

doi:10.1115/IMECE2020-24643

Cutting-tools degradation assessment for structural-steel machining centers

Tsung Ren Huang, Hao Ting Chen, Min Chun Pan

Department of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

To assess cutting-tools degradation, mounted accelerometers and current clamp probes were used to acquire machining vibration for both the band-saw machine and the drilling machine, of which a structural-steel machining center is composed. Significant features were first extracted through spectral analysis, and tool degradation assessment was conducted through using a supervised learning scheme, self-organizing map. The results reveal that 10% remaining useful life can be predicted before the band-saws and drills wore out.

Original language	English
Title of host publication	Advanced Manufacturing
Publisher	American Society of Mechanical Engineers (ASME)
ISBN (Electronic)	9780791884492
DOIs	https://doi.org/10.1115/IMECE2020-24643
State	Published - 2020
Event	ASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020 - Virtual, Online Duration: 16 Nov 2020 → 19 Nov 2020

Publication series

Name	ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
Volume	2B-2020

Conference

Conference	ASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020
City	Virtual, Online
Period	16/11/20 → 19/11/20

Keywords

Feature extraction
Remaining useful life
Self-organizing map
Structural-steel machining
Tool degradation assessment

Access to Document

10.1115/IMECE2020-24643

Development of Smart-Automation Laser Processing System with Iot-Based Machining-Integration Services( II )
Tung, P.-C. (PI)
1/12/19 → 31/10/20
Project: Research

Cite this

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title = "Cutting-tools degradation assessment for structural-steel machining centers",

abstract = "To assess cutting-tools degradation, mounted accelerometers and current clamp probes were used to acquire machining vibration for both the band-saw machine and the drilling machine, of which a structural-steel machining center is composed. Significant features were first extracted through spectral analysis, and tool degradation assessment was conducted through using a supervised learning scheme, self-organizing map. The results reveal that 10% remaining useful life can be predicted before the band-saws and drills wore out.",

keywords = "Feature extraction, Remaining useful life, Self-organizing map, Structural-steel machining, Tool degradation assessment",

author = "Huang, {Tsung Ren} and Chen, {Hao Ting} and Pan, {Min Chun}",

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series = "ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)",

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Huang, TR, Chen, HT & Pan, MC 2020, Cutting-tools degradation assessment for structural-steel machining centers. in Advanced Manufacturing. ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE), vol. 2B-2020, American Society of Mechanical Engineers (ASME), ASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020, Virtual, Online, 16/11/20. https://doi.org/10.1115/IMECE2020-24643

Cutting-tools degradation assessment for structural-steel machining centers. / Huang, Tsung Ren; Chen, Hao Ting; Pan, Min Chun.
Advanced Manufacturing. American Society of Mechanical Engineers (ASME), 2020. (ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE); Vol. 2B-2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Huang, Tsung Ren

AU - Chen, Hao Ting

AU - Pan, Min Chun

PY - 2020

Y1 - 2020

N2 - To assess cutting-tools degradation, mounted accelerometers and current clamp probes were used to acquire machining vibration for both the band-saw machine and the drilling machine, of which a structural-steel machining center is composed. Significant features were first extracted through spectral analysis, and tool degradation assessment was conducted through using a supervised learning scheme, self-organizing map. The results reveal that 10% remaining useful life can be predicted before the band-saws and drills wore out.

AB - To assess cutting-tools degradation, mounted accelerometers and current clamp probes were used to acquire machining vibration for both the band-saw machine and the drilling machine, of which a structural-steel machining center is composed. Significant features were first extracted through spectral analysis, and tool degradation assessment was conducted through using a supervised learning scheme, self-organizing map. The results reveal that 10% remaining useful life can be predicted before the band-saws and drills wore out.

KW - Feature extraction

KW - Remaining useful life

KW - Self-organizing map

KW - Structural-steel machining

KW - Tool degradation assessment

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U2 - 10.1115/IMECE2020-24643

DO - 10.1115/IMECE2020-24643

M3 - 會議論文篇章

AN - SCOPUS:85101245590

T3 - ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)

BT - Advanced Manufacturing

PB - American Society of Mechanical Engineers (ASME)

T2 - ASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020

Y2 - 16 November 2020 through 19 November 2020

ER -

Cutting-tools degradation assessment for structural-steel machining centers

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Projects

Development of Smart-Automation Laser Processing System with Iot-Based Machining-Integration Services( II )

Cite this