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A data-driven appro...
A data-driven approach for tool wear recognition and quantitative prediction based on radar map feature fusion
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- Li, Xuebing (författare)
- Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China.
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- Liu, Xianli (författare)
- Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China.
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- Yue, Caixu (författare)
- Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China.
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- Liu, Shaoyang (författare)
- Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China.
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- Zhang, Bowen (författare)
- Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China.
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- Li, Rongyi (författare)
- Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China.
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- Liang, Steven Y. (författare)
- Georgia Inst Technol, George W Woodruff Sch Mech Engn, Atlanta, GA 30332 USA.
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- Wang, Lihui (författare)
- KTH,Hållbara produktionssystem
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Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China Georgia Inst Technol, George W Woodruff Sch Mech Engn, Atlanta, GA 30332 USA. (creator_code:org_t)
- Elsevier BV, 2021
- 2021
- Engelska.
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Ingår i: Measurement. - : Elsevier BV. - 0263-2241 .- 1873-412X. ; 185
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Tool wear monitoring during the cutting process is crucial for ensuring part quality and productivity. A datadriven monitoring approach based on radar map feature fusion is proposed for tool wear recognition and quantitative prediction, aiming at tracking the evolution of tool wear comprehensively. Specifically, the sensitive features from multi-source signals are fused by a radar map, and health indicators capable of characterizing the tool wear evolution are obtained. For the recognition of tool wear state and the quantitative prediction of tool wear values, the Adaboost Decision Tree (Adaboost-DT) ensemble learning model and stacked bi-directional long short-term memory (SBiLSTM) deep learning network are established, respectively. Experimental results demonstrated that the proposed approach could recognize the current wear state quickly and accurately whilst predicting wear values based on limited historical data available. Combining tool wear recognition and prediction results contributes to making a more flexible tool replacement decision in intelligent manufacturing processes.
Nyckelord
- Tool wear monitoring
- Radar map feature fusion
- Tool health indicator
- Adaboost-DT
- SBiLSTM
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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