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Tytuł:
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Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors.
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Autorzy:
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Ranjan J; Department of Mechanical Engineering, Indian Institute of Technology Patna, Patna-801103, India.
Patra K; Department of Mechanical Engineering, Indian Institute of Technology Patna, Patna-801103, India.
Szalay T; Department of Manufacturing Science and Engineering, Budapest University of Technology and Economics, H-1111 Budapest, Hungary.
Mia M; Department of Mechanical Engineering, Imperial College London, Exhibition Rd., London SW7 2AZ, UK.
Gupta MK; Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250100, China.
Song Q; Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250100, China.
Krolczyk G; Faculty of Mechanical Engineering, Opole University of Technology, 76 Proszkowska St., 45-758 Opole, Poland.
Chudy R; Faculty of Mechanical Engineering, Opole University of Technology, 76 Proszkowska St., 45-758 Opole, Poland.
Pashnyov VA; Department of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, Chelyabinsk 454080, Russia.
Pimenov DY; Department of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, Chelyabinsk 454080, Russia.
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Źródło:
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Sensors (Basel, Switzerland) [Sensors (Basel)] 2020 Feb 07; Vol. 20 (3). Date of Electronic Publication: 2020 Feb 07.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: Basel, Switzerland : MDPI, c2000-
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References:
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Sensors (Basel). 2018 Dec 11;18(12):. (PMID: 30544961)
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Grant Information:
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TÉT_10-1-2011-0233 The authors sincerely acknowledge the support a joint research project "Multi-sensors based intelligent tool condition monitoring in mechanical micro-machining" supported by project no. INT/HUN/P-01/2012, DST, Govt. of India and project number TÉT_10-1-20
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Contributed Indexing:
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Keywords: adaptive neuro fuzzy inference system; cutting force; micro drilling; vibration; wavelet packet
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Entry Date(s):
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Date Created: 20200213 Date Completed: 20200213 Latest Revision: 20200311
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Update Code:
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20240105
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PubMed Central ID:
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PMC7039300
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DOI:
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10.3390/s20030885
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PMID:
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32046037
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The prevalence of micro-holes is widespread in mechanical, electronic, optical, ornaments, micro-fluidic devices, etc. However, monitoring and detection tool wear and tool breakage are imperative to achieve improved hole quality and high productivity in micro-drilling. The various multi-sensor signals are used to monitor the condition of the tool. In this work, the vibration signals and cutting force signals have been applied individually as well as in combination to determine their effectiveness for tool-condition monitoring applications. Moreover, they have been used to determine the best strategies for tool-condition monitoring by prediction of hole quality during micro-drilling operations with 0.4 mm micro-drills. Furthermore, this work also developed an adaptive neuro fuzzy inference system (ANFIS) model using different time domains and wavelet packet features of these sensor signals for the prediction of the hole quality. The best prediction of hole quality was obtained by a combination of different sensor features in wavelet domain of vibration signal. The model's predicted results were found to exert a good agreement with the experimental results.
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