Matches in SemOpenAlex for { <https://semopenalex.org/work/W3130044255> ?p ?o ?g. }
- W3130044255 endingPage "1115" @default.
- W3130044255 startingPage "1106" @default.
- W3130044255 abstract "A cutting tool sustains & preserves the economy of machining activity. Inclusion of unforeseen faults in the cutting tool affects machining accuracy, product quality, and overall efficiency. The efficient supervision of a cutting tool has become essential in modern manufacturing scenario. Health monitoring is a key aspect of prognostic maintenance. It facilitates monitoring of a cutting tool condition for changing in certain parameters, to diagnose the noteworthy change in it. In an era of Artificial Intelligence (AI), the applicability of Machine Learning (ML) in health monitoring is truly commanding as it examines the history and presents signatures for prediction of pre-defined classes. Moreover, advancements in instrumentations, signal processing, data science & analytics have captured the attention of researchers. This paper presents a review of health monitoring techniques applied for cutting tools particularly using vibration as a signal acquisition parameter and machine learning approach used for fault classification and prediction." @default.
- W3130044255 created "2021-03-01" @default.
- W3130044255 creator A5030059507 @default.
- W3130044255 creator A5064533907 @default.
- W3130044255 date "2021-01-01" @default.
- W3130044255 modified "2023-10-02" @default.
- W3130044255 title "Review on tool condition classification in milling: A machine learning approach" @default.
- W3130044255 cites W1486398451 @default.
- W3130044255 cites W1540155273 @default.
- W3130044255 cites W1964357740 @default.
- W3130044255 cites W1965545172 @default.
- W3130044255 cites W1969221044 @default.
- W3130044255 cites W1971593457 @default.
- W3130044255 cites W1973540626 @default.
- W3130044255 cites W1982976530 @default.
- W3130044255 cites W1993058135 @default.
- W3130044255 cites W2003072142 @default.
- W3130044255 cites W2018203407 @default.
- W3130044255 cites W2021831889 @default.
- W3130044255 cites W2023869529 @default.
- W3130044255 cites W2030932242 @default.
- W3130044255 cites W2041752335 @default.
- W3130044255 cites W2050976072 @default.
- W3130044255 cites W2059015806 @default.
- W3130044255 cites W2068157365 @default.
- W3130044255 cites W2068798864 @default.
- W3130044255 cites W2075027378 @default.
- W3130044255 cites W2076159448 @default.
- W3130044255 cites W2076487645 @default.
- W3130044255 cites W2077341607 @default.
- W3130044255 cites W2081721585 @default.
- W3130044255 cites W2089146439 @default.
- W3130044255 cites W2093828611 @default.
- W3130044255 cites W2104976632 @default.
- W3130044255 cites W2131501756 @default.
- W3130044255 cites W2140967873 @default.
- W3130044255 cites W2141663819 @default.
- W3130044255 cites W2144094032 @default.
- W3130044255 cites W2149850568 @default.
- W3130044255 cites W2167368373 @default.
- W3130044255 cites W2208080718 @default.
- W3130044255 cites W2279035659 @default.
- W3130044255 cites W2289713587 @default.
- W3130044255 cites W2335336340 @default.
- W3130044255 cites W2344097077 @default.
- W3130044255 cites W2532054530 @default.
- W3130044255 cites W2567610245 @default.
- W3130044255 cites W2605910748 @default.
- W3130044255 cites W2747277714 @default.
- W3130044255 cites W2790661307 @default.
- W3130044255 cites W2802507231 @default.
- W3130044255 cites W2803411422 @default.
- W3130044255 cites W2894733597 @default.
- W3130044255 cites W2949666355 @default.
- W3130044255 cites W2950911818 @default.
- W3130044255 cites W2979942370 @default.
- W3130044255 cites W2980347176 @default.
- W3130044255 cites W2998506103 @default.
- W3130044255 cites W2998856376 @default.
- W3130044255 cites W3003381082 @default.
- W3130044255 cites W3010201892 @default.
- W3130044255 cites W3023549954 @default.
- W3130044255 cites W3096365635 @default.
- W3130044255 cites W4230096344 @default.
- W3130044255 cites W4239510810 @default.
- W3130044255 cites W4249850461 @default.
- W3130044255 doi "https://doi.org/10.1016/j.matpr.2021.01.523" @default.
- W3130044255 hasPublicationYear "2021" @default.
- W3130044255 type Work @default.
- W3130044255 sameAs 3130044255 @default.
- W3130044255 citedByCount "10" @default.
- W3130044255 countsByYear W31300442552021 @default.
- W3130044255 countsByYear W31300442552022 @default.
- W3130044255 countsByYear W31300442552023 @default.
- W3130044255 crossrefType "journal-article" @default.
- W3130044255 hasAuthorship W3130044255A5030059507 @default.
- W3130044255 hasAuthorship W3130044255A5064533907 @default.
- W3130044255 hasConcept C111472728 @default.
- W3130044255 hasConcept C117671659 @default.
- W3130044255 hasConcept C119599485 @default.
- W3130044255 hasConcept C119857082 @default.
- W3130044255 hasConcept C127313418 @default.
- W3130044255 hasConcept C127413603 @default.
- W3130044255 hasConcept C138885662 @default.
- W3130044255 hasConcept C154945302 @default.
- W3130044255 hasConcept C165205528 @default.
- W3130044255 hasConcept C175551986 @default.
- W3130044255 hasConcept C199360897 @default.
- W3130044255 hasConcept C2522767166 @default.
- W3130044255 hasConcept C26517878 @default.
- W3130044255 hasConcept C2775846686 @default.
- W3130044255 hasConcept C2779530757 @default.
- W3130044255 hasConcept C2779843651 @default.
- W3130044255 hasConcept C2780383046 @default.
- W3130044255 hasConcept C38652104 @default.
- W3130044255 hasConcept C41008148 @default.
- W3130044255 hasConcept C523214423 @default.
- W3130044255 hasConcept C5941749 @default.