Szczegóły

Tytuł artykułu

Developing automatic recognition system of drill wear in standard laminated chipboard drilling process

Tytuł czasopisma

Bulletin of the Polish Academy of Sciences: Technical Sciences

Rocznik

2016

Numer

No 3 September

Autorzy publikacji

Wydział PAN

Nauki Techniczne

Wydawca

Polish Academy of Sciences

Data

2016

Identyfikator

ISSN 0239-7528, eISSN 2300-1917

Referencje

Scheffer (2003), Development of a tool wear - monitoring system for hard turning of &, International Journal Machine Tools Manufacture, 43, 973, doi.org/10.1016/S0890-6955(03)00110-X ; Khajavi (1995), Frequency and time domain analyses of sensor signals in drilling - Part I of Machine Tools and Manufacture, International Journal, 35, 775. ; Wilkowski (2011), Vibro - acoustic signals as a source of information about tool wear during laminated chipboard milling, Wood Research, 56, 57. ; Breiman (2001), Random forests, Machine Learning, 45, 5, doi.org/10.1023/A:1010933404324 ; Dimla (2000), On - line metal cutting tool condition monitoring force and vibration analyses of, International Journal Machine Tools Manufacture, 40, 739, doi.org/10.1016/S0890-6955(99)00084-X ; Panda (2006), Drill wear monitoring using back propagation neural network of Materials Processing, Journal Technology, 172, 283. ; Zahra (2003), Gradual wear monitoring of turning inserts using wavelet analysis of ultrasound waves of, International Journal Machine Tools Manufacture, 43, 33. ; Silva (2000), The adaptability of a tool wear monitoring system under changing cutting conditions and, Mechanical Systems Signal Processing, 14, 287, doi.org/10.1006/mssp.1999.1286 ; Patra (2007), Artificial neural network based prediction of drill flank wear from motor current signals, Applied Soft Computing, 7, 929, doi.org/10.1016/j.asoc.2006.06.001 ; Jemielniak (2012), Tool condition monitoring based on numerous signal features, Int J Technol, 59, 73. ; Zahra (2003), Gradual wear monitoring of turning inserts using wavelet analysis of ultrasound waves of &, International Journal Machine Tools Manufacture, 43, 333. ; Zhou (2011), and Tool wear monitoring using acoustic emissions by dominant - feature identification on Instrumentation and, IEEE Transactions Measurement, 60, 547, doi.org/10.1109/TIM.2010.2050974 ; Scheffer (2001), Wear monitoring in turning operations using vibration and strain measurements and, Mechanical Systems Signal Processing, 15, 1185, doi.org/10.1006/mssp.2000.1364 ; Lezanski (2001), An intelligent system for grinding wheel condition monitoring of Materials Processing, Journal Technology, 109, 258. ; Lemaster (2000), The use of process monitoring techniques on a CNC wood router Part Sensor selection, Forest Products Journal, 50, 31. ; Liu (1999), On - line monitoring of flank wear in turning with multilayered feed - forward neural network of &, International Journal Machine Tools Manufacture, 39, 1945, doi.org/10.1016/S0890-6955(99)00020-6 ; Kuo (2000), Multi - sensor integration for on - line tool wear estimation through artificial neural networks and fuzzy neural network of Artificial, Engineering Applications Intelligence, 13, 249, doi.org/10.1016/S0952-1976(00)00008-7 ; Leś (2013), Automatic recognition of industrial tools using artificial intelligence approach Systems with Application, Expert, 40, 4777.

DOI

10.1515/bpasts-2016-0071

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