Details

Title

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

Journal title

Bulletin of the Polish Academy of Sciences Technical Sciences

Yearbook

2016

Volume

64

Issue

No 3

Authors

Divisions of PAS

Nauki Techniczne

Coverage

633-640

Date

2016

Identifier

DOI: 10.1515/bpasts-2016-0071 ; ISSN 2300-1917

Source

Bulletin of the Polish Academy of Sciences: Technical Sciences; 2016; 64; No 3; 633-640

References

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.
×