Szczegóły

Tytuł artykułu

Artificial neural network based tool wear estimation on dry hard turning processes of AISI4140 steel using coated carbide tool

Tytuł czasopisma

Bulletin of the Polish Academy of Sciences: Technical Sciences

Rocznik

2017

Wolumin

65

Numer

No 4

Autorzy

Wydział PAN

Nauki Techniczne

Zakres

553-559

Wydawca

Polish Academy of Sciences

Data

2017

Identyfikator

ISSN 0239-7528, eISSN 2300-1917

Referencje

Rizal (null), Online tool wear prediction system in the turning process using an adaptive neuro - fuzzy inference system, Applied Soft Computing, 13, 1960. ; Lim (1995), Tool wear monitoring in machine turning of Material processing technology, Journal, 51, 1. ; Dimla (2000), On - line metal cutting tool condition monitoring II Tool - state classification using multi - layer perceptron neural networks, International Journal of Machine Tools Manufacture, 22, 769. ; (2002), Sick On - line and indirect tool wear monitoring in turning with artificial neural networks : a review of more than a decade of research Mechanical Systems and Signal Processing, null, 13, 487. ; Suresh (2012), Some studies on hard turning of steel using multilayer coated carbide tool, Measurement, 45, 4340. ; Sharma (2008), tool wear estimation for turning, Intell Manuf, 19, 99. ; Alonsoa (2008), of the structure of vibration signals for tool wear detection Systems and Signal Processing, Analysis Mechanical, 23, 735. ; Chelladurai (2008), of a cutting tool condition monitoring system for high speed turning operation by vibration and strain analysis, Development Int Technol, 15, 471. ; Aouci (2012), Analysis of surface roughness and cutting force components in hard turning with CBN tool : prediction model and cutting conditions optimization, Measurement, 45, 1. ; Asilturk (2011), Akkus Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method, Measurement, 44, 1697. ; Bhuiyan (2014), Monitoring the tool wear surface roughness and chip formation occurrences using multiple sensors in turning of, Journal Manufacturing Systems, 20, 476. ; Tanikić (2016), Application of response surface methodology and fuzzy logic based system for determining metal cutting temperature Pol, Tech, 24, 435. ; Chmielewski (2016), Metal ceramic functionally graded materials manufacturing characterization application Bull Pol, Tech, 11, 1. ; Dan (1990), Tool wear and failure monitoring techniques for turning a, review Int J Tools, 30, 579. ; Dimla (2000), On - line metal cutting tool condition monitoring Force and vibration analyses of Machine Tools and Manufacture, International Journal, 18, 739. ; Ozel (2005), Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks of Machine Tools and Manufacture, International Journal, 21, 467. ; Siddhpura (2013), of flank wear prediction methods for tool condition monitoring in a turning process, review Int J Adv Manuf Technol, 14, 1. ; Dinakaran (2009), An experimental investigation on monitoring of crater wear in turning using the ultrasonic technique of Machine Tools and Manufacture, International Journal, 12, 15. ; Bartarya (2012), State of the art in hard turning, International Journal of Machine Tools Manufacture, 53.

DOI

10.1515/bpasts-2017-0060

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