Details

Title

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

Journal title

Bulletin of the Polish Academy of Sciences: Technical Sciences

Yearbook

2017

Numer

No 4

Publication authors

Divisions of PAS

Nauki Techniczne

Publisher

Polish Academy of Sciences

Date

2017

Identifier

ISSN 0239-7528, eISSN 2300-1917

References

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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