Prediction models for on-line cutting tool and machined surface condition monitoring during hard turning considering vibration signal

Turning of hardened steel is an immense issue of interest concerning with machining technology and scientific research. A strategy to analyze vibration signals and its correlation on surface roughness and tool wear has not attracted much breakthrough in research so far in hard machining. Therefore,...

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Veröffentlicht in:	Mechanics & industry : an international journal on mechanical sciences and engineering applications 2020, Vol.21 (5), p.520
Hauptverfasser:	Panda, Amlana, Sahoo, Ashok Kumar, Panigrahi, Isham, Rout, Arun Kumar
Format:	Artikel
Sprache:	eng
Schlagworte:	Acceleration Aluminum oxide Amplitudes Carbide cutting tools Correlation analysis Correlation coefficients Cutting parameters Cutting speed Cutting tools Fault diagnosis Investigations Machine tools Machinery condition monitoring Mathematical models Multilayers Neural networks Prediction models Regression analysis Sensors Signal processing Surface roughness Titanium carbonitride Tool wear Turning (machining) Variance analysis Vibration Vibration analysis Vibration measurement Vibration monitoring
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