Evaluation of Cutting Tool Vibration and Surface Roughness in Hard Turning of AISI 52100 Steel: An Experimental and ANN Approach

Ambhore, Nitin; Kamble, Dinesh; Chinchanikar, Satish

doi:10.1007/s42417-019-00136-x

Cited by 27 publications

(16 citation statements)

References 14 publications

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“…3 However, turning to machine hard materials is difficult. 4 Hardened steel materials have high strength, corrosion resistance and wear resistance. 3 High hardness value is obtained in AISI H11 steels by heat treatment.…”

Section: Introductionmentioning

confidence: 99%

Investigation of machinability properties of AISI H11 tool steel for sustainable manufacturing

Şahinoğlu

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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In this experimental study, vibration, energy consumption, power consumption and surface roughness values that occur in the machining of AISI H11 tool steel under CO2, coolant and dry cutting conditions were investigated. The effects of different cutting parameters and cooling systems on machinability were investigated. Analysis of variance was done. Regression equations were obtained. Relationships between power consumption, vibration and surface roughness are explained with mathematical equations. Finally, the optimum CC were determined by the multiple optimization methods. According to the test results, while the instantaneous power consumption increases by increasing cutting parameters, energy consumption decreases as the processing time is shortened. Vibration value increases by increasing cutting parameters. The highest vibration value occurs in cutting with CO2. Compared to dry and CO2 cutting, the vibration is lowest in coolant cutting. The friction decreases with the coolant and the vibration value decreases. There is a similar relationship between vibration and surface roughness value. The most effective parameter on the surface roughness value is the feed rate. It was seen that the most suitable CC for the most efficient cutting, the lowest energy consumption, vibration and surface roughness value, under coolant cutting, 0.2 mm depth of cut, 175 m/min cutting speed and 0.119 mm/rev feed rate. With optimum CC, the vibration value was reduced by 5.18%, the surface roughness value by 37.12%, energy consumption by 36.19% and the machine efficiency was increased by 7.16%.

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Section: Introductionmentioning

confidence: 99%

Investigation of machinability properties of AISI H11 tool steel for sustainable manufacturing

Şahinoğlu

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…Bearing material can be through hardened and case hardened to achieve a minimum hardness of 58 HRC. 1,2 CBN is an expensive tool for machining hardened steel. Therefore, the search for the best alternate option for the machining of hardened steel, commercially available and worthy as compared to CBN cutting tools is a matter of interest.…”

Section: Introductionmentioning

confidence: 99%

Experimental study and multi-objective optimization of process parameters during turning of 100Cr6 using C-type advanced coated tools

Bhandarkar

Mohanty

Sarangi

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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The drive of this research is to examine the machinability of 100Cr6 bearing steel using advanced C-type cutting tools. Experimental studies investigated the effects of machining variables on the surface quality, chip reduction coefficient and cutting force. Seven advanced coated tools were checked for characterization by micro hardness (VHN), adhesion quality, X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDXS). The experimental trials were planned by Taguchi’s L18 orthogonal array using a mixed-level design. Two numerical machining variables feed rate and cutting speed, and one categorical machining variable tool type was taken into consideration while a constant depth of cut was kept for all trails. A combined Taguchi-Satisfaction function distance measure approach was implemented for multi-response optimization. The most promising machining parameter setting for minimization of surface roughness, cutting force, and chip reduction coefficient was identified. The most important process parameter was found to be tool-type. Ceramics tools are found to be best trailed by WC coated tools under most of the conditions. Lower tool wear was observed in the CBN tool as compared to others.

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“…The reduction of machining cost is noticed during hard turning. Ambhore et al [26] developed mathematical and ANN model to study vibration acceleration and surface roughness in turning hardened AISI 52100 steel. ANN model proved to be effective in prediction of responses compared to regression.…”

Section: Introductionmentioning

confidence: 99%

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

Panda

Sahoo

Panigrahi

et al. 2020

Mechanics & Industry

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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, tool condition monitoring (TCM) study will be definitely worthwhile for the effective application in hard part turning. The current study examines about the online prediction of flank wear and surface roughness monitoring during dry hard turning of AISI 52100 steel (55 ± 1 HRC) utilizing MTCVD multilayer coated carbide insert (TiN/TiCN/Al2O3) considering machining parameters and vibration signals through development of prediction model (MLR and MQR) after studying the Pearson correlation coefficient and test for its accuracy. Pearson correlation coefficient for feed on flank wear is utmost pursued by acceleration amplitude of vibration (Vy) in radial direction, depth of cut and cutting speed. Similarly, acceleration amplitude of vibration followed by cutting speed and feed has strong correlation with surface roughness. MQR model predicts well for responses as percentage of error is quite less and cutting speed is obtained to be the most important parameter for vibration signal. Multiple quadratic regression (MQR) models are observed to be noteworthy, effective and adequate to predict response outputs with regards to the combined effect of machining parameters and vibration signals online. A corrective measure can safely be taken with reasonable degree of accuracy during hard turning.

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Evaluation of Cutting Tool Vibration and Surface Roughness in Hard Turning of AISI 52100 Steel: An Experimental and ANN Approach

Cited by 27 publications

References 14 publications

Investigation of machinability properties of AISI H11 tool steel for sustainable manufacturing

Investigation of machinability properties of AISI H11 tool steel for sustainable manufacturing

Experimental study and multi-objective optimization of process parameters during turning of 100Cr6 using C-type advanced coated tools

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

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