Tribological characteristics and wear mechanism of cemented carbide tool in dry machining Ti-6Al-4V

Fan, Yanping; Hao, Zhao Peng; Lin, Jieqiong

doi:10.1504/ijmpt.2017.082620

Cited by 10 publications

(8 citation statements)

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“…33 Increase in cutting speed accelerates the friction between the tool and workpiece, which leads to increase the flank wear. 37 The flank wear steadily increased with increase in depth of cut. 38,39 The adjusted R 2 value of 0.9211 was in reasonable agreement with R 2 value of 0.9585.…”

Section: Resultsmentioning

confidence: 96%

Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network

Shankar

Mohanraj

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this work, the flank wear of the cutting tool is predicted using artificial neural network based on the responses of cutting force and surface roughness. EN8 steel is chosen as a work piece material and turning test is conducted with various levels of speed, feed and depth of cut. Cutting force and surface roughness are measured for both the fresh and dull tool under dry cutting conditions. The tool insert used is CNMG 120408 grade, TiN coated cemented carbide tool. The experiments are conducted based on the response surface methodology face central composite design of experiments. The feed rate (14.52%), depth of cut (27.72%) and the interaction of feed rate and depth of cut (50.39%) influence the cutting force. The feed rate (21.33%) and the interaction of cutting speed and depth of cut (26.67%) influence the flank wear. The feed rate (61.63%) has the significant influence on surface roughness. The feed forward back propagation neural network of 5-n-1 architecture is trained using the algorithms like Levenberg Marquardt, BFGS quasi-Newton, and Gradient Descent with Momentum and Gradient descent with adaptive learning rate. The network performance has been assessed based on their mean square error and computation time. From this analysis, the BFGS quasi-Newton back propagation algorithm produced the least mean squared error value with minimum computation time.

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Section: Resultsmentioning

confidence: 96%

Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network

Shankar

Mohanraj

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…7a, b. In case of uncoated carbide tools at low cutting speed, a layer of adhesive material is formed on the tool face which protects it from wear out (Fan et al, 2016). This is also confirmed by the SEM image of the tool when used for low-speed cutting Fig.…”

Section: Influence Of Tool Wear On Scementioning

confidence: 63%

“…Condition 1 and 2 show the highest rate Figure 11. Temperature variation with increasing the cutting speeds (Fan et al, 2016).…”

Section: Influence Of Tool Wear On Scementioning

confidence: 99%

Tool Wear Progression and its Effect on Energy Consumption in Turning of Titanium Alloy (Ti-6Al-4V)

et al. 2019

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Abstract. To achieve greater productivity, titanium alloy requires cutting at higher speeds (above 100 m min−1) that affects the tool life and energy consumption during the machining process. This research work correlates the wear progression and Specific Cutting Energy (SCE) in turning Ti-6Al-4V alloy using H13 tools (uncoated carbide) in dry conditions from low to high cutting speeds. Cutting condition employed in this study were selected from published wear map developed for titanium (Ti-6Al-4V alloy) with the same tool. Flank wear growth of the tool has been investigated at different length of cuts in correlation with the SCE under different cutting conditions. The useful tool life was found to be shorter at high-speed machining conditions, thus the end of useful tool life criteria (ISO 3685) was reached at a much shorter length of cuts as compared to low-speed machining conditions. The cutting conditions corresponding to high wear rate also resulted in high SCE. Finally, SCE and wear have been related by a linear relationship that can be used to monitor wear and/or SCE utilization during machining. The results help in the selection of appropriate cutting conditions that will enhance the tool life and minimize SCE consumption during machining titanium alloy.

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“…The tool coating loss is observed, and Tw is a combination of pulling off the coating on the flank face. The high tool wear rate can be attributed to the two reasons [35]: At high speed (200 m/min) and doc (1.0 mm), the tool penetration is more, the area of contact at the interaction zones, i.e., the workpiece À tool-tip, tool rake face-chip, and tool flank faceÀuncut chip(strained material), causing sliding friction. Speeds greater than 160 m/min results in vibration amplitudes more than 30 mm, which affects Twr.…”

Section: Effect Of Speedmentioning

confidence: 99%

Parametric optimization while turning Ti-6Al-4V alloy in Mist-MQCL (Green environment) using the DEAR method

et al. 2020

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Sustainability in any production emphasizes green-manufacturing techniques, improvement in quality with energy-efficient techniques, and environment-friendly processes. Titanium machining productivity is greatly influenced by speed, as high cutting velocity raises the temperatures in the shear zone and heat, owing to its low thermal conductivity. Hence in this work, an attempt is made to increase productivity by exploring the efficacy at transition speed for titanium alloy machining. Water-soluble lubricant is mist-sprayed as aerosols at a near-zero temperature in minor quantity, to minimize the temperatures generated during the cutting process at increased speed. Besides, an optimal decision variable vector optimizes multi-goals of machining Titanium grade 5 alloys under Minimum quantity cooling lubrication explored in this study in transitional speed zones. The response goals are the optimization of “vibration, surface quality, tool wear rate, and Material removal rate.” Multi goal optimization achieved by hybrid Taguchi coupled with Data Envelopment Analysis based Ranking (DEAR). The tool wear is very rapid at velocities of 200 mm/min. DEAR technique uses computed Multi performance rank index (MPRI) to predict the best data set at: (velocity, feed, doc) at (120 mm/min, 0.2 mm/rev, 1.0 mm). In this setting, the responses are compared in dry, flood, and MQL environment. It is observed a 30%, 60%, 40% improvement in surface finish, tool life, and vibrations compared to a dry environment and 13% and 3% of roughness and tool wear rate compared to a flood environment. Thus MQCL can be adopted for Ti6Al4V at transitional speeds.

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Tribological characteristics and wear mechanism of cemented carbide tool in dry machining Ti-6Al-4V

Cited by 10 publications

References 0 publications

Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network

Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network

Tool Wear Progression and its Effect on Energy Consumption in Turning of Titanium Alloy (Ti-6Al-4V)

Parametric optimization while turning Ti-6Al-4V alloy in Mist-MQCL (Green environment) using the DEAR method

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