Surface integrity and tool life when turning of Ti-6Al-4V with coolant applied by different methods

Silva, Rosemar B. da; Sales, Wisley Falco; Costa, Éder Silva; Ezugwu, E. O.; Bonney, J.; Silva, Márcio Bacci da; Machado, Álisson Rocha

doi:10.1007/s00170-017-0658-6

Cited by 20 publications

(10 citation statements)

References 26 publications

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“…Cryogenic cutting with PCD provided the best solution. Da Silva et al [ 43 ] focused on surface integrity and tool life under different cooling conditions, and high-pressure coolant provided the best tool life. Said et al [ 44 ] investigated the studies on using nano-cutting fluids comprehensively.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Kuntoğlu

Aslan

Sağlam

et al. 2020

Sensors

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Optimization of tool life is required to tune the machining parameters and achieve the desired surface roughness of the machined components in a wide range of engineering applications. There are many machining input variables which can influence surface roughness and tool life during any machining process, such as cutting speed, feed rate and depth of cut. These parameters can be optimized to reduce surface roughness and increase tool life. The present study investigates the optimization of five different sensorial criteria, additional to tool wear (VB) and surface roughness (Ra), via the Tool Condition Monitoring System (TCMS) for the first time in the open literature. Based on the Taguchi L9 orthogonal design principle, the basic machining parameters cutting speed (vc), feed rate (f) and depth of cut (ap) were adopted for the turning of AISI 5140 steel. For this purpose, an optimization approach was used implementing five different sensors, namely dynamometer, vibration, AE (Acoustic Emission), temperature and motor current sensors, to a lathe. In this context, VB, Ra and sensorial data were evaluated to observe the effects of machining parameters. After that, an RSM (Response Surface Methodology)-based optimization approach was applied to the measured variables. Cutting force (97.8%) represented the most reliable sensor data, followed by the AE (95.7%), temperature (92.9%), vibration (81.3%) and current (74.6%) sensors, respectively. RSM provided the optimum cutting conditions (at vc = 150 m/min, f = 0.09 mm/rev, ap = 1 mm) to obtain the best results for VB, Ra and the sensorial data, with a high success rate (82.5%).

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Section: Introductionmentioning

confidence: 99%

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Kuntoğlu

Aslan

Sağlam

et al. 2020

Sensors

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show abstract

“…Surface roughness is a feature of the outer form of the machined material and which can be controlled to obtain certain functional properties such as friction, thermal conductivity and oil retention [ 23 ]. The surface roughness value is commonly required to fall within a certain range based on the final application of the machined part [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Cutting Parameters and Tool Geometry for Multi-Criteria Optimization of Surface Roughness and Vibration via Response Surface Methodology in Turning of AISI 5140 Steel

et al. 2020

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AISI 5140 is a steel alloy used for manufacturing parts of medium speed and medium load such as gears and shafts mainly used in automotive applications. Parts made from AISI 5140 steel require machining processes such as turning and milling to achieve the final part shape. Limited research has been reported on the machining vibration and surface roughness during turning of AISI 5140 in the open literature. Therefore, the main aim of this paper is to conduct a systematic study to determine the optimum cutting conditions, analysis of vibration and surface roughness under different cutting speeds, feed rates and cutting edge angles using response surface methodology (RSM). Prediction models were developed and optimum turning parameters were obtained for averaged surface roughness (Ra) and three components of vibration (axial, radial and tangential) using RSM. The results demonstrated that the feed rate was the most affecting parameter in increasing the surface roughness (69.4%) and axial vibration (65.8%) while cutting edge angle and cutting speed were dominant on radial vibration (75.5%) and tangential vibration (64.7%), respectively. In order to obtain minimum vibration for all components and surface roughness, the optimum parameters were determined as Vc = 190 m/min, f = 0.06 mm/rev, κ = 60° with high reliability (composite desirability = 90.5%). A good agreement between predicted and measured values was obtained with the developed model to predict surface roughness and vibration during turning of AISI 5140 within a 10% error range.

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“…As HPWJA provides efficient lubrication of the tool/chip interface, the cutting temperature is drastically reduced. This effects surface integrity by inducing a softening effect of the machined surface [23,24]. A comparative study carried out by Da Silva et al [25] has shown that neither the use of a CBN tool nor the use of a ceramic tool gives satisfactory results when machining Ti-6Al4V using HPWJA.…”

Section: Introductionmentioning

confidence: 99%

High-pressure water-jet-assisted machining of Ti555-3 titanium alloy: investigation of tool wear mechanisms

Ayed

Germain

2018

Int J Adv Manuf Technol

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The main objective of this study is to investigate uncoated tungsten carbide tool wear mechanisms for high-pressure waterjet machining of the Ti555-3 titanium alloy. A comparative study has been undertaken (i.e. conventional versus assisted machining) based on numerous experimental tests. These tests have been accompanied by the measurement of the cutting forces and flank wear. It is concluded that the high-pressure water-jet assistance can greatly increase tool life compared to conventional machining, for all cutting conditions. The gain in tool life depends on the severity of the cutting condition. The analyses performed for each test (i.e. SEM, EDS and 3D profilometer) made it possible to monitor the tool wear and to investigate the main wear mechanisms. Based on these analyses, adhesion wear appears to be the most influential mechanism and it is accelerated by an increase in water-jet pressure. Monitoring of the wear profile made it possible to study the evolution of crater wear and material chipping during machining.

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Surface integrity and tool life when turning of Ti-6Al-4V with coolant applied by different methods

Cited by 20 publications

References 26 publications

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Modeling of Cutting Parameters and Tool Geometry for Multi-Criteria Optimization of Surface Roughness and Vibration via Response Surface Methodology in Turning of AISI 5140 Steel

High-pressure water-jet-assisted machining of Ti555-3 titanium alloy: investigation of tool wear mechanisms

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