Optimization of the surface roughness in ball end milling of titanium alloy Ti-6Al-4V using the Taguchi Method

Mersni, Wael; Boujelbene, M.; Salem, Sahbi Ben; Alghamdi, A. S.

doi:10.1016/j.promfg.2018.02.040

Cited by 23 publications

(15 citation statements)

References 6 publications

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“…For these factor levels, the corresponding R a is 0.292 µm and R z is 1.943 µm (cut off λ C = 0.25 mm, λ S = 2.5 µm). The results from measurement and statistics meet the expectations and results of previous work: on one hand, a high cutting speed leads to better surfaces; on the other hand, the radial depth of cut is the most important parameter [14]. The influence of the depth of cut can be explained by the geometry of the milling cutter and the z-constant milling process.…”

Section: Millingsupporting

confidence: 82%

“…For milling in general and ball end milling in particular, several parameters have a decisive influence on the surface roughness of the component being machined. In previous studies, cutting speed v c , feed per tooth f z , and radial depth of cut a e have been proven to be the most important influencing parameters [14]. As this work deals with surface quality and not wear, tool life or productivity, only finishing is considered.…”

Section: Millingmentioning

confidence: 99%

“…The linear regression, which results according to formula (14), also reflects the insufficient predictability, since R 2 is only 73.04%. An adjustment of the regression model towards a higher R 2 is possible, but this does not change the physically difficult circumstances of the milling process:…”

Section: Millingmentioning

confidence: 99%

“…Design of experiments (DoE) using Taguchi's method narrows down the amount of experiments needed by using fractional factorial designs [13]. This method is widespread and common to achieve the best possible surface finish in milling processes [14] and has also been used for AM processes [15].…”

Section: Introductionmentioning

confidence: 99%

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Areal Surface Roughness Optimization of Maraging Steel Parts Produced by Hybrid Additive Manufacturing

2020

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We report on an experimental study and statistical optimization of the surface roughness using design of experiments and the Taguchi method for parts made of 1.2709 maraging steel. We employ a hybrid additive manufacturing approach that combines additive manufacturing by selective laser melting with subtractive manufacturing using milling in an automated process within a single machine. Input parameters such as laser power, scan speed, and hatching distance have been varied in order to improve surface quality of unmachined surfaces. Cutting speed, feed per tooth, and radial depth of cut have been varied to optimize surface roughness of the milled surfaces. The surfaces of the samples were characterized using 3D profilometry. Scan speed was determined as the most important parameter for non-machined surfaces; radial depth of cut was found to be the most significant parameter for milled surfaces. Areal surface roughness S a could be reduced by up to 40% for unmachined samples and by 23% for milled samples as compared to the prior state of the art.

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Section: Millingsupporting

confidence: 82%

Section: Millingmentioning

confidence: 99%

Section: Millingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Areal Surface Roughness Optimization of Maraging Steel Parts Produced by Hybrid Additive Manufacturing

2020

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“…Extensive research has been carried out on the superplastic deformation of titanium alloys, although there are insufficient available data related to the investigation of the deformation temperature (T d ) and strain rate ( . ε) effects on the flow behavior of titanium alloys at elevated temperatures using the analysis of variance technique, for example, the Taguchi method [32][33][34][35][36][37]. Babu et al [38] studied the effect of T d and .…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of the Effect of Temperature and Strain Rate on the Superplastic Deformation Behavior of Ti-Based Alloys in the (α+β) Temperature Field

Mosleh

Mikhaylovskaya

Котов

et al. 2018

Metals

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This paper presents the effect of temperature and strain rate on the superplastic deformation behavior of Ti-3%Mo-1%V-4%Al, Ti-4%V-6%Al, and Ti-1.8%Mn-2.5%Al alloys, which have different initial microstructures. The microstructure, before and after superplastic deformation in the deformation regimes that provided the maximum elongation, was analyzed. The deformation regimes, corresponding to the minimum strain hardening/softening effect, provided a higher elongation to failure due to their low tendency toward dynamic grain growth. As the values of stress became steady (σs), the elongation to failure and strain-hardening coefficient were analyzed under various temperature–strain rate deformation regimes. The analysis of variance of these values was performed to determine the most influential control parameter. The results showed that the strain rate was a more significant parameter than the temperature, with respect to the σs, for the investigated alloys. The most influential parameter, with both the elongation to failure and strain-hardening coefficient, was the temperature of the Ti-3%Mo-1%V-4%Al and Ti-1.8%Mn-2.5%Al alloys and the strain rate of the Ti-4%V-6%Al alloy.

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The Effect of High-Speed Milling on Surface Roughness of 42CrMo4 Hardened Steel Using a Ball Nose End-Mill Cutter

Sai

Belguith

Baili

et al. 2020

Lecture Notes in Mechanical Engineering

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Optimization of the surface roughness in ball end milling of titanium alloy Ti-6Al-4V using the Taguchi Method

Cited by 23 publications

References 6 publications

Areal Surface Roughness Optimization of Maraging Steel Parts Produced by Hybrid Additive Manufacturing

Areal Surface Roughness Optimization of Maraging Steel Parts Produced by Hybrid Additive Manufacturing

Experimental Investigation of the Effect of Temperature and Strain Rate on the Superplastic Deformation Behavior of Ti-Based Alloys in the (α+β) Temperature Field

The Effect of High-Speed Milling on Surface Roughness of 42CrMo4 Hardened Steel Using a Ball Nose End-Mill Cutter

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