Optimization of process parameters for high speed machining of Ti-6Al-4V using response surface methodology

Hashmi, Khalid Hussain; Zakria, Ghulam; Raza, Muhammad Baqar; Khalil, Shahid

doi:10.1007/s00170-015-8057-3

Cited by 64 publications

(28 citation statements)

References 20 publications

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“…In addition, it also helps in obtaining the optimal parametric setting capable of predicting desirable values of output response. 29,33,34 The present paper focuses on a second-order statistical model which indicates the relationship between the output response`y' and independent variables f 1 , 2 ; . .…”

Section: Response Surface Methodologymentioning

confidence: 99%

3d Finite Element Modeling for Estimating Key Machinability Aspects in Turning of Commercially Pure Titanium

Khan

Maity

2019

Surf. Rev. Lett.

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The present work aims at numerical approximation in combination with experimental validation of some of the important performance measures in turning of commercially pure titanium (CP-Ti) with uncoated carbide inserts. A three-dimensional (3D) finite element model was developed based on Lagrangian criterion. Simulation of the turning operation was performed using DEFORM 3D software in order to approximate the responses viz. feed force ([Formula: see text], radial force ([Formula: see text], tangential force ([Formula: see text], flank wear (Vb) and machining temperature ([Formula: see text]. Usui’s tool wear model was used to predict the flank wear. Morphology of the free and back surfaces of the chips was examined under a field emission electron microscope (FESEM). Turning experiments were carried out on a heavy duty lathe equipped with a 3D dynamometer. Secondly, a quadratic model was acquired for all the aforementioned quality characteristics using response surface methodology (RSM). Analysis of variance (ANOVA) test was performed to confirm the adequacy of the developed quadratic model. The results obtained from simulation and quadratic model were compared with the experimental data sets. Finally, an error analysis was done to determine the percentage inaccuracy of both the models. The percentage error for all the turning responses, was observed within 6% which showed the satisfactoriness of the proposed approximation tools. However, the simulation model exhibited lower prediction error when compared with the quadratic counterpart.

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Section: Response Surface Methodologymentioning

confidence: 99%

3d Finite Element Modeling for Estimating Key Machinability Aspects in Turning of Commercially Pure Titanium

Khan

Maity

2019

Surf. Rev. Lett.

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“…The results indicated that the depth of cut has the minimum effect on surfaces roughness compared to cutting speed and feed rate while the tool wear is statistically affected. Hashmi et al [25] studied the single objective optimization of surface roughness using response surface methodology. The predicted model suggested that the depth of cut is the most critical parameter that affects the surface roughness in the machining process.…”

Section: Introductionmentioning

confidence: 99%

Multi-Response Optimization of Face Milling Performance Considering Tool Path Strategies in Machining of Al-2024

et al. 2019

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It is hypothesized that the orientation of tool maneuvering in the milling process defines the quality of machining. In that respect, here, the influence of different path strategies of the tool in face milling is investigated, and subsequently, the best strategy is identified following systematic optimization. The surface roughness, material removal rate and cutting time are considered as key responses, whereas the cutting speed, feed rate and depth of cut were considered as inputs (quantitative factors) beside the tool path strategy (qualitative factor) for the material Al 2024 with a torus end mill. The experimental plan, i.e., 27 runs were determined by using the Taguchi design approach. In addition, the analysis of variance is conducted to statistically identify the effects of parameters. The optimal values of process parameters have been evaluated based on Taguchi-grey relational analysis, and the reliability of this analysis has been verified with the confirmation test. It was found that the tool path strategy has a significant influence on the end outcomes of face milling. As such, the surface topography respective to different cutter path strategies and the optimal cutting strategy is discussed in detail.

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“…In recent years, superfinishing has received special attention due to its higher material removal rate (MRR), better part quality, and relatively low manufacturing costs [3]. To date, the superfinishing of simple profile workpieces with hard alloys has had a more mature process and equipment [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

The Grey Theory Combining the Taguchi Method for the Best Parameters: A Case Study of Polishing M300 Steel

Yang

Tong

et al. 2019

Mathematical Problems in Engineering

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M300 steel, as high-chromium alloy steel with strong wear resistance and corrosion resistance, is widely used in the manufacture of complex profile molds and aerospace military equipment such as missile parts. However, there are still some problems such as the contradiction between productivity and surface quality in the polishing process for M300 steel. Therefore, in order to solve these problems, surface polishing experiments on M300 steel, single-factor and orthogonal experiments, and parameters’ optimization were studied. In this paper, orthogonal experiments are conducted for four selected machining parameters: grain size (A), grinding speed (B), cutting depth (C), and feed rate (D) on a grinding machine. The experiment and parameters’ optimization of the ball type abrasive tool polishing M300 were investigated by a five-axis machining center, electronic analytical balance, and three-dimensional surface topographer, and the optimal process parameters and preferred intervals were optimized. The optimal parametric condition obtained for simultaneous minimization of surface roughness (Ra) and maximization of material removal rate (MRR) is as follows: grain size=#320, grinding speed=4500 r/min, cutting depth=0.4 mm, and feed rate=80 mm/s. The above parametric combination has been validated by confirmatory tests.

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Optimization of process parameters for high speed machining of Ti-6Al-4V using response surface methodology

Cited by 64 publications

References 20 publications

3d Finite Element Modeling for Estimating Key Machinability Aspects in Turning of Commercially Pure Titanium

3d Finite Element Modeling for Estimating Key Machinability Aspects in Turning of Commercially Pure Titanium

Multi-Response Optimization of Face Milling Performance Considering Tool Path Strategies in Machining of Al-2024

The Grey Theory Combining the Taguchi Method for the Best Parameters: A Case Study of Polishing M300 Steel

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