An Investigation on Surface Roughness and Tool Wear in Turning Operation of Inconel 718

Gürgen, Selim; Tali, Dinçer; Kuşhan, Melih Cemal

doi:10.5028/jatm.v11.1030

Cited by 10 publications

(10 citation statements)

References 37 publications

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“…Literatures [2,[38][39][40] also studied the influence law of cutting parameters on surface roughness, and their research believed that reducing feed and cutting depth was conducive to reducing surface roughness, and feed was the most important factor affecting surface roughness, which was consistent with the research conclusion of this paper. As for the influence of cutting speed on the surface roughness, literatures [2] and [38] believe that the cutting speed has little influence on the surface roughness, and the surface roughness shows a decreasing trend when the cutting speed is increased, which is slightly different from the conclusion of this paper.…”

supporting

confidence: 85%

Research on surface roughness prediction in turning Inconel 718 based on Gaussian process regression

Hao¹,

Cheng²,

Fan³

2022

Phys. Scr.

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Nickel-based alloy Inconel 718 is widely used in aircraft engine industry because of its good mechanical properties. Inconel 718 is a typical difficult-to-machine material and its price is relatively expensive. Therefore, accurate prediction of Inconel 718 machined surface roughness with small sample space can improve machining efficiency, optimize process parameters and reduce machining cost. In this paper, a method is proposed to characterize the influence of cutting parameters on roughness by stablishing the corresponding relationship between the proportional hyperparameters in the multivariate kernel function and the cutting speed, cutting deep, feed rate and the rake angle of the tool. A multi input single output (MISO) multivariate Gaussian process regression（GPR）surface roughness prediction model with cutting speed, cutting depth, feed rate and tool rake angle as input variables and surface roughness as output variables is established. The model can not only output the predicted value of surface roughness, but also give the reliability of the predicted value. Experimental results show that the proportional hyperparameter has an independent adjustment function, and the influence of the process parameters characterized by the proportional hyperparameter on the surface roughness is consistent with the experimental results. The experimental results show that the average relative error of MISO multivariate GPR surface roughness prediction model proposed in this paper is 1.5%, which can accurately predict the surface roughness in small sample space.

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supporting

confidence: 85%

Research on surface roughness prediction in turning Inconel 718 based on Gaussian process regression

Hao¹,

Cheng²,

Fan³

2022

Phys. Scr.

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“…Polymer composite material has a not linear highly wear manner [2]. To improve wear rate, it is required to decrease adhesive force among its components [3], surface roughness is the important parameter effect on the accelerated wear by increasing it [4] and it is dependent on solution chemical type immersed it [5]. Fernanda Regina Voltarelli et al (2010), the evaluation of the brush simulation for the roughness of the surface of five composite materials, and the results showed that the surface roughness of the resin materials superimposed differently with food simulation solutions, depending on the immersion medium [6].…”

Section: Introductionmentioning

confidence: 99%

Chemical Immersion Effects on Wear Property of Epoxy Reinforced Copper Powder Composites

Hilal

2022

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In this research, the effect of chemical solutions: (sulfuric acid H2SO4, sodium hydroxide NaOH) at (5 M) and distilled water were studied on some mechanical properties. Especially the wear resistance by pin-on-disk and hardness properties for neat epoxy composite and (epoxy - copper powder) composite. That has been prepared by hand mixing (weight ratio = 20%). The samples were immersed in these chemical solutions for different periods (7, 14, and 30) days. Then the hardness by the Shore D method was measured before and after immersion of the samples in acidic, neutral, and alkali solutions. Whereas the hardness was measured by the Vickers method for the wear disk. Wear rate was measured for all samples. The results showed that the neat epoxy composite was more affected by the alkali solution, followed neutral solution, and then the acidic solution by increasing the radius of wear disk (3, 5, 7, 9, and 11) cm, t= 15 min. and the load 5 N. In contrast to the composite material that was more affected by the alkali solution than the acid, neutral solution.

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“…Up to now, many studies were performed to evaluate the effect of technological parameters and cutting conditions on the tool wear and machining surface roughness to improve the quality Engineering and reduce the cost and time of machining processes. These studies were performed with the normal cutting conditions and with different machining methods such as grinding [1], drilling [2], turning [3][4][5][6], milling [7][8][9][10], and so on. In machining processes, the tool wear and surface roughness in these were investigated and modeled following two directions that were theoretical modelling and experimental modelling.…”

Section: Introductionmentioning

confidence: 99%

Analysis of tool wear and surface roughness in high-speed milling process of aluminum alloy Al6061

et al. 2021

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In this study, the influence of cutting parameters and machining time on the tool wear and surface roughness was investigated in high-speed milling process of Al6061 using face carbide inserts. Taguchi experimental matrix (L9) was chosen to design and conduct the experimental research with three input parameters (feed rate, cutting speed, and axial depth of cut). Tool wear (VB) and surface roughness (Ra) after different machining strokes (after 10, 30, and 50 machining strokes) were selected as the output parameters. In almost cases of high-speed face milling process, the most significant factor that influenced on the tool wear was cutting speed (84.94 % after 10 machining strokes, 52.13 % after 30 machining strokes, and 68.58 % after 50 machining strokes), and the most significant factors that influenced on the surface roughness were depth of cut and feed rate (70.54 % after 10 machining strokes, 43.28 % after 30 machining strokes, and 30.97 % after 50 machining strokes for depth of cut. And 22.01 % after 10 machining strokes, 44.39 % after 30 machining strokes, and 66.58 % after 50 machining strokes for feed rate). Linear regression was the most suitable regression of VB and Ra with the determination coefficients (R2) from 88.00 % to 91.99 % for VB, and from 90.24 % to 96.84 % for Ra. These regression models were successfully verified by comparison between predicted and measured results of VB and Ra. Besides, the relationship of VB, Ra, and different machining strokes was also investigated and evaluated. Tool wear, surface roughness models, and their relationship that were found in this study can be used to improve the surface quality and reduce the tool wear in the high-speed face milling of aluminum alloy Al6061

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An Investigation on Surface Roughness and Tool Wear in Turning Operation of Inconel 718

Cited by 10 publications

References 37 publications

Research on surface roughness prediction in turning Inconel 718 based on Gaussian process regression

Research on surface roughness prediction in turning Inconel 718 based on Gaussian process regression

Chemical Immersion Effects on Wear Property of Epoxy Reinforced Copper Powder Composites

Analysis of tool wear and surface roughness in high-speed milling process of aluminum alloy Al6061

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