Effect of High Speed Machining on Surface Roughness Characteristics R a , R q , R Z

. Today's machining industries experience a variety of challenges, but achieving high quality in terms of dimensional accuracy, surface quality, and production rate is their key concern. An effort has been made in the current study to investigate the impact of HSM process parameters on different Surface Roughness characteristics Ra, Rq, and Rz. The trials were conducted on Ti-6Al-4V, a material that finds extensive use in the automotive, aerospace, and biomedical industries. Depth of cut, feed rate, cutting speed, and step over were taken into account as HSM inputs for the experiment. Similar to this, the output characteristics Surface roughness parameters Ra, Rq, and Rz were used. The trials were carried out using Taguchi's standard L9 Orthogonal Array, and the optimization of multi responses was carried out using Grey analysis. The most significant influences on the parameters of surface roughness were found to be cutting speed and depth of cut. A confirmation experiment was performed using the ideal set of parameters to validate the study, and the results were found to be in good agreement with those of the experiment. According to the findings, the HSM method produced a superior surface quality.


Introduction
High-speed machining is currently receiving a lot of attention from industry, and HSM is one of the most effective and cutting-edge techniques [1].Most manufacturing sectors employ high speed machining (HSM) to more accurately machine tougher materials.HSM is a fast evolving technology in the manufacture of aerospace, automotive, defense, and missile components, where a greater quantity of precise components are required than in any other sector [2].The HSM technique varies according on the material, and titanium alloys are one of them for efficient research and industry use [3].Due to the recent surge in hsm demand, more productivity, better quality items are created at the lowest possible cost.HSM is valued in the domain of particularly efficient machining as a result [4].According to the material being cut, the high-speed machining range is chosen [5].Characteristics of surface roughness have a significant impact on productivity as well [6].For the effective application of milling operations on titanium alloys, milling rates of 100 to 125 m/min are thought to be adequate in order to provide a high quality surface finish [7].The improved surface quality is the primary goal while selecting the machining conditions.Setting machining parameters heavily relies on operator knowledge as well as machining boundary tables provided by machine equipment makers [8].Because there are so many different configurable machining boundaries, it is challenging to employ the optimum machine components [7].The primary goal of the sector nowadays is to improve the surface quality while machining titanium alloys.This research article will examine many facets of the machinability of titanium alloys at a faster cutting speed.The experiment's results have been thoroughly analyzed, and its conclusion is also presented.

Literature Review
A significant element that influences the performance and dependability of components is their surface texture [9].Since it directly influences the materials' optical and mechanical qualities, roughness is an essential factor to consider when assessing the surfaces of different materials.Roughness affects a variety of qualities, including adhesion, fracture toughness, fatigue resistance, optical properties, fluid flow, heat transfer, wear, friction, lubrication, and corrosion [10].One of the most crucial elements in tribology, roughness may be used to assess the efficiency of a machining process.Contact techniques, where a stylus tip makes direct physical contact with the surface to be measured and a piezoelectric or inductive transducer transforms the vertical movement into an electrical signal, are frequently used to assess roughness [11].The average values of the ordinates from the mean line are referred to as the arithmetic average.The square root of the arithmetic mean of the values of the squares of the ordinates of the surface measured from a mean line is known as the root mean square value.Ten point height is the average difference, calculated from a line parallel to the mean line, between the five highest peaks and the five lowest troughs of the surface texture within the sample length [12].It is challenging to think of all the machine parameters as inputs since there will be more trials to conduct as the number of input parameters rises [13].Denichi Taguchi devised a design known as an orthogonal array (OA) to minimize the quantity of trials, their cost, and their duration [14].OA is used to identify the ideal circumstances for producing improved output and covers the whole parametric space with the least experimentation [14,15].Grey Relational Grade technique based on Taguchi has been presented for multi-response optimization issues.Systems where some information is available and some is uncertain are dealt with using the grey technique.In terms of the Grey relational grade, this strategy reduces the multi-objective problem to a single objective problem [15].Attempts have been made in the current study to investigate the impact of HSM machining settings on different Surface Roughness characteristics (Ra, Rq and Rz).Cutting speed, depth of cut, feed rate, and step over were taken into consideration as experimentation's factors.Similar to this, output responses for Surface Roughness Parameters Ra, Rq, and Rz were used.On a VMC setup, the experiment was carried out using Taguchi's L9 orthogonal array.Grey approach based on Taguchi has been applied to optimize multi replies.Using ANOVA, the impact of the machining settings was investigated.

Materials and Methods
Ti alloy specimens were machined using inserts and wet cooling medium.Ti-6Al-4V specimens were machined using a VMC machine and wet cooling techniques.Experiments are designed to determine how machining variables impact response.The design considers octagonal inserts to investigate how inserts affect reaction.Wet cooling mediums were tested in the HSM technique to determine their effects.Table 1 lists the process parameters and their corresponding levels.As a coolant in HSM, high flash point milling machine oil is utilised to remove debris and heat from machined surfaces.The most frequent definition of surface roughness (SR) is the change in height of the surface in relation to a reference plane.The International Standardization Organization (ISO) and the American National Standards Institute (ANSI) often define it (ISO).The three most often utilised surface roughness characteristics are Rq (geometric average roughness), Ra (arithmetic average roughness), and Rz (ten point height).The formulas will be used to calculate the values of Ra, Rq, and Rz [16].
Where m represents the total number of deviations and Xj represents the deviation value.
Rq =√ Where m is the total number of deviations and Xj is the deviation value.
Where the ith highest peak (Rpi) and the ith lowest valley (Rvi) are both present.
Signal to noise ratio (S/N) is typically used in the classic Taguchi technique to process parameter optimization.Closer to the ideal process settings are those with a higher signal to noise ratio.The Taguchi technique can only optimize a single answer; if there are several responses, it cannot optimize them all at once.In order to handle multi-response optimization  Higher GRG offers an ideal set of reaction characteristics.Based on the grey theory response quality measure, the literatures provide three types of data normalization: "lower the better," "higher the better," and "normal the better" [17].The "lower is better" criteria is followed by SR.The most prominent GRG will be analyzed as the best parametric combination.
Surface roughness a determines the "lower the better" condition as follows: Where a = 1, 2, 3, and 4 for the various yield responses taken into account in a grouping, min Zi (a) is the lowest estimate of Zi for the ith reaction, and max Zi (a) is the greatest estimate of Zi for the ith reaction.GRC is aimed to provide a link between the most accurate information and the unmistakably standardized information.Table 3 illustrates the data after it has been standardized for the "dark social age." When calculating the GRC, take into account: S0, i (a) = βmin +ⱷβmax β 0,i (a) +ⱷβmax (5) The GRG may be determined as: Where x stands for the quantity of yield responses.The following parameters will be more closely matched to the ideal layout the higher the GRG number.

Result and Discussion
The SR for each experiment is shown in Tables 3 based on the results of the Surface Roughness Test.The mean data plot for SR is shown in Fig. 2 along with the interactions between the process parameters.This approach is the most suitable to evaluate the impact of machining variables on SR. with velocity and yields a low Ra.In Figures 3 and 4, the interactions between the process parameters are displayed together with the mean data plot for SR.
Figure 3:-An interaction between process parameters and the Rq means plot.
Figure 4:-An interaction between process parameters and the Rz means plot.
The best method for examining the effects of machining settings on surface roughness is the one described here.Higher cutting depths and lower minimum cutting speeds show  (2.441 μm).This is because the movement has a lower impact velocity and higher stepover value, which results in a lower Rq (0.658 μm).To create the Yi (a) matrix in GRA, the results of trial runs were employed.Alternative experiment runs are referred to in this as 1, 2, 3, etc.As a criteria, SR is equal to 1 4 1 2 3.... x.The deviation sequence is computed for the response measure (Table 3).
Experiment number 3 correlates to the experiment with the greatest GRG value, as can be seen in Figure 5, which shows a plot between experiment number and GRG (best rank).The relative error between the experimental data and the results of the confirmation test for HSM machining condition Ra, Rq, Rz is 5.48%, 5.28%, and 7.08%, respectively.Improvements in output responses were seen throughout the confirmation test.

Conclusions
The Ti-6Al-4V surface that was HSM-machined is investigated using different ra, rq, and rz.Approaches to GRA optimization are examined.The results of the aforementioned trials and optimization techniques led to the findings that are presented below.
• The optimum result that corresponds to choice 1 was supported by both Taguchi techniques and Grey connection analysis.• The GRA methodology was shown to be more efficient than the other optimization techniques that were investigated.• When Ti-alloy was machined using a HSM, Ra (0.575 µm), Rq (6.58 µm) and Rz (2.441 µm) were attained at the lowest possible value for each process parameter, and they decreased as the depth of cut and stepover grew.• For HSM conditions, factor levels Vf1F2DoC3So3 for GRA were obtained.Max-Min research demonstrates that cutting speed and stepover have a substantial impact on GRA.For Ra,Rq and Rz respectively, a confirmation test conducted on the optimal factor level process parameters yields errors of 5.48%, 5.28%, 7.08% .

Figure 2 :
Figure 2:-An interaction between process parameters and the Ra means plot.Slower cutting speeds with the deepest cut indicate a lower Ra (.575μm).This is brought on by the movement of medium feed rate and greater stepover value, which impacts /doi.org/10.1051/e3sconf/202343001271271 430

Table No
By using Taguchi's L9 experimental design technique, it was possible to use resources more effectively and reduce machining costs by doing fewer experiments.Analysis of the response parameters is done following HSM machining.

Table 2 :
-High speed machining under wet cooling medium

Table 6
displays the findings of the confirmation test conducted on the process parameters (Vf1F2DoC3So3).

Table 6 :
-Confirmation test and % error