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\title{Optimization of Turning Parameters using RSM During Turning of AISI H11 with Dimple Textured Uncoated Carbide Tool}
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\section[{INTRODUCTION}]{INTRODUCTION}\par
Developments in the metal cutting industry are driven by the manufacturer's need to expand the performance of parts used for various applications by maintaining the environmental legislation.\par
Extreme heat develops at cutting edge while machining "difficult-to-cut" materials, which is carried out by workpiece, tool, and coolant/lubricants. Traditionally, to dissipate heat large quantity of coolants (flooded coolants) is used to minimize friction and heat, which create difficulties in procurement, dumping, storage, and preservation. However, some disadvantages like cost, environmental impact, safety, and health hazards limit the use of flood cooling which compels industries to minimize or eliminate its use and encourage the development of new cooling/lubricating options. "Dry machining" is oldest, most environmentally friendly options, but absence of cutting fluid leads to heat generation, adhesion, poor chip evacuation, and increase friction at the cutting zone. Dry machining is found to lead to poor results with rough surface on products, whereas flood cooling leads to economic, environmental, and health challenges.\par
Worldwide manufacturers are actively seeking cost-effective alternatives to produce satisfactory quality components which are accepted globally.\par
To promote the metal cutting industry and creating new opportunities, technology plays an important role. Turning at one time is based on the skills of the operator and still important, so to be competitive it is necessary to adopt and implement modern and cost-efficient manufacturing techniques \hyperref[b32]{(Soroka, 2003)}.\par
Materials used extensively in aerospace, food, and the nuclear industry have great-strength and wear-resistance are "difficult-to-machine" due to thermo-physical stresses at the edge of cutting tool \hyperref[b29]{(Sharma, Dogra and Suri, 2008)}. The degradation of the surface finish is closely associated with wear growth. Different researchers working on enhancing tool life during conventional turning; otherwise, tool failure refers to the damage that is so severe to remove material from the workpiece due to its vulnerability to changes in cutting conditions \hyperref[b30]{(Sharma, Dogra and Suri, 2009)}. Tool-workpiece friction and heat generation at cutting area are prevalent challenges in turning, reducing life of tool and compromising the quality of surface during turning operation.\par
Due to unusual properties of "difficult-tomachine" materials, such as low conductivity to heat, great-strength at eminent temperatures, and wear-resistance and corrosion generate heat and produce negative effects \hyperref[b38]{(Zhang, Li and Wang, 2012)}. Enormous generation of heat (indicated in Fig.  {\ref 1}.1) at the cutting zone while turning "difficult-to-cut" materials, is due to plastic deformation because of chip formation and friction between the tool-work-piece and tool-chip. Dissipation of heat is dependent on geometry of tool, its heat conductivity, and cooling techniques (Shokrani, Dhokia and Newman, 2012). Surface topography moderation of cutting tools using textures has been an innovative/ecological method, which helps to improve tribological conditions of sliding surfaces relative to each other, and the addition of solid lubricant in the pockets generated on the tool makes dry machining environmental friendly (Sharma and Pandey, 2016).\par
Dry machining with surface texturing tends to be the most suitable solution for sustainable machining. But for hard materials, high-temperature gradient at cutting zone necessitates the use of traditional flood cooling, which makes the process harmful for ecological considerations \hyperref[b23]{(Mosarof et al., 2016)}. 
\section[{Dry Machining}]{Dry Machining}\par
Dry machining is an eco-friendly approach and it will be mandatory for the industries in the coming future to enforce regulations of environment sustainability for occupational safety and health standards (Klocke, F., \& Eisenblätter, 1997).\par
Dry turning/machining offers pollution-free atmosphere (or water); no swarf residues that are measured by decreased disposal and cleaning costs; no health hazards; no skin and allergy \hyperref[b33]{(Sreejith and Ngoi, 2000)}. Dry machining helps to create an environmentally friendly image, reduce costs, and improve work satisfaction in workers (Figure  {\ref 1}.2). The major issues during dry machining include overheating, enhanced abrasion, diffusion and oxidation of the tool material. However, a significant amount of heat inhibits near tolerances and its surface is subjected to metallurgical damage \hyperref[b7]{(Debnath, Reddy and Yi, 2014)}. 
\section[{London Journal of Engineering Research}]{London Journal of Engineering Research}\par
Dry hard turning of difficult-to-machine materials would not be feasible without the selection of appropriate tool material. Costly, CBN (cubic boron nitride) tools are widely applied to machine high hardness and thermal conductivity materials, since these are second toughest tool material after diamond. Various merits or demerits of dry hard turning are discussed in Table  {\ref 1}.1. The cost of the CBN tool is high, but tool wear is low in comparison to other tool materials \hyperref[b21]{(Kundrák et al., 2006)}. Cemented carbide tools have been developed over time and still to be primarily used to machine Ni-alloys, particularly Inconel-718.\par
Conversely, with the increase demand for high MRR and higher surface quality, machining at high speed was adopted and usage of cemented carbide tools became more complex \hyperref[b8]{(Dudzinski et al., 2004)}. For dry turning of Inconel-718, cutting tool material must meet the following requirements \hyperref[b8]{(Dudzinski et al., 2004}):  
\section[{Textures Engraved on Tool Surface}]{Textures Engraved on Tool Surface}\par
"Surface structuring or surface texturing on the cutting tool is an innovative strategy for sustainable metal cutting. Surface texturing is a term that refers to the process of altering the topography of a surface to enhance tribological efficiency of contact surfaces" \hyperref[b12]{(Fatima and Mativenga, 2013;}\hyperref[b13]{Ghosh and Rao, 2015)}. In the last decade, textures on surface has been the feasible option in surface engineering, leading to considerable increments in load carrying capacity, wear, strength, friction coefficient, etc. of tribo-mechanical components \hyperref[b11]{(Etsion, 2005)}. The surface texture reduces the friction coefficient, cutting forces and sticking between the tool-chip interface \hyperref[b15]{(Jianxin et al., 2012)}. Different researchers have graved various textures for this reason on tool inserts \hyperref[b13]{(Ghosh and Rao, 2015)}.\par
Metal cutting processes are now carried out at high speeds to ensure optimum efficiency, thanks to the advancement of modern engineering technologies. In the era of automation, long continuous chips produced at high cutting rates have become a challenge for industry. Textures on tool surface helps to control contact because their length of contact is shorter than conventional contact length of chip \hyperref[b2]{(Chao and Trigger, 1959)}.\par
Surface textures are generated by a variety of advanced manufacturing techniques, which include; micro-wire cut "electron discharge machining" (EDM), "Focused ion beam" (FIB) machining \hyperref[b34]{(Tseng, 2004)}, and photolithography, "reactive ion etching" method (RIE), laser technology, and so forth \hyperref[b26]{(Pettersson and Jacobson, 2003)}. Texture geometries such as micro-holes, linear, circular, square, and wavy indentations, were developed on tool surfaces and its application improves the cutting tool tribological performance \hyperref[b11]{(Etsion, 2005)}. 
\section[{II. LITERATURE}]{II. LITERATURE}\par
Nowadays, the tendency is toward high-quality, cheap, and small-batch sizes. To compete with countries having structure of low-wage in manufacturing industry, it is vital to innovate techniques that contribute to the improvement of the manufacturing sector's level, with a beginning to witness technical improvements in the field of hard turning \hyperref[b32]{(Soroka, 2003)}. Generally, when the hardness of material is more than 45 HRC, then it is called hard turning  {\ref (Pavel et}   When texture surface chip-tool friction is reduced and the wear debris that forms from this is confined in the cavity owing to textures on the surface \hyperref[b0]{(Blatter et al., 1999;}\hyperref[b6]{Costa and Hutchings, 2009)}. During the turning of materials hard to machine, a comparative examination of textured and conventional cutting tools was done. Textured tools utilized in the turning process, helps to reduce friction, heat generation, and thus wear, which helps to improve energy efficiency, product quality, and tool life \hyperref[b22]{(Ling et al., 2013)} The textures orientation at the cutting edge has been demonstrated to increase or decrease cutting performance, several studies have examined this in terms of sliding contacts. The tribological performance of parallel direction, perpendicular direction, and at a 45°angle to the sliding direction was tested \hyperref[b27]{(Pettersson and Jacobson, 2004)}; and found that orientation modification helps pressure accumulation due to textures; which disrupt surface roughness.\par
It is observed that perpendicular texture displayed more extraordinary film thickness under higher loads, whereas narrower parallel grooves exhibited a flow-direction because the lubricant may be directed away from contact in a parallel groove and so reduces the thickness of the film and tribology \hyperref[b5]{(Costa and Hutchings, 2007)}. The angle of textures was essential since they alter the lubricant's capacity to capture wear particles \hyperref[b37]{(Zhan and Yang, 2012)}. The foregoing tests show that textures must be oriented towards the sliding direction since it has a direct effect on the texture mechanism which promotes the tribological performance on a contact surfaces. For the building grooves in parallel on the rake tool face (known as textured tool), EDM has been used \hyperref[b17]{(Kim et al., 2015)} and evaluated its influence on the cuts in comparison with conventional tool. Some researchers have discriminate textures (Fig.  {\ref 2}.1,showing perpendicular shape, parallel shape, and cross patterns) to chip flow direction using femtosecond laser on WC inserts \hyperref[b16]{(Kawasegi et al., 2009)}. Wear resistance of tool is found to increased greatly in comparison to conventional insert while machining carbon steel with four kinds of textured tool because this micro-grooved surface collects the wear particles created by wear mechanisms (Enomoto and Sugihara, 2010).\par
Four varieties (perpendicular, parallel, square-shaped pits and dots) as depicted in Fig.  {\ref 2}.2 were produced in the same way. Micro-scale textures in orthogonal and diagonal sites showing that using rake and flank face grooves decreases tool wear as compared with ordinary tools \hyperref[b36]{(Xie et al., 2012)}. Diagonal micro-sites reduce wear of tool by 6.7\% and increase heat dissipation rate in the cutting are due to texture orientation to the chip flow direction.    Uncoated cemented carbide tool with texture on the rake face is utilized in comparison to costly CBN tools for turning "difficult-to-machine" materials. The tool holder (WTJNR1616H16) and cutting insert on clamping, gave approach angle (93°), clearance angle (6°), rake angle (-6°) and nose radius (0.80 mm). The dimensions of tool holder (in mm) are given in Table  {\ref 3}.4.  
\section[{III. EXPERIMENTAL SETUP}]{III. EXPERIMENTAL SETUP} 
\section[{Fabrication of Surface Texture on Rake Face (Dimple Shape)}]{Fabrication of Surface Texture on Rake Face (Dimple Shape)}\par
In general, the several "micro-machining" techniques for removing materials, such as 'micro-grinding', 'micro-EDM', 'femtosecond laser' and 'fibre laser' have been used.\par
Optimization of Turning Parameters using RSM During Turning of AISI H11 with Dimple Textured Uncoated Carbide Tool\par
Dimple-shaped texture with 50 µm diameter and 100 µm special distance, as illustrated in Fig.  {\ref 3}.1 a,b, is taken into consideration and is modelled using CAD software. The CAD model was then exported to appropriate interface for fabricating texture on tool rake surface. A "multidiode pump fibre laser" (LM-487-A-22-SD6-UX-M30-M) was applied on uncoated carbide tool's rake surface, the array of dimple texture is taken zigzag and the direction parallel to cutting edge was fabricated.\par
The conventional insert with non-textured and dimple-textured pictures was measured by optical microscope and is displayed in Fig.  {\ref 3}.1 a, b. 
\section[{Machine and Measuring Equipment}]{Machine and Measuring Equipment}\par
The turning runs were conducted on convention lathe HMT, India, model: high-speed precision lathe NH 22 and for tool flank wear and surface roughness measurement Metzer toolmaker's microscope Surf Test SJ-201 analyzer are utilized.\par
To measure the cutting forces during cutting a strain gauge based digital lathe dynamometer supplied by RMS controls, India was used during experimentation. The specifications of cutting force dynamometer is given in Table  {\ref 3}.5. "Scanning electron microscopy" was used to characterize the wear of worn-out cutting inserts.\par
The SEM/EDAX equipment is shown in Figure  {\ref 3}.6 with the following specifications, associated with two computer interface for wear and elemental analysis, respectively. 
\section[{London Journal of Engineering Research}]{London Journal of Engineering Research}\par
Optimization of Turning Parameters using RSM During Turning of AISI H11 with Dimple Textured Uncoated Carbide Tool 37  
\section[{IV. MODELING OF FLANK WEAR AND SURFACE ROUGHNESS}]{IV. MODELING OF FLANK WEAR AND SURFACE ROUGHNESS}\par
To generate models for response factors, i.e., tool flank wear and surface roughness, an entire set of 20 experiments obtained through RSM based on CCFC (central composite face-centered) design have been conducted. The input turning parameters viz. cutting speed, feed rate, and depth of cut were varied over 3 levels, whereas the tool geometry and texture on the rake surface were kept constant for each experiment. The new cutting edge of the insert was used to conduct each turning test. CCFC experimental layout, along with results obtained for output responses, i.e., tool flank wear (VB) and surface roughness (Ra), is shown in Table \hyperref[tab_17]{4}.1.\par
In order to build their respective models in terms of input factors, the output responses were examined using RSM. The quadratic model was presented the best fit for both output factors, whereas cubic models had been aliased, in Table \hyperref[tab_17]{4}.2 and Table \hyperref[tab_17]{4}.3.\par
The recommended quadratic models' R-Squared (R 2 ) values were the greatest (except the aliased one), while their associated PRESS values remain lowest, indicating quadratic model's superiority over other source models.\par
Optimization of Turning Parameters using RSM During Turning of AISI H11 with Dimple Textured Uncoated Carbide Tool Likewise,; R 2 or R-squared = 1-(residual sum of squares / total sum of squares) was higher than 0.85, for both models, which confirmed furthermore the accuracy of the models that were fitted with regard to the projection of capability of response parameters. The values of output responses that are predicted by the adjusted R 2 and R 2 indicate excellent interactions. In addition, adequate precision (AP: signal/noise ratio) > 4, shows the validity of the models for future predictions for both models. Furthermore, the lowest press values (predicted residuals error sum of squares): 0.2568 and 0.2107, shows quadratic models for output responses are best fit models for the test findings.  To create a connection between response variables and input turning factors, multiple regression analysis was used to produce output response (VB and Ra) mathematical models. Table \hyperref[tab_17]{4}.8 lists empirical models for output responses. To check the accuracy of developed models for VB and Ra, shown in Table  {\ref 8}.8, the values of VB and Ra have been calculated from these regression equations for a different combination of input parameters, as reported in Table \hyperref[tab_17]{4}.   Fig. \hyperref[fig_12]{4}.3 reported high wear at flan with higher feed rate/ speed. Initially, the tool edge was protected by hard particle and rake face textures, which limits chip tool contact, resulting in less tool flank wear, as the speed and feed rate rises, temperature between tool-chip interfaces becomes dominating factor. At high speed cutting, the friction rises due to uneven contact with a chip-tool, which causes the protective layer to be removed, i.e. adhesive wear. Furthermore, due to the high temperature during cutting, the bonds between tool particles are weakened by the severe diffusion between the work material and tool. The hard particles are thus removed from the tool and wear is thereby enhanced. The tool flank wear (VB) rises with an increase in feed rate, but above the mean value, the VB increases quickly. A reduction in tool flank wear was seen with a combination of low cutting speed and low feed.      to the effects of feed rate, as, at lower speed of cutting, Ra is higher, and as the speed increases, up to 130 m/min. values of Ra decreases. But with the further increment of speed of cutting does not have a noteworthy effect on Ra.\par
Whereas on other hand, the cuts depth (d) has a significant influence on roughness (Ra). At lower depth of cut (0.2 mm), surface roughness is nearly 2.23 µm, while with rise in cuts depth, roughness increases up to 2.98 µm. As illustrated in Fig. \hyperref[fig_12]{4}.7, the lowest roughness of surface was achieved by low depth of cut and speed of cutting.\par
For optimizing turning variables for AISI H11 multi-response optimization using desirability function was employed with uncoated carbide tool to achieve minimal tool flank wear (VB) and surface roughness (Ra) using textured tools. The numerical optimization algorithm seeks a set of factors levels that simultaneously satisfies the criteria placed on each response factor with highest combined desirability. The various constraints applied throughout the optimization process are described in Table \hyperref[tab_17]{4}.9. The cutting parameters were determined to be optimal as: cutting speed (Vc = 86.61 m/min.), feed rate (f = 0.16 mm/rev.) and depth of cut (d = 0.2 mm) with projected optimum value of VB = 1.82 µm and Ra = 1.79 µm at a desirability level of 0.967 (Table \hyperref[tab_17]{4}.10; Solution no.1).  \begin{figure}[htbp]
\noindent\textbf{11}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{12}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 1 . 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{21}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Figure 2 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{22}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Figure 2 . 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{141}\includegraphics[]{image-7.png}
\caption{\label{fig_5}1 .Figure 4 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{42}\includegraphics[]{image-8.png}
\caption{\label{fig_6}Figure 4 . 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{43}\includegraphics[]{image-9.png}
\caption{\label{fig_7}Figure 4 . 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-10.png}
\caption{\label{fig_8}London}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{44}\includegraphics[]{image-11.png}
\caption{\label{fig_9}Figure 4 . 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{45}\includegraphics[]{image-12.png}
\caption{\label{fig_10}Figure 4 . 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{46}\includegraphics[]{image-13.png}
\caption{\label{fig_11}Figure 4 . 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-14.png}
\caption{\label{fig_12}Figure 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{47}\includegraphics[]{image-15.png}
\caption{\label{fig_13}Figure 4 . 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-16.png}
\caption{\label{figure16}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{11} \par 
\begin{longtable}{P{0.85\textwidth}}
London Journal of Engineering Research\\
1. Adequate stability at high temperatures\\
2. Superior thermal shock resistance\\
3. Superior strength and toughness\\
4. High hot hardness\\
5. Excellent resistance to wear\end{longtable} \par
 
\begin{quote}
\par
31Optimization of Turning Parameters using RSM During Turning of AISI H11 with Dimple Textured Uncoated Carbide Tool\end{quote}

\caption{\label{tab_0}Table 1 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.1839231547017189\textwidth}P{0.0017189079878665318\textwidth}P{0.022345803842264915\textwidth}P{0.016329625884732052\textwidth}P{0.012891809908998988\textwidth}P{0.6127906976744185\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{texturing}\\
is\tabcellsep an\tabcellsep \multicolumn{2}{l}{environment-friendly}\tabcellsep method\tabcellsep for\\
\multicolumn{5}{l}{tribological characteristics improvement.}\\
\multicolumn{6}{l}{"Tribology is the study of friction, wear and}\\
\multicolumn{6}{l}{lubrication of the surfaces in relative motion" (Ian}\\
\multicolumn{2}{l}{Hutchings,}\tabcellsep 2017).\tabcellsep \multicolumn{3}{l}{Lubrication/Cooling}\\
\multicolumn{6}{l}{capabilities and tribological properties are}\\
\multicolumn{3}{l}{improved as observed}\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep al., no date). While\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep it may not remove the requirement of grinding, it\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep may relieve the load on costly grinders for the\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep specified application (König, Berktold and Koch,\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 1993; Chou, Evans and Barash, 2003).\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep During cutting of metal, there is maximum\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep conversion of energy into heat. Friction inside\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep tool-chip interface and in the workpiece-tool\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep interaction zone generates extra heat at cutting\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep region. The temperature due to friction and\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep plastic deformation at the cutting zone is reached\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep upto 1700°C. During machining 10-20\% heat is\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep taken up by tool and rest of 80-90\% is transferred\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep to chip (Budak et al., 2010). Heating the tool and\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep workpiece is excessive and also raises their\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep temperature and distortion in the cutting tool\end{longtable} \par
 
\begin{quote}
\par
\par
\par
\par
\hyperref[b24]{(Motorcu et al., 2016)}.To overcome these problems of dry machining, sustainable techniques are developed like; a new class of tools, texturing on rake surface of tool,London Journal of Engineering ResearchOptimization of Turning Parameters using RSM During Turning of AISI H11 with Dimple Textured Uncoated Carbide Tool improved tool geometry, and coatings are applied on tools. From all of the above, surface\end{quote}

\caption{\label{tab_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{31} \par 
\begin{longtable}{P{0.10365853658536585\textwidth}P{0.10365853658536585\textwidth}P{0.12439024390243901\textwidth}P{0.10365853658536585\textwidth}P{0.10365853658536585\textwidth}P{0.10365853658536585\textwidth}P{0.10365853658536585\textwidth}P{0.10365853658536585\textwidth}}
C\tabcellsep Si\tabcellsep Mn\tabcellsep P\tabcellsep S\tabcellsep Cr\tabcellsep Mo\tabcellsep V\\
0.33\tabcellsep 0.9\tabcellsep 0.29\tabcellsep 0.03\tabcellsep 0.02\tabcellsep 5.1\tabcellsep 1.2\tabcellsep 0.34\end{longtable} \par
 
\caption{\label{tab_3}Table 3 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{32} \par 
\begin{longtable}{P{0.2125\textwidth}P{0.09739583333333332\textwidth}P{0.11510416666666666\textwidth}P{0.08854166666666667\textwidth}P{0.11510416666666666\textwidth}P{0.09296875\textwidth}P{0.12838541666666667\textwidth}}
Tesile\tabcellsep Yield\tabcellsep Young\tabcellsep Density\tabcellsep Melting\tabcellsep Hardness,\tabcellsep Thermal\\
Strength\tabcellsep Strength\tabcellsep modulus\tabcellsep (g/cm 3 )\tabcellsep Range (°C)\tabcellsep Brinell\tabcellsep Conductivity\\
(MPa)\tabcellsep (MPa)\tabcellsep (MPa)\tabcellsep \tabcellsep \tabcellsep HB\tabcellsep (W/mK)\\
1590\tabcellsep 1380\tabcellsep 215 × 103\tabcellsep 8.19\tabcellsep 1345-1427\tabcellsep 195\tabcellsep 42.2\\
\multicolumn{2}{l}{3.2 Cutting Tool Material}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\begin{quote}
\par
\par
Cutting tool material selected for the turning tests was uncoated cemented carbide tool; ISO designation -TNMA 160408-THMF (provided by M/s Kennametal India Limited) having specifications shown in Table3.3. The name of the tool holder was WTJNR1616H16. It was used to rigidly mount the tool mentioned above and had a tool cutting edge angle of 93°.\end{quote}

\caption{\label{tab_4}Table 3 . 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{33} \par 
\begin{longtable}{P{0.18989361702127658\textwidth}P{0.18989361702127658\textwidth}P{0.2531914893617021\textwidth}P{0.06329787234042553\textwidth}P{0.045212765957446804\textwidth}P{0.10851063829787233\textwidth}}
Cutting\tabcellsep \tabcellsep \multicolumn{3}{l}{Insert dimensions (mm)}\tabcellsep No. of\\
Type\tabcellsep Insert Geometry\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
insert\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Edges\\
\tabcellsep L\tabcellsep IC\tabcellsep R e\tabcellsep S\tabcellsep \\
TNMA\tabcellsep 16.50\tabcellsep 9.52\tabcellsep 0.80\tabcellsep 4.76\tabcellsep 6\end{longtable} \par
 
\caption{\label{tab_5}Table 3 . 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{34} \par 
\begin{longtable}{P{0.3007692307692308\textwidth}P{0.03923076923076923\textwidth}P{0.27461538461538465\textwidth}P{0.06538461538461539\textwidth}P{0.06538461538461539\textwidth}P{0.05230769230769231\textwidth}P{0.05230769230769231\textwidth}}
Tool Holder\tabcellsep \tabcellsep \multicolumn{4}{l}{Dimensions (in mm)}\tabcellsep \\
WTJNR1616\tabcellsep H\tabcellsep B\tabcellsep LF\tabcellsep LH\tabcellsep HF\tabcellsep WF\\
H16\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 20\tabcellsep 20\tabcellsep 125\tabcellsep 125\tabcellsep 25\tabcellsep 25\end{longtable} \par
 
\caption{\label{tab_6}Table 3 . 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{35} \par 
\begin{longtable}{P{0.32041884816753924\textwidth}P{0.5295811518324608\textwidth}}
Make\tabcellsep RMS Controls, India\\
Axis\tabcellsep 3 axis-X, Y, Z\\
Sensor\tabcellsep 500.0 kgf\\
Range\tabcellsep 0.1 kgf\\
Resolution\tabcellsep 0.05 \% Full scale\\
Accuracy\tabcellsep 0.05 \%\\
Linearity\tabcellsep Front side push button switch\\
Zero/Tare\tabcellsep 230 V AC\\
Power supply\tabcellsep Table\\
Model\tabcellsep Top\end{longtable} \par
 
\caption{\label{tab_7}Table 3 . 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{36} \par 
\begin{longtable}{P{0.4375739644970414\textwidth}P{0.41242603550295853\textwidth}}
Make\tabcellsep JEOL, Japan\\
Model\tabcellsep JSM-6510LV\\
Maximum magnification\tabcellsep 5X to 300,000X\\
Resolution\tabcellsep 3 nm\\
Operating voltage\tabcellsep 0.5 KV to 30 KV\\
Maximum specimen\tabcellsep 150 mm diameter\\
Objective lens\tabcellsep Super conical\end{longtable} \par
 
\caption{\label{tab_8}Table 3 . 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{41} \par 
\begin{longtable}{P{0.06408730158730158\textwidth}P{0.1349206349206349\textwidth}P{0.16865079365079363\textwidth}P{0.15009920634920634\textwidth}P{0.15853174603174602\textwidth}P{0.17371031746031745\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Surface\\
\tabcellsep Cutting speed:\tabcellsep Feed rate: f\tabcellsep Depth of cut:\tabcellsep Flank wear: VB\tabcellsep \\
Exp. No\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep roughness: Ra\\
\tabcellsep Vc (m/min.)\tabcellsep (mm/rev)\tabcellsep d (mm)\tabcellsep (mm)\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep (µm)\\
1\tabcellsep 130\tabcellsep 0.24\tabcellsep 0.35\tabcellsep 2.81\tabcellsep 1.84\\
2\tabcellsep 180\tabcellsep 0.16\tabcellsep 0.2\tabcellsep 2.65\tabcellsep 1.78\\
3\tabcellsep 180\tabcellsep 0.32\tabcellsep 0.2\tabcellsep 3.11\tabcellsep 2.12\\
4\tabcellsep 80\tabcellsep 0.16\tabcellsep 0.2\tabcellsep 1.74\tabcellsep 1.86\\
5\tabcellsep 80\tabcellsep 0.32\tabcellsep 0.2\tabcellsep 2.19\tabcellsep 2.6\\
6\tabcellsep 80\tabcellsep 0.16\tabcellsep 0.5\tabcellsep 1.81\tabcellsep 2.23\\
7\tabcellsep 80\tabcellsep 0.24\tabcellsep 0.35\tabcellsep 2\tabcellsep 2.34\\
8\tabcellsep 80\tabcellsep 0.32\tabcellsep 0.5\tabcellsep 2.21\tabcellsep 2.98\\
9\tabcellsep 130\tabcellsep 0.24\tabcellsep 0.35\tabcellsep 2.54\tabcellsep 2.02\\
10\tabcellsep 130\tabcellsep 0.24\tabcellsep 0.5\tabcellsep 2.65\tabcellsep 2.21\\
11\tabcellsep 130\tabcellsep 0.24\tabcellsep 0.35\tabcellsep 2.77\tabcellsep 1.99\\
12\tabcellsep 130\tabcellsep 0.24\tabcellsep 0.35\tabcellsep 2.54\tabcellsep 2.18\\
13\tabcellsep 130\tabcellsep 0.24\tabcellsep 0.2\tabcellsep 2.64\tabcellsep 1.87\\
14\tabcellsep 130\tabcellsep 0.32\tabcellsep 0.35\tabcellsep 2.64\tabcellsep 2.24\\
15\tabcellsep 180\tabcellsep 0.16\tabcellsep 0.5\tabcellsep 2.97\tabcellsep 2.04\\
16\tabcellsep 130\tabcellsep 0.24\tabcellsep 0.35\tabcellsep 2.74\tabcellsep 1.94\\
17\tabcellsep 180\tabcellsep 0.24\tabcellsep 0.35\tabcellsep 3.07\tabcellsep 2.18\\
18\tabcellsep 130\tabcellsep 0.16\tabcellsep 0.35\tabcellsep 2.3\tabcellsep 1.78\\
19\tabcellsep 130\tabcellsep 0.24\tabcellsep 0.35\tabcellsep 2.62\tabcellsep 1.96\\
20\tabcellsep 180\tabcellsep 0.32\tabcellsep 0.5\tabcellsep 3.33\tabcellsep 2.44\end{longtable} \par
 
\caption{\label{tab_9}Table 4 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{42} \par 
\begin{longtable}{P{0.2528925619834711\textwidth}P{0.1896694214876033\textwidth}P{0.2458677685950413\textwidth}P{0.1615702479338843\textwidth}}
Source models\tabcellsep R 2\tabcellsep PRESS values\tabcellsep Remarks\\
Linear\tabcellsep 0.9281\tabcellsep 0.3621\tabcellsep \\
2FI\tabcellsep 0.9364\tabcellsep 0.6941\tabcellsep \\
Quadratic\tabcellsep 0.9707\tabcellsep 0.2568\tabcellsep Suggested\\
Cubic\tabcellsep 0.9743\tabcellsep 19.70\tabcellsep Aliased\end{longtable} \par
 
\caption{\label{tab_10}Table 4 . 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{43} \par 
\begin{longtable}{P{0.3879604672057502\textwidth}P{0.020619946091644203\textwidth}P{0.4238544474393531\textwidth}P{0.01756513926325247\textwidth}}
Source models\tabcellsep R 2\tabcellsep PRESS values\tabcellsep Remarks\\
Linear\tabcellsep 0.7169\tabcellsep 0.8139\tabcellsep \\
2FI\tabcellsep 0.7610\tabcellsep 2.06\tabcellsep \\
Quadratic\tabcellsep 0.9099\tabcellsep 0.2107\tabcellsep Suggested\\
Cubic\tabcellsep 0.9613\tabcellsep 3.27\tabcellsep Aliased\\
4.1 ANOVA Analysis\tabcellsep \tabcellsep \multicolumn{2}{l}{was determined that models for VB and Ra are}\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{fitted and significant statistically. ANOVA}\\
\multicolumn{2}{l}{The ANOVA test was used to determine the}\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{findings indicate that A, B, C, AB, AC, BC, A², B²,}\\
\multicolumn{2}{l}{statistical significance of models and input}\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{and C², are all statistically significant variables for}\\
\multicolumn{2}{l}{variables produced by RSM. Findings through}\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{both output responses. Lack of fit: F-test was used}\\
\multicolumn{2}{l}{ANOVA for VB and Ra models are shown in Table}\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{to determine how well the generated response}\\
\multicolumn{2}{l}{4.4 and Table 4.6 respectively. The importance of}\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{models fit the input data. F-values (i.e., the ratio}\\
\multicolumn{2}{l}{RSM models and input variables may be}\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{between mean square lack of fit and pure error}\\
\multicolumn{2}{l}{determined by examining their associated p-value}\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{mean square) for lack of fit test were found as}\\
\multicolumn{2}{l}{(Prob>F). If the p-value < 0.05 (Prob>F, at 95\%}\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{0.3969, and 0.2749 for VB and Ra models,}\\
\multicolumn{2}{l}{confidence level), the contribution of parameters}\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{respectively and their associated p-values (>0.05)}\\
\multicolumn{2}{l}{is considered significant. Using the ANOVA test, it}\tabcellsep \tabcellsep \end{longtable} \par
 
\begin{quote}
Optimization of Turning Parameters using RSM During Turning of AISI H11 with Dimple Textured Uncoated Carbide Tool\end{quote}

\caption{\label{tab_11}Table 4 . 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{44} \par 
\begin{longtable}{P{0.1985655737704918\textwidth}P{0.1550204918032787\textwidth}P{0.03483606557377049\textwidth}P{0.14631147540983605\textwidth}P{0.10973360655737704\textwidth}P{0.14805327868852458\textwidth}P{0.05747950819672131\textwidth}}
Source\tabcellsep Sum of\tabcellsep DF *\tabcellsep Mean\tabcellsep F value\tabcellsep p-value\tabcellsep Remarks\\
\tabcellsep Squares\tabcellsep \tabcellsep Square\tabcellsep \tabcellsep Prob>F\tabcellsep \\
Model\tabcellsep 3.27\tabcellsep 9\tabcellsep 0.3635\tabcellsep 36.76\tabcellsep < 0.0001\tabcellsep significant\\
A-Cutting speed (Vc)\tabcellsep 2.68\tabcellsep 1\tabcellsep 2.68\tabcellsep 271.31\tabcellsep < 0.0001\tabcellsep \\
B-Feed rate (f)\tabcellsep 0.404\tabcellsep 1\tabcellsep 0.404\tabcellsep 40.85\tabcellsep < 0.0001\tabcellsep \\
C-Depth of cut (d)\tabcellsep 0.041\tabcellsep 1\tabcellsep 0.041\tabcellsep 4.14\tabcellsep 0.0692\tabcellsep \\
AB\tabcellsep 0.0001\tabcellsep 1\tabcellsep 0.0001\tabcellsep 0.0114\tabcellsep 0.9172\tabcellsep \\
AC\tabcellsep 0.0253\tabcellsep 1\tabcellsep 0.0253\tabcellsep 2.56\tabcellsep 0.1407\tabcellsep \\
BC\tabcellsep 0.0028\tabcellsep 1\tabcellsep 0.0028\tabcellsep 0.2844\tabcellsep 0.6055\tabcellsep \\
A²\tabcellsep 0.0118\tabcellsep 1\tabcellsep 0.0118\tabcellsep 1.19\tabcellsep 0.3007\tabcellsep \\
B²\tabcellsep 0.0468\tabcellsep 1\tabcellsep 0.0468\tabcellsep 4.73\tabcellsep 0.0547\tabcellsep \\
C²\tabcellsep 0.0055\tabcellsep 1\tabcellsep 0.0055\tabcellsep 0.5517\tabcellsep 0.4747\tabcellsep \\
Residual\tabcellsep 0.0989\tabcellsep 10\tabcellsep 0.0099\tabcellsep \tabcellsep \tabcellsep \\
Lack of Fit\tabcellsep 0.0281\tabcellsep 5\tabcellsep 0.0056\tabcellsep 0.3969\tabcellsep 0.8333\tabcellsep not significant\\
Pure Error\tabcellsep 0.0708\tabcellsep 5\tabcellsep 0.0142\tabcellsep \tabcellsep \tabcellsep \\
Cor Total\tabcellsep 3.37\tabcellsep 19\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\begin{quote}
*Degrees of Freedom\end{quote}

\caption{\label{tab_12}Table 4 . 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{45} \par 
\begin{longtable}{P{0.23246346555323588\textwidth}P{0.1987473903966597\textwidth}P{0.014196242171189977\textwidth}P{0.05323590814196242\textwidth}P{0.2022964509394572\textwidth}P{0.07275574112734864\textwidth}P{0.07630480167014614\textwidth}}
Std. Deviation\tabcellsep 0.0994\tabcellsep \tabcellsep \tabcellsep R 2\tabcellsep \tabcellsep 0.9707\\
Mean\tabcellsep 2.57\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Adjusted R 2}\tabcellsep 0.9443\\
C.V. \%\tabcellsep 3.87\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Predicted R 2}\tabcellsep 0.9238\\
PRESS\tabcellsep 0.2568\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Adeq Precision}\tabcellsep 22.2693\\
\tabcellsep \multicolumn{5}{l}{Table 4.6: ANOVA Results for Surface Roughness (Ra)}\tabcellsep \\
Source\tabcellsep Sum of\tabcellsep DF *\tabcellsep Mean\tabcellsep F value\tabcellsep p-value\tabcellsep Remarks\\
\tabcellsep Squares\tabcellsep \tabcellsep Square\tabcellsep \tabcellsep Prob>F\tabcellsep \\
Model\tabcellsep 1.61142\tabcellsep 9\tabcellsep 0.179\tabcellsep 22.332\tabcellsep 0.00002\tabcellsep significant\\
A-Cutting speed (Vc)\tabcellsep 0.21025\tabcellsep 1\tabcellsep 0.210\tabcellsep 26.224\tabcellsep 0.00045\tabcellsep \\
B-Feed rate (f)\tabcellsep 0.72361\tabcellsep 1\tabcellsep 0.724\tabcellsep 90.253\tabcellsep 0.00000\tabcellsep \\
C-Depth of cut (d)\tabcellsep 0.27889\tabcellsep 1\tabcellsep 0.279\tabcellsep 34.785\tabcellsep 0.00015\tabcellsep \\
\multicolumn{2}{l}{© 2023 Great ] Britain Journals Press}\tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{| Volume 23 Issue 2 ?"? Compilation 1.0 |}\end{longtable} \par
 
\caption{\label{tab_13}Table 4 . 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{47} \par 
\begin{longtable}{P{0.21794871794871792\textwidth}P{0.1452991452991453\textwidth}P{0.3051282051282051\textwidth}P{0.18162393162393162\textwidth}}
Std. Deviation\tabcellsep 0.0895\tabcellsep R 2\tabcellsep 0.9526\\
Mean\tabcellsep 2.13\tabcellsep Adjusted R 2\tabcellsep 0.9099\\
C.V. \%\tabcellsep 4.20\tabcellsep Predicted R 2\tabcellsep 0.8755\\
PRESS\tabcellsep 0.2107\tabcellsep Adeq Precision\tabcellsep 19.6007\end{longtable} \par
 
\caption{\label{tab_14}Table 4 . 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{48} \par 
\begin{longtable}{P{0.019029850746268655\textwidth}P{0.04017412935323383\textwidth}P{0.6300995024875622\textwidth}P{0.05286069651741294\textwidth}P{0.07189054726368159\textwidth}P{0.035945273631840795\textwidth}}
\tabcellsep Response\tabcellsep \multicolumn{2}{l}{Empirical Model Equations (In Terms of Actual}\\
Sr. No.\tabcellsep \tabcellsep \\
\tabcellsep Factors\tabcellsep \tabcellsep Factors)\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{-0.650240 + 0.014767 * Vc + 12.96534 * f -1.55919 * d -}\\
1\tabcellsep VB\tabcellsep \multicolumn{2}{l}{0.000938 * Vc * f +0.007500 * Vc * d -1.56250 * f * d -}\\
\tabcellsep \tabcellsep 0.000026 * (Vc) 2\tabcellsep -20.38352 * (f) 2\tabcellsep + 1.97980 * (d) 2\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{2.09936 -0.021622 * Vc + 6.63144 * f + 0.571313 * d}\\
2\tabcellsep Ra\tabcellsep \multicolumn{2}{l}{-0.023438 * Vc * f -0.002833 * Vc * d + 0.729167 * f * d}\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{+ 0.000097 * (Vc) 2}\tabcellsep -0.994318 * (f) 2\tabcellsep + 1.05051 * (d) 2\end{longtable} \par
 
\caption{\label{tab_15}Table 4 . 8 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{49} \par 
\begin{longtable}{P{0.4325892857142857\textwidth}P{0.2011160714285714\textwidth}P{0.10625\textwidth}P{0.11004464285714287\textwidth}}
Constraints\tabcellsep Goal\tabcellsep Lower Limit\tabcellsep Upper Limit\\
Cutting Speed (m/min.)\tabcellsep is in range\tabcellsep 80\tabcellsep 180\\
Feed rate (mm/rev.)\tabcellsep is in range\tabcellsep 0.16\tabcellsep 0.32\\
Depth of cut (mm)\tabcellsep is in range\tabcellsep 0.2\tabcellsep 0.5\\
Flank wear, VB (µm)\tabcellsep minimize\tabcellsep 1.74\tabcellsep 3.33\\
Surface roughness, Ra (µm)\tabcellsep minimize\tabcellsep 1.78\tabcellsep 2.98\end{longtable} \par
 
\caption{\label{tab_16}Table 4 . 9 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.486154776299879\textwidth}P{0.05858524788391778\textwidth}P{0.05036275695284159\textwidth}P{0.2158403869407497\textwidth}P{0.03905683192261185\textwidth}}
S No. 1 2 3 4 5\tabcellsep Cutting Speed (m/min.) 86.616 87.054 87.573 85.628 88.012\tabcellsep Feed rate (mm/rev.) 0.160 0.160 0.160 0.160 0.160\tabcellsep 10: Optimization Solutions Depth of cut (mm) VB (µm) Ra (µm) Desirability Remarks 0.200 1.820 1.799 0.967 Selected 0.200 1.824 1.795 0.967 0.200 1.830 1.790 0.967 0.200 1.808 1.808 0.967 0.200 1.835 1.787 0.967\tabcellsep London Journal of Engineering Research\\
\multicolumn{2}{l}{4.4 Confirmation Tests}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{Confirmation tests (replicated thrice) have been}\tabcellsep \\
\multicolumn{4}{l}{carried out under the same experimental settings}\tabcellsep \\
\multicolumn{4}{l}{and tooling circumstances, with suggested}\tabcellsep \\
\multicolumn{4}{l}{optimal levels of machining variables. Table 4.11}\tabcellsep \\
\multicolumn{4}{l}{demonstrates the error between the expected}\tabcellsep \\
\multicolumn{4}{l}{response and the experimental results from}\tabcellsep \\
\multicolumn{4}{l}{validating the precision of the mathematical}\tabcellsep \\
\multicolumn{4}{l}{model created for both VB and Ra during the}\tabcellsep \\
\multicolumn{4}{l}{confirmatory tests within the 95\% prediction}\tabcellsep \\
\multicolumn{2}{l}{range, illustrated in Table 4.11.}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\begin{quote}
23 Optimization of Turning Parameters using RSM During Turning of AISI H11 with Dimple Textured Uncoated Carbide Tool 45 4.3 Multi-Response Optimization of Turning Parameters | | Volume 23 Issue 2 ?"? Compilation 1.0 © 2023 Great ] Britain Journals Press\end{quote}

\caption{\label{tab_17}Table 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.34124087591240876\textwidth}P{0.1551094890510949\textwidth}P{0.22335766423357664\textwidth}P{0.1302919708029197\textwidth}}
\tabcellsep RSM\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep Experimentally\tabcellsep Error\\
\tabcellsep Predicted\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep observed value\tabcellsep (\%)\\
\tabcellsep value\tabcellsep \tabcellsep \\
Tool flank wear (µm)\tabcellsep 1.82\tabcellsep 1.89\tabcellsep 3.84\%\\
Surface roughness (µm)\tabcellsep 1.79\tabcellsep 1.87\tabcellsep 4.47\%\\
V. CONCLUSION\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\begin{quote}
Optimization of Turning Parameters using RSM During Turning of AISI H11 with Dimple Textured Uncoated Carbide Tool\end{quote}

\caption{\label{tab_18}Table 4 .}\end{figure}
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