Experimental analysis on incoloy 800H superalloy using cryo treated textured inserts with vegetable oil enriched by ZnO nanoparticles

ABSTRACT

In this research an effort is made to investigate the machining of Incoloy 800H Super alloy which is hard to machine material due to its rapid hardening property. Laser textured cryogenically treated cutting inserts and vegetable oil enriched with ZnO nano particles through Minimum Quantity Lubrication (MQL) was used. Turning experiments were performed based on L₂₇ Orthogonal Array (OA). Cutting speed, feed rate, and depth of cut were the input parameters. Surface roughness, cutting force, micro hardness, tool-tip temperature, power and material removal rate were the responses measured. Taguchi based Grey Relational Analysis and Analysis of Variance were used to optimize and find the most influencing parameter. The results revealed that the optimal machining conditions were cutting speed at 35 m/min, feed rate at 0.06 mm/rev and depth of cut at 0.5 mm for the multiple-characteristic performances and it was improved by 4.87%. Microhardness, Tool-tip temperature and Power were reduced during machining to 1.31%, 4.53% and 2.10% respectively. Moreover, ZnO nanoparticles enriched base fluid gives better performances in terms of surface roughness, tool wear and cutting force. Furthermore, the White Light Interferometer (WLI) and Scanning Electron Microscopy (SEM) were used to study the machined surface topography and tool wear analysis.

Keywords:
Incoloy 800H; Cutting Force; Micro Hardness; Tool Tip Temperature; Power; Material Removal Rate; GRA

1. INTRODUCTION

When creep resistance and rupture strength are needed at temperatures higher than 1100° F, Incoloy 800H and 800HT are typically utilised. The nickel steel alloy’s exceptional resistance to oxidation, carburisation, and nitriding environments is made possible by its chemical balance. Chemical and petrochemical processes, Heat treating equipment, nuclear power stations, and the paper pulp industries are just a few of the many applications for alloys 800H that expose them to high temperatures and corrosive environments [¹, ²]. Due to the presence of nickel the hardness of the alloy increases due to a phenomenon called rapid hardening. This happens in most of the nickel based super alloys which is a result of temperature increase. As the temperature increases nickel tends to create a hard covering over the machined area which leads to the difficulty in machining of the alloy. Degree of work hardening is the percentage by which the hardness at the surface is increased when compared to bulk material [³, ⁴, ⁵]. Machining of hard materials under the dry environments leads to poor surface finish quality and the cost of machining is increased. Moreover, the metal cutting industries are moving to environmental friendly engineering techniques. Cutting coolants are required to overcome all the aforementioned difficulties but due to the chemical contaminants many other regulations of the usage of conventional coolants is not encouraged [⁶, ⁷, ⁸]. During machining hard to machine materials high cutting force is essential and that increases the power consumption and vibration that results in dimensional deviation and improper surface finish. The variation in tool geometry and increased temperature at cutting region increases the thermal load on the workpiece that degrades the mechanical and metallurgical properties. Hence there is a possibility of rapid tool wear and the tool life is short that affects the productivity and quality of the process [⁹, ¹⁰]. Strain hardening effect is a phenomenon where plastic deformation occurs during machining, it increases the hardness and strength of the workpiece material. Strain hardening occurs due to the increased density of dislocations within the material’s crystal structure while machining [¹¹].

During machining precipitation hardened stainless steel the performance of MQL, dry, flood and cryogenic cooling methods were compared. According to the statement, cryogenic machining performs better in terms of reduced subsurface microhardness, power consumption, and tool wear. It was stated that better surface finish was obtained during flood cooling machining when compared with other cutting fluids [¹², ¹³]. During machining of Ti–6Al–4V material the cutting forces, tool wear and surface integrity were all undoubtedly impacted by the cooling method. The lower cutting force, good surface finish and lower tool wear were identified in cryogenic machining when compared with other lubrication and cooling techniques [¹⁴, ¹⁵]. The machining performances of high thermal conductivity steel were studied and compared with Dry, MQL and supercritical CO₂ cryogenic machining under lubricant conditions. It was claimed that, in comparison to dry and MQL conditions, the application of supercritical CO₂ cryogenic and lubricant increased tool life [¹⁶]. Besides, Viscosity of the fluid was determined as the most dominant fluid property on droplet size, representing a direct connection by enlarged droplet diameter. The lowest surface roughness values obtained with MQL were obtained using higher flow rates of lubricant [¹⁷, ¹⁸]. In addition to being environmentally friendly, dry machining increases the friction and adhesiveness of the tool-chip interface. A novel and environmentally friendly technique for enhancing heat dissipation in the cutting area is cutting tool surface texturing. The tool insert’s surface texturing will change the area where the cutting edge and the workpiece surface come into contact during milling [¹⁹]. Under various cooling conditions, the impact of dimple texture on the rake face of the uncoated carbide insert during Inconel 718 machining was investigated. When compared to alternative cooling conditions, it was studied that hBN cooling with a textured tool under MQL on the rake surface decreased tool wear, surface roughness, and cutting temperature by 28.27%, 32.26%, and 21.36%, respectively [²⁰, ²¹]. According to a study, the coolant creates harmful chemical reactions during high-temperature machining, including a formaldehyde molecule that improves fluidity and chemical stability and sodium nitrite, an anti-rust agent [²²]. Using dimple-textured tool inserts fabricated using laser, the machining performance of Ti6Al4V alloy was investigated in a variety of machining conditions, including dry and MQL. According to the results, while using MQL and textured tools for machining, there was a decrease in cutting force, apparent friction coefficient, tool wear contact length, and chips staying on the rake face [²³, ²⁴].

The performance of various cutting fluids such as MQL, LN₂ and MQL + LN₂, on tool wear, surface roughness, and cutting force during machining CGI and grey cast iron (GCI) were studied. The results revealed that the cutting tool life has increased and the cutting force has reduced while using cutting fluid and MQL + LN₂ during machining [²⁵, ²⁶]. In order to study burr development, tool wear, surface topography, and chip formation, experiments have been conducted with cutting speed and chip load with little lubrication. The study revealed that surface finish with smoother surface with reduced peak to-valley height; flank wear and elevated chip breaking were observed at higher cutting speed [²⁷]. Eco-friendly vegetable oil Rice Bran oil (RBO with and without the addition of zinc oxide (ZnO) nanoparticles) was used as a lubricant to study the machining performance of AISI D3 steel. Cutting forces, cutting temperature, surface roughness, flank wear were the output measures considered. It was observed that ZnO nano particles-enriched base fluid performed better by than pure oil in reducing the cutting fore, surface roughness and tool wear during machining [²⁸, ²⁹, ³⁰]. When turning AISI 304 stainless steel work material under various cooling circumstances, including base fluid, Al-Multi-Walled Carbon Nanotubes, and Al₂O₃ based nanofluid, machinability parameters including cutting force and surface roughness were assessed. It was claimed that the hybrid nanofluid performance was noticeably better than that of the base fluid doped with Al₂O₃ nanoparticles [³¹, ³²]. Alumina particles with multi walled carbon nano tubes were used during machining Inconel 718 to enhance the machinability properties. The results stated that the use of nano fluids reduced the cutting force and higher shear angle were observed during machining [³³]. Experiments on turning were conducted on AISI 9310 and AISI 420 steel to find the performance of castor oil and food grade vegetable oil under MQL conditions. It was contrasted with dry cooling and flood conditions. It was said that the tool wear was less and the surface finish were better with MQL lubricating conditions [³⁴, ³⁵]. Previous researches states that feed rate and speed are the most influencing parameter that affects the machinability and it depends on the high fracture toughness, high strain hardening rate and low thermal conductivity of the machined materials [³⁶, ³⁷, ³⁸]. Comparison of Machinability studies with line groove and plain cutting inserts were studied. It was revealed that the frictional effect in between the tool and chip interface were less while machining with grooved inserts than the plain cutting inserts [³⁹].

In a study of machinability of stainless steel with micro-grooved textured tool inserts, it was revealed that micro grooved inserts enhanced the tribological properties due to the reduced friction and increased lubrication effect in between tool chip interface [⁴⁰]. In an investigation of dry machining with textured and plain conventional tool inserts, it was stated that increased stability and efficiency were observed during machining with textured cutting inserts. It was also stated that the enhancement in performance was due to the lubrication film and reduced tool-chip contact length [⁴¹, ⁴²]. It was observed that dimple-shaped textured inserts reduced the crater wear during machining. It was also stated that the dimple shaped textured inserts acts as a mini reservoir during wet machining that improves the tribological and wear properties with enhanced performance [⁴³]. The performance and comparison of different types textured tool inserts with conventional inserts was investigated. Laser source was used to fabricate the textures on the rake face of the cutting tool inserts. The results revealed that the textured inserts performed better than the conventional plain inserts and it was also stated that the introduction of hydrodynamic force reduced the tool-chip contact length [⁴⁴, ⁴⁵].

Investigation was done to find the performance of different types of textured inserts (parallel, vertical, elliptical) on dry machining. The result observed that vertical direction textured inserts performed better than the other inserts due to the better chip flow characteristics during machining [⁴⁶]. Dry turning process on titanium alloy using cryogenically treated carbide inserts was conducted. Machining performance was compared using treated and untreated inserts. It was observed that cryogenically treated inserts have uniform microstructure, improved hardness, lower thermal conductivity and better wear resistance [⁴⁷, ⁴⁸].

It is summarized that there is a lack of literatures available to correlate the machinability of Incoloy 800H Superalloy with cryo treated textured tool inserts assisted with different types of MQL methods which needs attention. In this current research a novel method has been used to study the machinability analysis of cryo treated laser textured inserts with vegetable oil enriched with Nano Particle mixture [Nano fluid MQL] on Incoloy 800H Superalloy to examine the Surface Roughness (SR), Cutting Force (Fz), Micro Hardness (MHD), Tool-Tip Temperature (TT), Power (P) and Material Removal Rate (MRR). This work focuses on the significance of selecting the suitable machining parameters and nano fluid with MQL in machining of Incoloy 800H Superalloy considering all the machining parameters and factors on machinability and to optimise the machining parameters for the multi-performance characteristics. ANOVA analysis has been used to find the most influential machining parameter on the output responses.

2. MATERIALS AND METHODS

Turning experiments were conducted in a dry environment using Incoloy 800H superalloy materials with a length of 125 mm and a diameter of 30 mm. The chemical elements of this material measured (in wt %) are: 45.26 Fe, 31.59 Ni, 20.42 Cr, 0.76 Mn, 0.57 Ti, 0.50 Al, 0.42 Cu, 0.13 Si, 0.069 C, 0.014 P and 0.001 S; were determined by spectroscopy analysis done in research Lab of Test point company, at Coimbatore, India. Turning experiments were conducted in an FANUC control LEADWEL T5 CNC turning center. The L₂₇ Orthogonal Arrays (OA) was chosen to design and conduct the turning experiments. First, second, and fifth columns of L₂₇ (3¹³) standard OA were owed to Cutting speed (A = V_c as 35, 45 and 55m/min), feed rate (B = f as 0.02, 0.04 and 0.06 mm/rev) and depth of cut (C = ap as 0.5, 0.75 and 1mm) respectively were the factors and levels considered; based on the linear graph and remaining columns for their interaction effects. For this experimental study, an uncoated Tungsten carbide tool inserts CNMG 120408 with the tool signature of clearance angle = 5°, side rake angle = –6°, inclination angle = –6°, approach angle = 95°, Point angle = 80°, nose radius = 0.8 mm, and a PCLNL 1616 M12 tool holder with general specifications were utilized. The design was created as permanent prints on the inserts rake face using a laser marking machine (Meeras Laser Solutions, Chennai). The width, depth and pitch of the texture were cut based on the previous literatures as 200 µm, 100 µm and 200 µm respectively [¹⁵, ⁴²]. The uncoated textured tungsten carbide inserts were subjected to deep cryogenic treatment in a cryogenic chamber. The details of the cryogenic treatment and tempering process applied to the inserts are shown in Table 1.

Table 1
Cryogenic treatment and tempering process applied.

SAMPLE	TREATMENT	TEMPERATURE (oC)	TIME (HRS.)
Tungsten Carbide insert	Cryogenic treatment	−196	24
	Tempering	200	2

After cryogenic treatment the inserts were warmed in air to reach room temperature and is subjected to tempering process followed by air cooling. Cutting inserts hardness was measured after cryogenic and tempering of inserts using Vickers Microhardness tester with an applied load of 500g with dwell period of 10 seconds. It was observed that the hardness of the cutting inserts was increased to 3.8–4.17%, due the effect of very finer and refined grain size. Here rice bran oil was selected as a base fluid due to its various health benefits such as serum cholesterol, reduced cancer risk, skin softening property and will not affect the machine operators. Moreover, due to its naturally existing antioxidants the performance of cost is rice bran oil is less and has good stability. The nanofluid was prepared by the addition of ZnO nanoparticles to the Vegetable oil with a 0.1% concentration. The technical data sheet of ZnO nano particles are given in Table 2. In order to prepare a proper highly homogeneous nanofluid, magnetic stirrer was used and followed by desperation method (ultrasonic homogenizer / sonication) and the delivery of ZnO nanoparticle doped nanofluid to the cutting area through nozzle while machining this alloy. Figure 1 (a) shows the Experimental setup with MQL Nozzle arrangement and (b) shows the cryo treatment setup for tool inserts with microstructure of untreated and cryo treated tool inserts. The experimental design and average test results of the corresponding experiments are shown in Table 3.

Table 2
Technical data sheet of ZnO nano particles.

NAME OF THE PRODUCT	ZINC OXIDE NANO PARTICLES / NANO POWDER
Synonyms	Zinc Oxide
CAS	1314-13-2
Molecular formula	ZnO
Purity	99.5%
Average particles size	30–50 nm
Appearance	Milky white
Morphology	Nearly spherical
Specific surface	20–60m2/g
Bulk density	0.28-0.48g/cm3
True density	6g/cm3
Atomic weight	81.38 g/mol
Melting point	1,975°C
Boiling point	2,360°C
Thermal conductivity	49W/m-K
Thermal expansion	10–80K
Make	Nano Research Lab

Figure 1
(a) Experimental set up with MQL nozzle arrangement, (b) Cryo treatment setup for tool inserts.

Table 3
Experimental layout (L₂₇ OA) with Experimental output responses.

EX. NO.	MACHINING PARAMETERS			OUTPUT RESPONSES
	CUTTING SPEED (m/min)	FEED RATE (mm/rev)	DEPTHOF CUT (mm)	SURFACE ROUGHNESS (µm)	CUTTING FORCE (N)	MICRO HARDNESS (HV0.5)	TOOL TIP - TEMP. (°C)	POWER (Watts)	MRR (mm3/s)
1	35	0.02	0.5	1.64	192.40	395.2	95.3	112.23	5.83
2	35	0.02	0.75	1.50	186.23	358.9	112.9	108.63	8.75
3	35	0.02	1	1.37	180.35	350.2	136.1	105.20	11.67
4	35	0.04	0.5	1.89	202.18	383.9	100.6	117.94	11.67
5	35	0.04	0.75	1.78	196.30	352.1	122.5	114.51	17.50
6	35	0.04	1	1.65	191.13	343.7	141.0	111.49	23.33
7	35	0.06	0.5	2.11	212.25	358.9	111.2	123.81	17.50
8	35	0.06	0.75	2.04	206.37	328.4	129.3	120.38	26.25
9	35	0.06	1	1.91	200.50	309.4	150.5	116.96	35.00
10	45	0.02	0.5	1.39	161.72	363.2	96.3	121.29	7.50
11	45	0.02	0.75	1.25	155.85	329.5	118.3	116.89	11.25
12	45	0.02	1	1.13	149.97	320.8	139.7	112.48	15.00
13	45	0.04	0.5	1.63	171.79	348.4	102.5	128.84	15.00
14	45	0.04	0.75	1.52	165.92	319.6	126.2	124.44	22.50
15	45	0.04	1	1.40	160.04	303.2	146.1	120.03	30.00
16	45	0.06	0.5	1.90	181.87	321.9	116.5	136.40	22.50
17	45	0.06	0.75	1.81	175.99	297.7	133.5	131.99	33.75
18	45	0.06	1	1.67	170.11	285.3	154.1	127.58	45.00
19	55	0.02	0.5	1.10	131.30	341.3	108.3	120.36	9.17
20	55	0.02	0.75	1.00	125.44	310.8	127.9	114.99	13.75
21	55	0.02	1	0.92	121.56	295.8	148.2	111.43	18.33
22	55	0.04	0.5	1.43	141.41	325.9	113.7	129.63	18.33
23	55	0.04	0.75	1.32	135.53	297.6	136.5	124.24	27.50
24	55	0.04	1	1.23	130.66	289.8	155.5	119.77	36.67
25	55	0.06	0.5	1.64	151.48	297.1	125.3	138.86	27.50
26	55	0.06	0.75	1.56	145.60	269.4	146.1	133.47	41.25
27	55	0.06	1	1.40	139.73	256.9	163.8	128.09	55.00

The roughness of the machined samples were measured using surface roughness tester (Mitutoyo, SJ301) in four locations with cut off length of 4 mm and the average values were reported. The response of main cutting force (Fz) was recorded by three component piezoelectric dynamometer KISTLER 9257B type (Make:Zwitzerland) multi-channel amplifier through measuring times was 500 seconds & with the sample rate was 1,000 Hz. Through the charge amplifier, data acquisition was made with a computer using KISTLER DYNOWARE software. The microhardness of the machined surface values was measured using Wolpert Wilson Instruments (Vickers hardness tester) at different locations on all samples and the observations average values were recorded.

The tool-tip temperature at the cutting zone was measured using professional Infrared IR thermometer (HTC Instruments IRX 68 Distance: Spot 50:1), with measuring capacity of 1850 °C through dual LASER targeting with 150 ms sampling during turning operation is was measured. Moreover, the actual cutting or machining power required for during machining (turning operation) at the tool tip. This power parameter plays a crucial role in cost-effectiveness. Here, Power was calculated using the following formula,

(1) $$\text{P(Watts)}\text{=}\text{Fz}\times \text{Vc}\times \left(\frac{1}{60}\right)$$

Besides, Material Removal Rate (MRR) or Volume of material removed from the work piece per unit time of machining process is expressed by, the product of cutting speed (Vc), feed rate (f) and depth of cut (ap). The production rate of any machine or machining industries depends on mainly by the MRR. MRR would play a vital role in machining industries for higher productivity. MRR is calculated per unit time in (mm³/s):

(2) $${\text{MRR(mm}}^{\text{3}}\text{/s)}\text{=}\text{Vc}\times \text{f}\times ap\times \left(\frac{1000}{60}\right)$$

Where, Fz = cutting force measured, Vc = cutting speed (m/min), f = feed rate (mm/rev), and ap = depth of cut (mm). Figure 2 shows the flow chart for the Methodology used in this experimental study.

Figure 2
Flow chart for the Methodology used in this experimental study.

To optimize the machining parameters for the multiple characteristic was performed using Taguchi-based Grey relational Analysis (GRA) technique [⁴⁹, ⁵⁰]. First, the experimental results were converted to S/N (signal to noise) ratio using the following equation based on the characteristics: Except MRR (Higher the better) other responses were considered as lower the better characteristics. Where, n designates the replicas; y_ij designates the output values.

(3) $$\raisebox{1ex}{$S$}\!\left/ \!\raisebox{-1ex}{$N$}\right.ratio;\mathrm{for}\mathrm{Lower\; the\; better}=-10\ast \log \left(\frac{1}{n}\right){\displaystyle {\sum }_{i=1}^n{y}_{ij}^2}$$

(4) $$\raisebox{1ex}{$S$}\!\left/ \!\raisebox{-1ex}{$N$}\right.ratio;\mathrm{for\; Higher\; the\; better}=-10\ast \log \left(\frac{1}{n}\right){\displaystyle {\sum }_{i=1}^{n}1/y_{ij}^2}$$

Then, Normalization (conversion of the response value in-between 0-1) was performed by using equation (5) & (6) for all the responses and then grey relational co-efficient (GRG) was performed using equation (7).

(5) $$N_i^{\ast }(k);\mathrm{for\; Lower\; the\; better}=\frac{max{y}_i(k)-y_i(k)}{\max y_i(k)-\min y_i(k)}$$

(6) $$N_i^{\ast }(k);\mathrm{for\; Higher\; the\; better}=\frac{y_i(k)-\min y_i(k)}{\max y_i(k)-\min y_i(k)}$$

Where i = 1…m; k =1, 2, 3…n; yi(k) = original sequence 1, 2, 3...27; Ni*(k) value after GRG, min yi(k) and max yi(k) are the minimum and maximum value of yi(k) respectively.

(7) $${\in }_i(k)=\frac{\mathrm{\Delta }min+\tau \mathrm{\Delta }max}{{\mathrm{\Delta }}_{oi}(k)+\tau \mathrm{\Delta }max}$$

Where, ϵ_i(k) is the GRC; Δ_oi is deviation among $N_O^{*}\left(k\right)$ and $N_i^{*}\left(k\right)N_O^{*}\left(k\right)$ = ideal sequence (reference); Δ max = highest value of Δ_oi (k); Δ min = least value of Δ_oi (k); τ was assumed as 0.5 here, i.e: distinguishing coefficient. The grey relational grade (GRG) was calculated finally using the formula (8) after averaging the GRC values;

(8) $$GRG=\left(\frac{1}{m}\right){\displaystyle \sum {\in }_i}(k)$$

Where m indicates the number of response variables. The highest GRG indicates the closer to the ideal solution (i.e optimum) which means that deviation between actual experimental result and ideal value minimum. The GRC, GRG and their corresponding rank is shown in Table 4.

Table 4
Grey relational coefficient (GRC), GRG and their corresponding rank.

EX.NO.	GREY RELATIONAL COEFFICIENT (GRC)						GRG	RANK
	SR	FZ	MHD	TTEMP.	POWER	MRR
1	0.6222	0.7395	0.6875	0.3333	0.3945	0.3333	0.5184	17
2	0.5488	0.6806	0.5555	0.4212	0.3611	0.3790	0.4910	21
3	0.4901	0.6311	0.5296	0.5938	0.3333	0.4198	0.4996	19
4	0.7903	0.8515	0.6416	0.3571	0.4593	0.4198	0.5866	10
5	0.7093	0.7811	0.5351	0.4824	0.4184	0.4949	0.5702	12
6	0.6279	0.7267	0.5114	0.6437	0.3873	0.5668	0.5773	11
7	1.0000	1.0000	0.5555	0.4115	0.5473	0.4949	0.6682	1
8	0.9248	0.9084	0.4719	0.5338	0.4927	0.6027	0.6557	3
9	0.8065	0.8303	0.4287	0.7618	0.4470	0.7128	0.6645	2
10	0.4986	0.5062	0.5690	0.3377	0.5062	0.3602	0.4630	23
11	0.4422	0.4743	0.4746	0.4542	0.4461	0.4142	0.4509	24
12	0.3993	0.4452	0.4539	0.6298	0.3970	0.4634	0.4648	22
13	0.6166	0.5685	0.5244	0.3662	0.6493	0.4634	0.5314	15
14	0.5586	0.5309	0.4512	0.5094	0.5585	0.5566	0.5275	16
15	0.5029	0.4967	0.4157	0.7031	0.4877	0.6492	0.5426	13
16	0.7983	0.6434	0.4565	0.4428	0.8856	0.5566	0.6305	6
17	0.7302	0.5980	0.4047	0.5697	0.7321	0.6967	0.6219	7
18	0.6396	0.5574	0.3811	0.8160	0.6208	0.8483	0.6439	5
19	0.3892	0.3672	0.5049	0.3956	0.4924	0.3850	0.4224	26
20	0.3573	0.3464	0.4317	0.5226	0.4237	0.4473	0.4215	27
21	0.3333	0.3333	0.4010	0.7301	0.3867	0.5052	0.4483	25
22	0.5162	0.4070	0.4659	0.4259	0.6683	0.5052	0.4981	20
23	0.4694	0.3832	0.4045	0.5976	0.5548	0.6181	0.5046	18
24	0.4347	0.3648	0.3895	0.8389	0.4840	0.7345	0.5411	14
25	0.6222	0.4524	0.4035	0.5027	1.0000	0.6181	0.5997	9
26	0.5788	0.4251	0.3534	0.7031	0.7776	0.7959	0.6057	8
27	0.5029	0.4000	0.3333	1.0000	0.6319	1.0000	0.6447	4

3. RESULTS AND DISCUSSION

3.1. Influence of machining parameters on surface roughness (Ra)

In order to get the accurate values, the Surface roughness measurement was done at four different points and four times on the circumference of the turned surface and the average values was considered to represent the final R_a. The measured surface roughness was ranges from 0.92 to 2.11 µm; the higher roughness value of 2.11 µm was observed at higher feed (0.06 mm/rev). It is evident from Figure 3 that during machining of Incoloy 800H Superalloy hard to machining, as the cutting speed increases the value of surface roughness decreases due to the effect of thermal softening effect and at the same time the surface roughness increases with the increase in feed rates from 0.02 to 0.06mm/rev, it is due to the strain or work hardening effect of this material property, vibrations and the tool flank wear that is caused during machining at higher feed rate [⁴³, ⁵¹].

Figure 3
Effect of machining parameters on Surface Roughness at different feed rate 0.02, 0.04 and at 0.06 mm/rev (a) at 0.5 mm DOC, (b) at 0.75 mm DOC and (c) at 1 mm DOC.

The observed low value of surface roughness (0.92 µm) was observed at the highest cutting speed of 55 m/min, the lowest feed rate of 0.02 mm/rev and the highest depth of cut of 1 mm. From this experimental study it is observed that as there is an increase in cutting speeds from 35 to 55 m/min, the value of surface finish was low and the surface finish of the machined area was better. It is due to the thermal softening effect that happens when greater cutting speeds are used during turning. At higher cutting speeds, more heat is generated and, the nano-lubrication which is stagnated at the textured inserts on the flank face reduces the temperature and produces a surface morphology due to the thermo-physical properties of ZnO nanoparticles and thus assists to reduce the cutting temperature. During machining at high speed with ZnO nanoparticles and textured inserts, the cutting temperature due to the friction in between the tool and workpiece is low and thereby the surface finish is better. Figure 4 shows the SEM Image of the Feed marks of the machined samples of (a) Initial Condition, (b) Optimal Condition and (c) Final Condition. Moreover, the formation of built-up edge is also less compared to other speed conditions considered. The small feed marks on the machined surfaces are clearly visible in the surface topography photos. The depth of cut has a minor impact on the surface roughness on the machined surface. There is an increase in hardness of the textured cryo treated inserts and with the addition of the thermo-physical properties of ZnO nanoparticles reduces the adhesion of work material on the tool surface. Thereby, the contact length at the tool-chip on the rake face of the cryo treated textured insert decreases which in turn reduces the surface roughness value and improves the surface finish and reduces the cutting forces also. The above results are in good agreement with the previous literatures [⁴, ²³, ³⁷, ⁴⁰, ⁴⁶]. The surface topography of the machined surface was observed using the surface profilometer (White light Interferometer, RTEC Instruments, USA).

Figure 4
SEM Image of the Feed marks of the machined samples (a) Initial condition, (b) Optimal condition and (c) Final condition.

The 3D images covered a distance of 2.9 × 2.3 mm, and the peaks and valleys of the surface profile was analysed using Gwyddion software. Figure 5 shows the 3D Surface topography of the machined samples (a) Initial Condition, (b) Optimal Condition and (c) Final Condition. Variations in the surface profile observed (Figure 5a, b and c) during machining were smooth and rough during the initial and final machining conditions respectively and this happened due to the various combinations of the machining parameter conditions.

Figure 5
3D Surface topography of the machined samples (a) Initial Condition, (b) Optimal Condition and (c) Final Condition.

3.2. Influence of machining parameters on Cutting Force (Fz)

Cutting force is the most significant and dominant parameter that affects the machined surface. During machining hard materials the generation of cutting force affects the power consumed, dimensional deviation and tool wear. The effect of machining parameters on the cutting force is showed in the Figure 6. It is clear that the cutting force increases as the feed rate and the depth of cut increases during machining with cryo treated textured tool inserts assisted with nano particles. The ZnO nano particles are capable of penetrating in between the frictional surfaces and it can reduce the frictional force that is generated during machining hard to machine materials. At the maximum cutting speed of 55 m/min, lowest feed of 0.02 mm/rev and the maximum depth of cut 1 mm, the lowest cutting force of 121.56 N were achieved. Similarly the highest cutting force of 212.25 N were observed for the machining conditions at cutting speed 35 m/min, Feed rate 0.06mm/rev and depth of cut 0.5mm.

Figure 6
Effect of machining parameters on Cutting Force at different feed rate 0.02, 0.04 and at 0.06 mm/rev (a) at 0.5 mm DOC, (b) at 0.75 mm DOC and (c) at 1 mm DOC.

The greatest heat absorption by the work material during machining and the inappropriate heat dissipation to the surrounding area of the machining area are the causes of this increase in cutting force. During machining at higher cutting speeds, high compressive stress occurs at the nose radius of the tool inserts and hence the flank surface of the tool insert gets distorted. Moreover, this distortion causes difficulty in penetration and increased tool wear during machining of Incoloy 800H Superalloy that causes higher cutting force. It is found that the cutting force increases as the feed rate increases. During Machining at lesser feed rate, the contact distance between tool and work material is less and the sharp edge of the textured cryo treated inserts is not deteriorated and hence the machined surface is smooth and the cutting force measured is less for the considered machining conditions. Similarly, as the feed rate increases from low level to higher level, the contact length between the tool and work material is more and this will affect the sharp edge of the textured cryo treated tool insert that result in increased cutting force and MRR. Moreover, the resistance and the area of the sheared chip is increased in the direction of shear is also increased that leads to higher cutting forces. The above results are in good agreement with the previous literatures [⁴, ²³, ³⁷, ⁴⁰, ⁴⁶, ⁵²]. Comparing all the machining parameters considered, it is observed that the measured cutting force depends on the speed and feed rate while depth of cut has negligible effect during machining with texture cryotreated nano fluid assisted machining. The cutting force measured was less while machining with nano fluid assisted MQL and it is due to the formation of a thin tribological film that reduces the friction in between the tool work interface during machining.

3.3. Influence of machining parameters on Micro Hardness (HV0.5)

Micro hardness measurement of the machined specimens with textured cryo treated inserts with nano lubricants are mounted appropriately after cutting it into semicircular pieces using wire-cut EDM. The micro hardness values was at four different locations on each sample, starting with a point 30 µm away from machined edge with a load of 500g and dwell period of 10s. Microhardness measurements were carried out into the depth of material along a straight line perpendicular to the turned surface (radial direction). The machining parameters had a significant impact on the microhardness of the machined samples. Previous Table 3. showed that the micro hardness values decreased as the cutting speed was increased. Figure 7 shows the effect of machining parameters on Micro Hardness at different feed rate 0.02, 0.04 and at 0.06 mm/rev (a) at 0.5 mm DOC, (b) at 0.75 mm DOC and (c) at 1 mm DOC.

Figure 7
Effect of machining parameters on Micro Hardness at different feed rate 0.02, 0.04 and at 0.06 mm/rev (a) at 0.5 mm DOC, (b) at 0.75 mm DOC and (c) at 1 mm DOC.

Increased cutting speed from lower to higher level (35 to 55 m/min) produces an excessive amount of heat on the machined surface, softening the material and lowering the work materials hardness. Due to the thermal softening effect, the cutting temperature increased as the cutting speed increased from 35 to 55 m/min, subsequently decreasing the machined surface hardness [⁵⁰]. The vegetable oil enriched with Nano Particles that was used for machining helps to reduce the temperature further at the tool-tip interface during machining. During machining the vegetable oil enriched with ZnO nano particles forms as a nano-layer tribological film between the tool tip interface and it penetrates in between the frictional surfaces and thus the frictional force is reduced at the machining zone. Austenite is a stronger strain hardening phase and therefore the austenite matrix in Incoloy 800H increases the hardness and it is due to strain hardening effect at the machined surface. The maximum micro hardness value of 395.2 HV_0.5 was acquired while turning with the lower cutting speed of 35 m/min. Here poor metal shearing action takes place and the metal is subjected to increased work hardening and increase in hardness at the machined surface [⁵², ⁵³]. Figure 8. Shows the main effects plot for the (a) Surface roughness, R_a, (b) Cutting force, F_z and (c) Micro Hardness. Additionally, when the cutting speed increases, the microhardness reduces, which had been expected due to good metal shearing action. This would shorten the time required to shear off the metal, which will decrease the amount of work hardening that take place during turning operations. The creation of microhardness is significantly impacted by this phenomenon. This effect has a significant influence on the microhardness formation. Similar types of observations were observed for all the machining parameters considered. The lowest microhardness (256.9 HV_0.5) was observed for the machining conditions of highest levels of cutting speed, feed rate and depth of cut.

Figure 8
The main effects plot for the (a) Surface roughness, Ra, (b) Cutting force, Fz and (c) Micro hardness.

3.4. Influence of machining parameters on Tool-Tip Temperature

Infrared thermography was used to assess the cutting tool-tip temperature distribution at the tool-chip interface during the turning of Incoloy 800H Superalloy, using cryotreated textured tool inserts and nanoparticle-assisted MQL under various machining circumstances. Based on to the experimental data used for machining, variations in temperature was observed during machining and were dependent on the levels of machining parameters, Figure 9 illustrates how machining factors affect the Tool-Tip temperature at various feed rates 0.02, 0.04 and at 0.06 mm/rev and at (a) at 0.5 mm (b) at 0.75 mm and (c) at 1 mm DOC respectively. During turning at higher cutting speeds resulted from increased power consumptions which in turn caused increased heat flux across the chip-tool contact surface. The machining temperature rises in the cutting zone as the machining factors such as cutting speed, feed rate, and depth of cut increases from lower level to higher levels. The strain rate in the shear zone area would likely to be high during machining at higher speed and hence more heat energy would be generated that results in higher temperature at the tool-chip interface [²⁶, ³⁰]. Figure 10 shows the main effects plot for the (a) Tool tip-Temperature, (b) Power, (c) MRR. Furthermore, as speed increases from lower level to higher level, the time for heat dissipation decreases, due to lower thermal conductivity and thus temperature rises. At the same time, tool-tip temperature at the machining zone gradually increases as the rate of feed increases from 0.02 mm/rev to 0.06 mm/rev; similar trend of increase in temperature were observed for the higher cutting speed and depth of cut, due to high temperature and higher material removal rate from the work piece. The above results are in good agreement with the previous literatures [⁴⁰, ⁴⁴]. The measured cutting tool- tip temperature range is from 89.1 °C – 162.4 °C. The addition of ZnO nano particles to the vegetable oil reduces the cutting temperature and it is mainly due to the improved heat transfer coefficient of the vegetable oil with addition of the ZnO nano particles [¹⁷, ²⁰].

Figure 9
Effect of machining parameters on Tool-Tip temperature at different feed rate 0.02, 0.04 and at 0.06 mm/rev (a) at 0.5 mm DOC, (b) at 0.75 mm DOC and (c) at 1 mm DOC.

Figure 10
The main effects plot for the (a) Tool tip-temperature, (b) Power, (c) MRR.

3.5. Influence of machining parameters on Power

The obtained highest power was 138.89 Watts (Table 3) during machining at the levels of 55 m/min of cutting speed, 0.06 mm/rev of feed rate and 0.5 mm of depth of cut of, it was due to the more volume of material removed from the work piece or higher Material Removal Rate (MRR) [⁵⁰]. Similarly the lowest power was obtained at the lowest levels of cutting speed and feed rate (35 m/min & 0.02 mm/rev) and highest levels of depth of cut (1 mm). Moreover, during machining the vegetable oil enriched with Nano Particles, the ZnO nano particles helps to reduce the frictional surfaces that makes the machining easier and hence the power required for machining is also reduced [²⁸, ³⁰]. Figure 10b shows the main effects plot for the power, it reveals that feed rate is the most influencing parameter followed by cutting speed and depth of cut.

3.6. Influence of machining parameters on Material Removal rate (MRR)

The ranges of MRR values obtained from this experimental study vary from 5.83 to 55 mm³/s (Table 3) due to the effects of machining parameters levels (lowest to the highest). It was due to the reason of thermal softening effect while cutting speed increases from lower level to higher levels (35 m/min to 55 m/min). The highest MRR (55 mm³/s) was attained at the highest levels of cutting speed of 55 m/min, feed rate of 0.06 mm/rev and depth of cut of 1 mm, due to more volume of material removed from the work piece and it is proved based on the highest S/N ratio (34.8073 dB). During machining with textured tool inserts and with the addition of ZnO nano particles to the vegetable oil, the machining is made easier and heavier cuts can be given during ploughing of work material [³⁹, ⁴⁰, , ⁴¹, ⁴²]. Besides, heat produced by the work-hardening effect controls the removal of material from work piece during turning operation. Figure 10c shows the main effects plot for the MRR, it reveals that feed rate is the most influencing parameter followed by cutting speed and depth of cut [⁵⁰].

3.7. Tool wear analysis

EDS (Energy Dispersive Spectroscopy) analysis was conducted to study the tool wear and the diffusion at the tool chip interface, using scanning electron microscopy (SEM), The EDS analysis of the tool insert is shown in Figure 11a and it is observed that the Fe, Cr, Ni, Ti, Al, and Mg contents loss were the good indications of the work material transfer to and stick on the cutting tool inserts’ rake face. It is due to the amount of carbon in the tungsten carbide insert significantly greater than Incoloy 800H work piece material [⁷, ⁸, ¹⁰]. In addition, it was found that the growths in crater wear of the cutting inserts were effectively dealt with the diffusion and abrasive wear mechanism. Figure 11a confirms the elemental mapping of cryogenically treated textured tool inserts after machining at the optimal machining conditions i.e., cutting speed, feed rate and depth of cut at 55 m/min, 0.06 mm/rev and 1mm respectively. Besides using ZnO nanoparticles with vegetable oil decreased tool wear due to the effect of nanoparticles stagnation within the texture that reduced variations in machining temperature and friction during turning [³², ⁴⁰].

Figure 11
(a) Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) analysis of tool (b) The main effects plot for the Grey relational grade (GRG).

3.8. Influence of machining parameters on Grey relational grade (GRG)

The highest grey relational grade value (0.6682) was obtained while machining seventh experimental run indicates the optimal solution (A₁B₃C₃). Figure 11b shows the main effects plot of the corresponding GRG value with respect to the experimental run. Table 5 shows the Mean response table for the Grey relational grade (GRG), feed rate was the most influential parameter followed by cutting speed and depth of cut. It was identified by the L₂₇ OA and also by ANOVA analysis. Moreover, ANOVA analysis was performed using statistical software MINITAB 16.0, for the GRG as well as for the output responses. The investigations were performed in favour of 5% significance level i.e., 95% confidence level and the results were showed in Table 6 and Table 7 respectively. The qualitative understandings of relative factors effects was given by F ratio (F > 4); and the corresponding P value <0.05 means, that corresponding machining parameter was significant on the output responses [²⁰, ²¹, ⁵², ⁵³].

Table 5
Mean response table for the Grey relational grade (GRG).

LEVELS	CUTTING SPEED (m/min)	FEED RATE (mm/rev)	DEPTH OF CUT (mm)
	A = Vc	B = f	C = Ap
Level 1	0.5813*	0.4644	0.5465
Level 2	0.5418	0.5421	0.5388
Level 3	0.5207	0.6372*	0.5585*
Max-Min	0.0606	0.1728	0.0197
Rank	2	1	3
* Indicates the optimum level of turning parameters ie., A1B3C3

Table 6
ANOVA analysis for Grey Relational Grade (GRG) using adjusted SS.

PARAMETERS	DOF	ADJUSTED SUM OF SQUARES	ADJUSTED MEAN SQUARE	F VALUE	P VALUE
Cutting Speed (m/min)	2	0.017040	0.008520	65.40	0.000
Feed (mm/rev)	2	0.134773	0.067387	517.29	0.000
Depth of Cut (mm)	2	0.001780	0.000890	6.83	0.005
Error	20	0.002605	0.000130
Total	26	0.541722
S = 0.0114135 R-Sq = 98.33% R-Sq(adj) = 97.83%

Table 7
ANOVA Analysis for the output responses, using Adjusted SS.

RESPONSES	PARAMETERS	DF	ADJUSTED SUM OF SQUARES	ADJUSTED MEAN SQUARE	F VALUE	P VALUE	REMARKS
Surface roughness(SR)	Cutting speed (m/min)	2	1.02260	0.51130	1004.74	0.000	S = 0.0225586 R-Sq = 99.60% R-Sq(adj) = 99.47%
	Feed (mm/rev)	2	1.25060	0.62530	1228.76	0.000
	Depth of cut (mm)	2	0.23389	0.11694	229.80	0.000
	Error	20	0.01018	0.00051
	Total	26	2.51727
Cutting force(Fz)	Cutting speed (m/min)	2	16501.7	8250.8	51646.8	0.000	S = 0.399696 R-Sq = 99.98% R-Sq(adj) = 99.98%
	Feed (mm/rev)	2	1781.7	890.8	5576.21	0.000
	Depth of cut (mm)	2	582.3	291.1	1822.36	0.000
	Error	20	3.2	0.2
	Total	26	18868.8
Micro hardness(MHD)	Cutting speed (m/min)	2	13810.3	6905.2	692.56	0.000	S = 3.15760 R-Sq = 99.32% R-Sq(adj) = 99.12%
	Feed (mm/rev)	2	6799.8	3399.9	341.00	0.000
	Depth of cut (mm)	2	8543.2	4271.6	428.43	0.000
	Error	20	199.4	10.0
	Total	26	29352.8
TooltipTemp.(TT)	Cutting speed (m/min)	2	943.5	471.8	262.11	0.000	S = 1.34161 R-Sq = 99.63% R-Sq(adj) = 99.51%
	Feed (mm/rev)	2	1216.2	608.1	337.84	0.000
	Depth of cut (mm)	2	7413.6	3706.8	2059.42	0.000
	Error	20	36.0	1.8
	Total	26	9609.3
Power(P)	Cutting speed (m/min)	2	589.65	294.82	156.82	0.000	S = 1.37115 R-Sq = 98.07% R-Sq(adj) = 97.49%
	Feed (mm/rev)	2	998.14	499.07	265.45	0.000
	Depth of cut (mm)	2	323.87	161.93	86.13	0.000
	Error	20	37.60	1.88
	Total	26	1949.25
Material removal rate(MRR)	Cutting speed (m/min)	2	450.00	225.00	15.92	0.000	S = 3.75940 R-Sq = 92.97% R-Sq(adj) = 90.87%
	Feed (mm/rev)	2	2278.13	1139.06	80.60	0.000
	Depth of cut (mm)	2	1012.50	506.25	35.82	0.000
	Error	20	282.66	14.13
	Total	26	4023.29

Table 6, shows the Analysis of variance (ANOVA) for the Grey relational grade (GRG) using Adjusted SS. According to L₂₇ OA, the A₁B₃C₃ levels produced the best optimum turning conditions for machining Incoloy 800H super alloy, with a cutting speed of 35 m/min, feed of 0.06 mm/rev, and depth of cut of 1 mm for the multi-response optimum characteristics.

Table 7 shows the ANOVA Analysis for the output responses using Adjusted SS. The most significant curve on the Grey Relational Grade (GRG) is the one with a greater slope. In terms of GRG reaction, Feed rate was identified as the most influencing parameter for the multi-performance characteristics when compared with cutting speed and depth of cut on the responses.

3.9. Confirmation experiment

Final step in this experimental study is to conduct the confirmation experiment to confirm the enhancement of performance characteristics by the obtained optimal levels of machining parameters.

The projected GRG (γ_estimated) at the optimal machining condition can be designed by:

(9) $${\gamma }_e{}_{stimated}=\gamma n+{\displaystyle {\sum }_{(i=1)}^n(\gamma i-\gamma n)}$$

Where γn (0.5569) showed the total mean of GRG, γi stand for the mean of GRG at the optimal condition (i.e. significant parameters of A, B, and C); n is the number of input factors which have significant effects on the responses. GRG can be estimated using the Eqn. (9), at optimal input parameters and for validating the test, confirmation tests with two replicas were used at optimal machining conditions and the obtained averages result were given in Table 8. This result confirmed the enhancement of out responses when compared to the initial conditions of the experiment.

Table 8
Results of the confirmation experiment.

OUTPUT RESPONSES	INITIAL CONDITIONS LEVELS	OPTIMAL MACHINING CONDITIONS
		PREDICTION	EXPERIMENT
	A1B3C1	A1B3C3	A1B3C3
Surface roughness, μm	2.11		2.10
Cutting force, N	212.25		210.5
Micro hardness, (HV0.5)	358.9		353.9
Tooltip-Temperature, °C	111.2		106.2
Power, Watts	123.81		121.2
MRR, mm3/sec	17.50		17.50
GRG	0.6682	0.6811	0.7024
The percentage improvement in GRG = 4.87%

4. CONCLUSIONS

Cryogenically treated laser-textured cutting inserts using vegetable oil enhanced with nanoparticles by MQL and the experiments (turning operation) were performed on Incoloy 800H super alloy in a CNC turning centre. Surface roughness average, Cutting force, Micro hardness, Power consumption, tool-tip temperature and Material removal rate (MRR), were recorded as output responses to the turning parameters which included cutting speed, feed rate, and depth of cut. Besides, the Taguchi-based GRA approach was used to determine the optimum turning parameter settings for the multi-response characteristics. The following observations and conclusions were drawn.

• The addition of ZnO nanoparticles-enriched vegetable oil has a positive effect on reducing high cutting temperature and pressure during machining of this Incoloy 800H superalloy.

• Due to the limited thermal conductivity of Incoloy alloy 800H, it was found that when speed increased, the time required for heat dissipation decreased, resulting in a rise in tool-tip temperature. The tool wear profile was significantly reduced during machining, due to the flow of ZnO nano particles between work piece and tool-tip.

• The optimum machining parameters were determined to be 35 m/min of cutting speed, 0.06 mm/rev of feed, and 1 mm of depth of cut for the multi-response optimum characteristics and the corresponding responses recorded were Surface roughness 1.91 (µm), Cutting force 200.50 (N), Micro hardness 309.4 (HV_0.5), Tool tip-Temp. 150.5 (°C), Power 116.96 (Watts) and MRR 35 (mm³/s).

• Micro hardness, Tool-tip temperature and Power during machining were reduced to 1.31%, 4.53% and 2.10% respectively.

• Based on the ANOVA analysis, the feed rate was found as the most influencing parameter that affected the machinability of Incoloy 800H super alloy followed by cutting speed and depth of cut.

• Laser textured-cryogenically treated cutting insert’s hardness was increased to 3.8–4.17%, it enhances the cutting inserts’ wear resistance property when compared with untreated inserts.

• The 3D surface topography of the turned surfaces were also analyzed using WLI for the initial, final and optimum machining conditions.

• The experimental results exhibited that the laser textured-cryogenically treated cutting inserts using vegetable oil enriched with nano Particles, showed improved wear resistance and increased tool life.

• The results attained could serve as a database of references for the manufacturing sector. Moreover, Incoloy 800H super alloy can be machined using various nano particles enriched vegetable oils and with different textured patterns.

5. ACKNOWLEDGMENTS

The Aauthors are thankful to the A. P. J. Abdul Kalam Research Centre, Department of Mechanical Engineering, Adhi College of Engineering and Technology, for providing the research facility and support to undergo the research work in the institution.

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Publication Dates

• Publication in this collection
  15 Sept 2025
• Date of issue
  2025

History

• Received
  26 Mar 2025
• Accepted
  29 July 2025