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Article

Sustainable Grinding Performances of Nano-Sic Reinforced Al Matrix Composites under MQL: An Integrated Box–Behnken Design Coupled with Artificial Bee Colony (ABC) Algorithm

1
Centre for Composite Science and Tribology, Kanchipuram 631561, India
2
Department of Mechanical Engineering, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya, Enathur, Kanchipuram 631561, India
*
Author to whom correspondence should be addressed.
Sustain. Chem. 2022, 3(4), 482-510; https://doi.org/10.3390/suschem3040030
Submission received: 17 September 2022 / Revised: 27 October 2022 / Accepted: 9 November 2022 / Published: 14 November 2022

Abstract

:
The presence of abrasive particles in ceramic reinforced composite materials makes the machining complicated by generating friction at elevated temperatures. Lubricants can be used to prohibit the hazard of higher temperatures. This research work is focused on examining the effects of lubricants on the grinding performances of Al matrix composites reinforced with nano-SiC particles under minimum quantity lubrication (MQL). A cylindrical grinding machine is used to perform the grinding experiments by employing a Box–Behnken design. Multiple performances, such as surface roughness, grinding forces and temperature, are optimized by considering the depth of cut, speed of the workpiece, wheel speed and wt % of nano-SiC using response surface methodology (RSM)-based artificial bee colony (ABC) algorithm. Atomic force microscope (AFM) and scanning electron microscopy (SEM) are used to observe the morphologies of the machined surfaces and the wheel.

1. Introduction

At present, sustainable manufacturing has become an emerging environmental, economical, societal and technological challenge to the industry, academia and government entities [1]. Sustainable development is an important issue for many fields, including production, industry and manufacturing. However, in modern production, there has been increasing attention directed toward careful selection of proper cutting fluids from the viewpoint of cost, ecology and environmental issues [2].
The usage of metal matrix composites (MMCs) is increasing in both aeronautical and automotive industries [3]. The successful and cost-effective machining of MMCs is essential for the production of flawless items possessing improved surface finish and the required dimensions. The grinding process might play a significant part in hard machining and finishing operations, where a single grinding operation signifies a financial alternative, by eliminating the necessity for conventional machining processes, such as milling and turning [4].
Even though various ceramic reinforcements were found suitable for MMCs, Silicon carbide (SiC) has received increased attention owing to its dimensional stability, stiffness, high specific strength, wear resistance and high hardness [5]. The machining of SiC reinforced composites presents substantial difficulties for manufacturing because of the non-homogenous and anisotropic structure of the reinforcing particles along with their high abrasiveness. Early tool failure is proclaimed as a critical issue in the machining of MMCs [6]. The usage of traditional coolants to overcome these issues is an alternate option, even though unsuitability has been reported relative to traditional coolants in the machining of composites [7]. While MMCs are preferred due to high capacity and machine-intensive mechanisms, it is crucial that the machinability of the materials is implicit. A feasibility study of grinding on nano-SiC reinforced Al matrix composites was performed under the influences of parameters including wheel speed, workpiece speed, depth of cut and wt % of nano-SiC [8].
In conventional machining processes, the technical efforts focused on the investigation of the grinding characteristics of metal matrix composites are inadequate [9,10,11,12,13]. In the grinding of SiCp/Al composites, a common problem is the formation of voids and delamination on the machined surface, which is due to reinforced particles being pulled out and aluminum matrix adhesion on the machined surface [14]. Optimizing the grinding process is a difficult task because the grinding wheel contains a large number of cutting edges, which are randomly spaced within the periphery of the wheel [15].
Surface finish and surface integrity are important for surface-sensitive parts subjected to fatigue or creep. Therefore, finishing processes, such as grinding, are utilized to marginally improve the surface integrity of machined MMNCs [16]. Dry cutting is preferred in the field of environmentally friendly manufacturing. However, these methods are less effective when higher machining efficiency, better surface finish and severe cutting conditions are required [17]. The minimum quantity of lubricant (MQL) concept in machining is an alternative to completely drying or flooding the lubricating system for reducing the amount of lubricant [18]. This calls for the applicability of MQL-based vegetable oil in the grinding of MMNCs.
Driven by the elevated cost of cutting tools, it is logical and attractive to optimize the cutting conditions, such as the effect of the cutting fluid. Currently, to reduce the adverse effects related to the usage of cutting fluids, researchers have established eco-friendly manufacturing, such as employing a cutting fluid based on vegetable oil with other cutting fluids in machining and minimum quantity lubrication (MQL) in machining [19].
The most important objective of the cutting fluid is to minimize the friction between abrasive work contacts by lubricating the whole machining zone [20]. The synthesized nano-fluids are effective in reducing the grinding forces and temperatures, especially in extreme machining conditions [21]. Nano-fluid-assisted MQL grinding obtained low grinding force ratios, specific grinding energies and surface roughness [22]. Optimal grinding results are obtained when the MQL nozzle is positioned at a 10–20° angle toward the wheel [23]. MQL can effectively reduce friction at the cutting interface, inhibit temperature rise, prevent adhesion, extend tool life and improve surface quality in machining processes, such as turning, milling, drilling and grinding [24].
Bin and Albert demonstrated that MQL grinding can achieve the same level of material removal rate without increasing the grinding forces compared to flood cooling [25]. Sethuramalingam et al. showed that the carbon nano-tube mixed with nano-fluid in the grinding process improved surface characteristics, such as surface roughness and micro-cracks [26]. The grinding forces are significantly reduced when a nano-cutting fluid is used, even at low concentrations of nano-particles, and surface finish was found to improve with higher concentrations of nano-particles [27]. MQL grinding reduces the grinding forces and specific grinding energy. As a result, the wheel wear is lowered with an increase in G-ratio. In addition, the surface generated by MQL grinding is smoother than fluid grinding and significantly better than dry grinding [28]. An improvement in tool life of almost 20–25% has been observed under minimum quantity lubrication (MQL) due to better cooling and lubricating effects. However, this effect is more prominent at a higher cutting speed of 150 m/min [29].
MQL grinding using vegetable oil achieved a lower friction coefficient, as well as specific grinding energy and grinding wheel wear compared to flood grinding [18]. MQL grinding in comparison to fluid cooling and dry grinding reduces the tangential forces considerably due to the presence of a lubricant around the grinding wheel, providing better slipping of the grain at the workpiece–tool interface [30]. Water-based Al2O3 nano-fluid MQL grinding can significantly reduce the grinding temperature, decrease the grinding forces, improve the ground surface morphology and reduce the surface roughness in comparison to pure water MQL grinding [31]. Nano-fluid MQL grinding with higher concentration nano-particles has a lower grinding force, grinding temperature and surface roughness in comparison with lower concentration nano-particles [32]. Nano-fluid MQL is effective for reducing the grinding forces and enhancing surface quality. In addition, the size and volumetric concentration of nano-particles are critical parameters to influence the performances of the micro-grinding process [33]. The tangential cutting force is decreased with MQL, possibly due to the presence of a lubricant around the grinding wheel, providing better slipping of the grain at the workpiece–tool interface [34].
Sani et al. have studied the effectiveness of the modified neat Jatropha lubricant (MJO) while machining AISI 1045 steel orthogonally by using the MQL technique [35]. The experimental results revealed that MJO + AIL 10% and MJO + PIL 1% lubricant blends enhanced the machinability characteristics during the cutting process, thereby promoting a further sustainable and greener working environment. Krolczyk et al. have reviewed the hard-to-cut materials’ machining ecological trends. They analyzed the ways of improving the machining processes through sustainable and cleaner production. The use of sustainability in machining resulted in an eco-friendly environment [9]. Li investigated the performance of graphene-based nano-fluids in the milling of TC4 alloy applying MQL. The experimental results revealed that MQL machining improved the machinability characteristics of TC4 alloy by employing enhanced cooling and lubrication [10]. Mia et al. have found that LN2 dual jets improved the machinability characteristics of Ti-6Al-4V. It was observed that the optimized parameters mainly had a feed rate of 0.16 mm/rev and a cutting speed of 140 m/min during the cryogenic machining process [11]. Gajrani et al. have investigated the performance of a green cutting fluid based on vegetable oil in the turning of hardened AISI H-13 steel under MQL, flood and dry environments. The experimental results revealed that the machinability characteristics of the workpiece improved with the MQL technique in comparison with flood and dry machining [12]. Ni assessed the performance of water-based cutting fluids during the grinding disc cutting process (GDCP) of AISI 1045. The experimental results revealed that W-S-based cutting fluids can decrease the life span and cutting tilt by 34.5% and 14.8%, respectively, while enhancing the G-ratio by 77.6% when compared to cutting under dry conditions [36]. Stachurski et al. have studied the simultaneous introduction of a lubricant into the grinding zone by using MQL and compressed cooled air (CCA) during the grinding process of hob cutters’ cutting blades. The experimental results revealed that the hybrid MQL–CCA method improves surface roughness by limiting the conventional coolant flow rate when compared with flood grinding [37].
Dogra et al. reviewed the eco-friendly techniques in grinding operations. They stated that MQL is a sustainable, cleaner production system during the grinding process, as it can enhance the heat transfer rate at the cost of a lower amount of coolants [38]. Zhang et al. have investigated the performance of a cryogenic air nano-fluid MQL (CNMQL) system during surface grinding of Ti-6Al-4V. It was revealed from the experiments that the CNMQL system obtained a minimum specific grinding energy of 51.96 J/mm3 and a friction coefficient of 0.60 as compared to nano-fluid MQL and cryogenic air due to better grinding/lubricating mechanisms [39]. Rapeti et al. evaluated the influence of nano-fluids based on vegetable oil during the turning of AISI 1040 steel. Optimum results were obtained by employing gray relational analysis based on the Taguchi technique. The experimental results revealed that 0.5% nano molybdenum disulfide in coconut oil improved the turning performance at a feed rate of 0.14 mm/rev and a cutting speed of 40 m/min under MQL conditions [40]. Lee et al. have employed electrostatic lubrication assisted by the nano-fluid airflow (AF-ESL) technique during the micro-grinding operation in a titanium alloy. The experimental results revealed that nano-fluid AF-ESL can enhance the grinding characteristics of a workpiece at a nano-diamond particle size of 80 nm in the nano-fluids [41].
Pashmforoush and Bagherinia have studied the effect of copper nano-fluid based on water during Inconel 738 superalloy grinding. The performance measures of the grinding process, such as surface roughness and wheel loading ratio, were evaluated by employing the image processing technique. The experimental results revealed that nano-fluids had improved the grinding performance significantly in comparison with conventional fluid grinding and dry grinding [42]. Mia et al. have investigated the effect of a solid lubricant, MQL and dry plain turning with compressed air-cooling techniques during the machining of hardened AISI 1060 steel. The experimental results revealed that the MQL technique improved the machinability characteristics of the workpiece, thereby providing environmental friendliness and cleaner production [43]. The outcome of the grinding process is influenced by many factors/parameters, and the design of experiments is used to identify the individual contributions of the parameters along with their intricate relationships [8]. Box–Behnken designs are response surface designs that have the prime benefit of addressing the problems involved in determining the experimental limits and specifically in preventing extreme treatment combinations [5].
The literature indicated that sustainable manufacturing appears to be a universal idea. Mitigating the vital elements in all areas, including machining processes and MQL-based nano-fluid lubrication, has demonstrated a significant decrease in the consumption of the cutting fluid as well as the cutting energy. A careful examination of the literature review revealed that only a minimal number of articles are available in the area of MQL-based grinding of MMCs. Therefore, this research focuses on examining the effect of lubricants on the grinding performance of nano-SiC reinforced Al matrix composites employing MQL. A cylindrical grinding machine is used to carry out the experiments applying a Box–Behnken design. Multiple performances, such as surface roughness, grinding forces and temperature, are optimized by considering the depth of cut, wheel speed, the speed of the workpiece and the wt % SiC nano-particles using an RSM-based artificial bee colony (ABC) algorithm. Atomic force microscope and scanning electron microscopy (SEM) are used to observe the morphologies of the machined surfaces and the wheel.

2. Materials and Methods

2.1. Material Used

Aluminum reinforced with SiC nano-particles is fabricated by a vacuum-based solidification process. The details of aluminum and nano-SiC used for the fabrication are given in Table 1. SEM with EDX and IR spectroscopy of nano-SiC reinforced aluminum matrix composites are shown in Figure 1. A uniform distribution of SiC nano-particles in the metal matrix nano-composites is observed. The micro-graphs predict that commercial nano-particles of silicon carbide will exhibit typical features that peak slightly below 800 cm−1. This is a manifestation of the fundamental Si and C sublattice corresponding to the Reststrahlen band of the SiC single crystal observed in the range of 770 to 1000 cm−1 [44].
Vegetable oil based on cashew nut, SAE20W40 and Nano-TiO2 bought from M/s Ganapathy traders, India, are used for this investigation. The ultrasonic processor is used to mix 500 mL of cashew nutshell oil and 10 g of Nano-TiO2. Prior to the property measurements, the blended samples are maintained ideal for 24 h. The measured nano-fluid properties are presented in Table 2. The heat-carrying ability of the nano-fluid increases as the flash and fire points increase. The diameter of the wear scar and the image of the wear scar on the ball surface are measured using a TR-30L-IAS four-ball tribometer. The wear tests are carried out at a regular load of 148 N, speed of 1200 rpm and lubricant temperature of 75 °C [45]. Specific image capture software is used to measure the diameter of the wear scar and to obtain the wear scar image on the ball surface. The wear scar diameter is very low in cashew nutshell oil + TiO2 compared to that of SAE20W40 and cashew nutshell oil. It is evident from Figure 2 that the wear scar image of cashew nutshell oil + TiO2 is smoother, more precise and circular.

2.2. Experimental Design

The experiments for this work are designed using an RSM-based Box–Behnken design and other designs to model and examine the responses to MQL-based grinding of composites. The design scheme is shown in Table 3. Based on the assessment, the number of experiments and the literature study analysis, five factors are changed over three levels.

2.3. Experimental Procedure

Experiments are carried out using an HMT-brand cylindrical machine with horizontal spindle (Type G13P). Aluminum oxide grinding wheel (AA60K5V8) is selected for grinding the MMNC cylindrical specimens. The MQL setup is attached as an alternative to the standard lubrication setup, and a flow rate of 50 mL/h is employed, which is controlled by the adjuster knob. The nozzle is placed between the grinding wheel and the workpiece interface at a distance of 35 mm. The details of the MQL system are presented in Figure 3.
A variable frequency drive (three-phase AC drive) is a type of motor controller, which drives an electric motor by varying the frequency and voltage supplied to the electric motor. The grinding wheel motor is coupled with a VFD setup, where the VFD exclusively runs it with run command and stops it with a stop command. If the VFD is coupled, then the complete control of input power will be exercised only by the VFD. The VFD has a range of 0–50 Hz, where 1 Hz reduces or increases 30 rpm. The maximum grinding wheel rpm is 1500. The complete circuit used for the present VFD is given in Figure 4. The following equation is used to determine the tangential grinding force (Ft) involved in this operation [8].
F t = ( P × 94535 ) / V s
where the tangential grinding force Ft is expressed in Newton, power P is expressed in kW, and wheel speed Vs is expressed in m/min. The workpiece surface temperature is recorded using a fluke-type infrared (Type 8839) thermometer allowing a measuring range of −50 to 1000 °C with an accuracy of ±2 °C. Before carrying out each experiment, the grinding wheel is dressed using a single-point diamond dresser. A surface roughness tester with a tip radius of 2 mm and evaluation and cut-off lengths of 4 mm and 0.8 mm, respectively, is employed for measuring the surface finish of the ground surface. The measurements are taken in the direction perpendicular to the cutting at six different points, and the average value of the readings is considered as the response. The experimental results are presented in Table 4. The schematic representation is presented in Figure 5.

3. Results and Discussion

3.1. Development of RSM-Based Design Models

The surface roughness, cutting force and temperature under MQL-based grinding of composites are examined through Box–Behnken design experimental models based on RSM. RSM is an association of statistical and mathematical techniques for modeling and analyzing uncertainties where the response is affected by restricted variables. Optimizing this response is the main objective of RSM [46].
The quadratic model for surface roughness is presented in Table 5, while Table 6 and Table 7 correspond to the cutting force and temperature separately for various MQL approaches employing cashew nutshell oil+ Nano-TiO2, cashew nutshell oil and SAE20W40. The values of βo depend on the main grinding parameters, as well as investigational anomalies, such as surface finish, environmental conditions and machine vibrations. The experimental results and the βo coefficient indicate that the MQL system based on cashew nutshell oil+ Nano-TiO2 for reinforced composites performs better than other systems.
The obtained experimental results are interpreted by applying the analysis of variance (ANOVA) at a significance level of α = 0.05. The ANOVA results for surface roughness are presented in Table 8, while Table 9 and Table 10 present the ANOVA results for the cutting force and temperature, respectively. A low probability value indicates that the models obtained may be considered statistically significant, which is desirable.

3.2. Multi-Objective Optimization Using the Artificial Bee Colony (ABC) Algorithm

Artificial bee colony (ABC) algorithm is a metaheuristic algorithm established in 2005 by Karaboga and Basturk [32], mimicking the natural foraging behavior of honey bee swarms. Compared to other evolutionary algorithms, such as harmony search, differential evolution, particle swarm algorithm, genetic algorithm and simulated annealing algorithm, ABC algorithms have the advantages of fewer parameters, simpler structure and simpler implementation in any complex task [9,33]. The employed bees are mainly responsible for looking at the food sources, as well as sharing the information on food resources with onlooker bees. If the number of food sources cannot be improved over the number of trials, the employed bees and onlooker bees become a scout bee, which generates a new random food source. In the last step in the cycle, when bees abandon the nectar food source, a new food source is randomly allocated to a scout bee and changed with the earlier one. Further, the above three steps are repeated by a programed number of cycles until the program reaches the final criteria. The food source is selected by an artificial observer bee based on the probability related to the food source (Pi), which is calculated by Equation (1):
P i = f i t i SN n = 1 f i t n
where fiti represents the exact quantity of solution i, which is relative to the quantity of the food source nectar in location i, and SN is the number of food sources relative to the sum of the observer or employed bees. A candidate food location is generated in the process from the old one in the memory using Equation (2).
v ij = x ij + Φ ij ( x ij x kj )
where k ∈ 1, 2, …SN and j ∈ 1, 2, …D are randomly selected items. Φij is a random number in a range from −1 to 1, which governs food source creation regarding Xij and evaluates two food locations based on a reducing food source of Xij. The new food source discovered by the scout bees replaces the nectar providing the present food source.
If the abandoned source is assumed as xi and j ∈ {1,2,….D}, the new food source discovered by the scout bees will replace xi. Equation (3) calculates the bees performance.
x i j =   x min j + rand [ 0 ,   1 ] ( x max j   x min j )
After creating the Vij position for every candidate food source, the artificial bee evaluates it and then compares the performance with the former one. If the nectar of the new food source is better when compared with the previous one, then the new food source will replace the old one.
In a multi-objective optimization, instead of treating the responses individually, all the responses are optimized simultaneously [35]. However, both single-objective optimization and multiple-objective optimization are carried out in order to develop multi-objective function (MOF). The multi-objective optimization model is formulated for demonstrating and validating the multi-objective ABC algorithm to optimize the grinding process parameters as follows.
Objective 1: Minimization of surface roughness
Surface roughness = −6.41215 − 1.52907 × w +0.017042 × n +0.065639 × d − 7.76648 × 10−3 × v + 6.26783 × 10−4 × w × n − 5.79756 × 10−3 × w × d + 1.35475 × 10−3 × w × v + 6.23725 × 105 × n × d + 1.21684 × 10−5 × n × v − 2.76480 × 10−6 × d × n + 0.12710 × w2 − 8.82632 × 10−6 × n2 − 3.09372 × 103 × d2 − 2.99332 × 10−5 × v2
Objective 2: Minimization of temperature
Temperature = +38.95727 − 3.81033 × w + 0.091072 × n − 1.37438 × d − 0.30353 × v +6.69773 × 10−3 × w × n − 0.031256 × w × d − 0.031270 × w × v + 1.72478 × 10−4 × n × d − 2.22106 × 10−5 × n × v + 9.61598 × 10−3 × d × v − 0.17044 × w2 − 4.46191 × 10−5 × n2 − 0.012329 × d2 + 5.53720 × 10−4 × v2
Objective 3: Minimization of cutting force
Cutting force = −45.44908 + 3.00705 × w + 0.19920 × n − 0.21707 × d − 0.028194 × v − 2.36391 × 10−3 × w × n − 3.93984 × 10−3 × w × d − 0.011355 × w × v − 5.25260 × 10−6 × n × d + 8.12632 × 10−6 × n × v + 8.23413 × 10−4 × d × n + 0.38692 × w2 − 9.38397 × 10−5 × n2 + 2.26018 × 10−3 × d2 + 8.19636 × 10−5 × v2
Based on the above objective considerations, the following multi-objective function is developed, and it is presented in Equation (4):
Min(z1) = w1Yu(SR)/SRmin + w2Yu(Temp)/Tempmin + w3Yu(CF)/CFmin
where Yu(SR), Yu(Temp) and Yu(CF) are the quadratic responses of the second order, as represented by the equation. SRmin, Tempmin and CFmin are the minimum values obtained from single-objective optimization using the ABC algorithm. w1, w2 and w3 are the weight values of each response. Weight values for anything can be provided; however, usually, this can be set by the process engineer depending on the significance of each response and must be equal to w1 + w2 + w3 = 1. Therefore, according to the present investigation, w1 = w2 = w3 = 0.333. The mathematical model developed based on the multi-objective function is
MOO = −1.679158412 − 0.548057133 × x(1) + 0.007340723 × x(2) + 0.004611971 × x(3) − 0.005384516 × x(4) + 0.000279656 × x(1) × x(2) − 0.002031593 × x(1) × x(3) + 2.8599 × 10−5 × x(1) × x(4) + 1.99025 × 10−5 x(2) × x(3) + 3.45841 × 10−6 × x(3) × x(4) + 9.63241 × 10−5 x(3) × x(4) + 0.04598869 × x(1)2 − 3.76321 × 10−6 × x(2)2 − 0.001006651 × x(3)2 − 3.07065 × 10−6 × x(4)2
The computer code is developed for the ABC algorithm using MATLAB with the following controlling factors chosen for obtaining the smooth parametric optimization problem convergence. The ABC algorithm controlling parameters are as follows:
Number of scout bees: 1,
Number of onlooker bees: 50,
Number of food sources: 25,
Number of iterations: 100.
The results obtained from the ABC algorithm are compared to the previous research works to confirm the adequacy of the results. The results obtained using the ABC algorithm are presented in Table 11, and the iteration chart is presented in Figure 6. The optimized result shows that the nano-filled MQL could significantly reduce grinding forces and enhance surface quality. Nano-fluid-exposed grinding showed improved tribological and thermophysical properties and reduced the cutting forces and surface roughness, cutting zone temperature and tool wear [5].

3.3. Influence of Grinding Parameters on Responses

The 3D surface plots of models fitted for surface roughness on grinding parameters are shown in Figure 7. The wheel velocity is found to be a prominent influencing factor that affects surface roughness. The increase in wheel velocity reduces the chip thickness, justifying the prominent wheel velocity effect on surface roughness [47]. The ground surface quality of aluminum composites is influenced by the reinforcement particles. In all grinding conditions, the surface roughness decreases as the wt % of nano-SiC increases. The hardness, heat resistance and tensile strength were increased by the addition of nano-SiC in an Al matrix [48]. The experimental result shows the reduction in surface roughness as the wt % of nano-SiC increases. Suna et al. established that surface roughness increases with an increase in the depth of cut. When the depth of cut is increased, the increase in material removal rate and the increase in chip thickness account for the increase in surface roughness values [49]. Thiyagarajan et al. found that surface roughness decreases with an increase in wheel velocity and workpiece velocity. This is due to the increase in relative velocity between the wheel and the workpiece and the reduction in contact time, thereby reducing chip thickness[17]. Tawakoli et al. discussed that increasing the depth of cut and the wheel speed leads to an increase in the penetration of each grain in the workpiece in the contact zone, lower amounts of uncut chips left on the ground surface, and consequently, lower surface roughness can be observed in MQL grinding compared to dry grinding.
The experiments indicated a reduction in surface roughness with an increase in wt % of nano-SiC. The hardness of the composites greatly affects surface roughness and thereby improves MMNC grinding surface finish [30].
Figure 8 presents the 3D surface plots of models fitted for the cutting force on grinding factors. One of the most important parameters is the tool life, which regularly decreases as the cutting force and depth of cut increase owing to increased stress and contact of the workpiece. More cutting force is generated with rapid tool advancement and vibrations resulting in an increase in surface roughness Surface roughness is increased as a result of high cutting force, which is generated through vibrations and rapid tool movement [50] The experiments point out that the tangential force is decreased when the wheel speed and workpiece speed are increased. When the depth of cut increases, the tangential grinding force and surface roughness are increased. The MQL system based on cashew nutshell oil+ Nano-TiO2 attained the best performance, as the MQL system lubricant infiltrates the wheel and the workpiece boundary contact region [51]. Thiyagarajan et al. concluded that the tangential grinding force decreases with an increase in wheel velocity and workpiece velocity. This could be attributed to the thermally induced softened matrix at high speeds. As the grinding wheel velocity increases, the heat generated in the deformation zone increases, thereby softening the aluminum matrix, thus reducing the force required to remove the material [17]. Cong Mao et al. found that MQL grinding has a lower grinding force in comparison with dry grinding. This demonstrates that the MQL system can penetrate the contact zone between the workpiece and the wheel to lubricate the contact zone [32]. Dinesh et al. found that the grinding forces in the case of nano-fluid MQLs decreased effectively in both normal and tangential directions. It is observed from the experiments that the increase in workpiece speed and wheel speed resulted in a decrease in the tangential grinding force, while the increase in depth of cut increased the tangential grinding force and surface roughness. The force reduction in the grinding processes could be attributed to the enhanced lubrication and cooling characteristics of the nano-cutting fluid [27].
Three-dimensional surface plots of models fitted for the temperature on grinding parameters are shown in Figure 9. The heat resistance, hardness and tensile strength are influenced by the addition of nano-SiC in the Al matrix; the temperature decreases with the increase in wt % of nano-SiC [52] The MQL grinding process is compared with dry grinding results, the benefits of reducing the grinding forces, improving surface roughness and preventing the workpiece from burning. MQL grinding could remarkably lower the grinding temperature [53]. It is evident from the experimental results that the grinding temperature is decreased with the increase in wt % of nano-SiC particles. The lubricant filled with nano-particles plays a vital role in the actual grinding process by carrying heat throughout the process, thereby improving the surface finish. Thiyagarajan et al. found that the grinding temperature increases with an increase in wheel velocity, workpiece velocity and depth of cut. The higher values of wheel velocity, workpiece velocity and depth of cut result in higher grinding temperature due to the increase in the energy required to grind a unit volume of the metal [17].
Cong Mao et al. found that the temperature in dry grinding is considerably higher than that in MQL grinding. The best performance obtained by cashew nutshell oil+ Nano-TiO2 based MQL is mainly due to the lubricity of the utilized fluid and the decrease in grinding temperature. This indicates that the heat-collecting ability of the nano-fluid is increased. This is due to the lack of lubrication and cooling for dry grinding [54]. Cong Mao et al. found that the grinding temperature decreases with an increase in air pressure, which enhances the nano-fluid mist in penetrating the grinding zone [32]. Thiyagarajan et al. found that the grinding temperature increases with an increase in wheel velocity, workpiece velocity and depth of cut. The higher values of wheel velocity, workpiece velocity and depth of cut result in higher grinding temperature due to the increase in the energy required to grind a unit volume of the metal [17].

3.4. Surface Morphology of the Machined Surface

Scanning electron microscopy (SEM) was employed for observing and analyzing the machined surface morphologies. Figure 10 presents the micro-graphs obtained through SEM. The increased depth of cut and cutting force resulted in the fragmentation of Al2O3 grains of the grinding wheel and implantation on the workpiece surface. The workpiece under SAE20W40 lubrication exhibits grinding wheel marks on the surface and poor surface finish, which is attributed to the high depth of cut. The grinding wheel material plunges into the workpiece surface as a result of the increased depth of cut [55]. Zhu et al. found that the interface strength and interior cracks of silicon carbide are the causes of cavity defects [56]. Guoqiang et al. proved that surface topography is related to the feeding velocity and grinding depth (the higher the feeding velocity and grinding depth, the more uneven the surface topography), and the impact caused by the feeding velocity is more significant than that caused by the grinding depth [14]. It is distinctly clear from the micro-graph that the surface finish is excellent under MQL based on cashew nutshell oil + Nano-TiO2. Micro-cracks are formed on the machined surface due to the difference in thermal expansion between the nano-SiC particles and Al.

3.5. Surface Morphology of Grinding Wheel

The grinding wheel is examined using SEM under different MQL systems, which are presented in Figure 11. The grinding wheel contains capillary networks or holes, which act as micro-reservoirs to store the nano-fluids temporarily [57]. The TiO2 nano-particles are carried into the grinding zone by these micro-reservoirs; in addition, a vast volume of nano-fluids can be delivered by high-porosity grinding wheels. A lubricating film layer of the TiO2 nano-fluid can be formed on the abrasive grain surfaces through chemical and physical reactions of nano-fluids. The wear flattening generation is prevented and effectively forced to drop from the abrasive grains by this layer. Adibi et al. found that dry grinding has the highest value of wear flattening area, resulting in rapid blunting of abrasive grains and high amounts of grinding forces [28]. Nevertheless, the wear flattening areas on the wheel’s surface after MQL grinding and fluid grinding are roughly similar to each other.
Further, a good bearing in the grinding region is provided by the spherical appearance and nano-size of the TiO2 nano-particles. The contact area between the workpiece and grains is narrowed, thereby reducing grain wear flattening; friction pair direct contact is prevented, and the friction between the workpiece and abrasive grains is changed from sliding friction to rolling friction by the TiO2 nano-particles. The wear rate of the grinding wheel and consumption of energy are lower due to the reduction in the force ratio. The presence of TiO2 nano-particles is proven by the existence of elemental Ti and O on the abrasive grain surface through the confirmation from SEM and EDS analysis. A lubricating film is formed by TiO2 nano-particles under external pressure and temperature.

3.6. AFM Analysis of Surface Roughness

Surface roughness is quantitatively measured, and the nano-texture of the nano-composite surface is visualized through atomic force microscopic analysis [58]. Figure 12 represents the three-dimensional AFM image of MMNC using SAE20W40, cashew nutshell oil and cashew nutshell oil with Nano-TiO2, respectively. The average surface roughness of MMNC using Nano-TiO2 as lubricating oil is observed to decrease from 226.5 nm to 108.3 nm. The air stream in the MQL system eases the dispersion of TiO2 nano-particles in the contact region of abrasive grits and metal. The interaction between the cashew nutshell oil and MMNC supports the formation of an appropriate lubrication film, which provides a viscose medium for solid TiO2 nano-particles. This condition improves the rolling action of nano-particles and results in the improved surface quality of MMNC through the creation of a smooth surface. Mohammadreza et al. found that, in CuO nano-fluid MQL grinding, the rolling action of the nano-particles generates a smooth surface even at a high depth of cut and linear velocity of the workpiece [21]. Hence, it is confirmed from the AFM analysis that the resistance against surface roughness for MMNC is improved by using Nano-TiO2 in the MQL system.

4. Conclusions

The present paper deals with the investigation and optimization of machining parameters, such as the wheel speed, speed of the workpiece, depth of cut and wt % of nano-SiC particles, under different MQL environments, such as SAE20W40, cashew nutshell oil and cashew nutshell oil + Nano-TiO2, in the grinding of Al matrix nano-composites. The major conclusions drawn from the present investigation are as follows:
  • Aluminum matrix nano-composites reinforced by nano-SiC are successfully synthesized using an ultrasonic cavitation-based solidification process, and the characterization of nano-composites is performed using SEM with EDX and IR spectroscopy.
  • The properties of the nano-filled lubricant are compared with SAE20W40 and cashew nutshell oil. An improvement in the properties is observed in Tio2 mixed cashew-nut-based vegetable oil. The wear scar diameter is very low in veg oil + TiO2 compared to SAE20W40 and veg oil.
  • The MQL method is utilized economically in the grinding process, decreasing the coefficient of friction, providing diffusion of oil mist in the machining interface and reducing the tangential forces considerably.
  • The ABC algorithm is employed to optimize the sustainable grinding factors of composites reinforced with nano-SiC particles. The results of the optimization technique determine that the following parameters (wheel speed = 910 rpm, depth of cut = 29.42 μm, wt % of nano-SiC = 2.9, workpiece speed = 78 rpm, MQL type = veg oil + TiO2) are favorable for reducing the cutting force, temperature and surface roughness.
  • Nano-fluid MQL reveals a reduction in the cutting force and temperature. The addition of Nano-TiO2 in cashew nutshell oil improves the performance through enhanced cooling effects and lubrication by their greater diffusion and entrapment at the contact zone.
  • A lubricating film layer of the TiO2 nano-fluid can be formed on the abrasive grain surfaces through chemical and physical reactions of the nano-fluids. The wear flattening generation is prevented and effectively forced to drop from the abrasive grains by this layer.
  • It is distinctly clear from the micro-graph that the nano-SiC particles are disintegrated and dragged away from the surface. The difference in thermal expansion among the nano-SiC particles and Al generates heat, resulting in the formation of micro-cracks on the machined surface.

Author Contributions

A.N. Preparation of Composites and complete machining part, T.R. Surface Integrity Analysis S.V. Morphology analysis; D.V. Artificial Bee Colony (ABC) algorithm. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

The authors affirm that there is no external data and images incorporated in the present project for publication. Informed consent was obtained from all individual authors included in the present study.

Data Availability Statement

There is no data available in this project.

Conflicts of Interest

All authors have no conflict of interest to report. Ensure that accepted principles of ethical and professional conduct have been followed in the present research.

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Figure 1. (a) SEM with EDX of nano-SiC reinforced aluminum matrix composites. (b) IR spectroscopy analysis of nano-SiC reinforced aluminum matrix composites.
Figure 1. (a) SEM with EDX of nano-SiC reinforced aluminum matrix composites. (b) IR spectroscopy analysis of nano-SiC reinforced aluminum matrix composites.
Suschem 03 00030 g001
Figure 2. (a) Four-ball wear test for SAE20W40. (b) Four-ball wear test for veg oil. (c) Four-ball wear test for veg oil + TiO2.
Figure 2. (a) Four-ball wear test for SAE20W40. (b) Four-ball wear test for veg oil. (c) Four-ball wear test for veg oil + TiO2.
Suschem 03 00030 g002aSuschem 03 00030 g002b
Figure 3. Details of MQL setup.
Figure 3. Details of MQL setup.
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Figure 4. Circuit diagram of VFD setup.
Figure 4. Circuit diagram of VFD setup.
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Figure 5. Schematic arrangement of experimental setup.
Figure 5. Schematic arrangement of experimental setup.
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Figure 6. Iteration chart using ABC algorithm.
Figure 6. Iteration chart using ABC algorithm.
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Figure 7. (a) 3D response plot for SR on wt % of nano-SiC vs. depth of cut under SAE20W40. (b) 3D response plot for SR on wt % of nano-SiC vs. depth of cut under veg oil. (c) 3D response plot for SR on wt % of nano-SiC vs. depth of cut under veg oil + TiO2.
Figure 7. (a) 3D response plot for SR on wt % of nano-SiC vs. depth of cut under SAE20W40. (b) 3D response plot for SR on wt % of nano-SiC vs. depth of cut under veg oil. (c) 3D response plot for SR on wt % of nano-SiC vs. depth of cut under veg oil + TiO2.
Suschem 03 00030 g007aSuschem 03 00030 g007b
Figure 8. (a) 3D response plot for CF on wt % of nano-SiC vs. depth of cut under SAE20W40. (b) 3D response plot for CF on wt % of nano-SiC vs. depth of cut under veg oil. (c) 3D response plot for CF on wt % of nano-SiC vs. depth of cut under veg oil + TiO2.
Figure 8. (a) 3D response plot for CF on wt % of nano-SiC vs. depth of cut under SAE20W40. (b) 3D response plot for CF on wt % of nano-SiC vs. depth of cut under veg oil. (c) 3D response plot for CF on wt % of nano-SiC vs. depth of cut under veg oil + TiO2.
Suschem 03 00030 g008aSuschem 03 00030 g008b
Figure 9. (a) 3D response plot for TEMP on wt % of nano-SiC vs. wheel speeds under SAE20W40. (b) 3D response plot for TEMP on wt % of nano-SiC vs. wheel speed under veg oil. (c) 3D response plot for TEMP on wt % of nano-SiC vs. wheel speed under veg oil + TiO2.
Figure 9. (a) 3D response plot for TEMP on wt % of nano-SiC vs. wheel speeds under SAE20W40. (b) 3D response plot for TEMP on wt % of nano-SiC vs. wheel speed under veg oil. (c) 3D response plot for TEMP on wt % of nano-SiC vs. wheel speed under veg oil + TiO2.
Suschem 03 00030 g009aSuschem 03 00030 g009b
Figure 10. (a) Micro-structure of machined surface under SAE20W40. (b) Micro-structure of machined surface under veg oil. (c) Micro-structure of machined surface under veg oil + TiO2.
Figure 10. (a) Micro-structure of machined surface under SAE20W40. (b) Micro-structure of machined surface under veg oil. (c) Micro-structure of machined surface under veg oil + TiO2.
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Figure 11. (a) Micro-structure of grinding wheel under SAE20W40. (b) Micro-structure of grinding wheel under veg oil. (c) Micro-structure of grinding wheel under veg oil + TiO2.
Figure 11. (a) Micro-structure of grinding wheel under SAE20W40. (b) Micro-structure of grinding wheel under veg oil. (c) Micro-structure of grinding wheel under veg oil + TiO2.
Suschem 03 00030 g011aSuschem 03 00030 g011b
Figure 12. (a) AFM analysis of machined surface under SAE20W40. (b) AFM analysis of machined surface under veg oil. (c) AFM analysis of machined surface under veg oil + TiO2.
Figure 12. (a) AFM analysis of machined surface under SAE20W40. (b) AFM analysis of machined surface under veg oil. (c) AFM analysis of machined surface under veg oil + TiO2.
Suschem 03 00030 g012aSuschem 03 00030 g012b
Table 1. Details of aluminum and nano-SiC.
Table 1. Details of aluminum and nano-SiC.
SL. NO.MaterialSizeSupplierPurity
1AluminumBilletM/s Micro Fine chemicals, India99.9%
2Nano-SiC50–80 nmM/S US Research Nanomaterials Inc99%
Table 2. Properties of lubricants used.
Table 2. Properties of lubricants used.
PropertiesSAE20W40Vegetable Oil
(Cashew Nutshell Oil)
Vegetable Oil
(Cashew Nutshell Oil + TiO2)
Flash Point (°C)
[ASTM D92]
200214.27190.2
Thermal Conductivity
Watt/mK
0.1520.1610.169
Viscosity @100 °C (cSt) [ASTM D445]15.215.4816
Viscosity Index [ASTM D2270]120126158
Table 3. Grinding parameters and their levels.
Table 3. Grinding parameters and their levels.
Sl.NoParametersNotationUnitLevels
123
1Wheel speednrpm90012001500
2Depth of cutdμm102030
3Workpiece speedvrpm80150270
4wt % of nano-SiCw%123
5Type of MQL--SAE 20W40VEG OILVEG OIL + TiO2
Table 4. Experimental results.
Table 4. Experimental results.
Sl. NoWeight
%
Wheel
Speed
Depth
of Cut
Workpiece
Speed
Surface RoughnessTemperatureCutting Force
TiO2Veg OilSAE
20W40
TiO2Veg OilSAE
20W40
TiO2Veg OilSAE
20W40
11900201752.012.0601.91143.3044.38245.49252.5453.85355.199
23900201751.451.4861.22334.8535.72136.61453.3854.71456.082
311500201751.461.4961.83337.4038.33539.29336.9337.85338.799
431500201751.121.1482.11736.6037.51538.45235.0735.94636.845
52120010801.741.7831.82855.6056.99058.41454.3855.73956.133
62120030801.861.9061.95437.0037.92538.87352.0153.31054.643
721200102701.581.6191.65936.8237.74038.68452.6453.95655.304
821200302701.691.7321.77553.0054.32555.68354.2055.55556.943
91120020802.502.5622.62646.6947.85749.05352.1753.47454.811
103120020801.962.0092.05947.0048.17549.37952.4953.80256.147
1111200202702.012.0602.11152.0053.30054.63254.1255.47356.998
1231200202701.962.0092.05941.0042.02543.07552.3753.67955.021
132900101750.910.9321.25638.5539.51340.50152.7554.06855.420
1421500101751.091.1171.14536.4037.31038.24235.0835.95737.855
152900301750.940.9630.98736.7837.69938.64252.8054.12055.473
1621500301751.881.9271.62536.6037.51538.45235.0735.94637.845
1711200101752.502.5622.22642.7143.77744.87253.0054.32555.683
1831200101751.982.0292.08038.7039.66740.65952.3853.68955.031
1911200301752.282.3372.39543.0044.07545.17653.1754.49955.861
2031200301751.921.9682.01737.8038.74539.71352.4053.71055.052
21290020801.191.2191.55044.1445.24346.37453.1554.47855.840
222150020801.121.1481.17643.3544.43345.54436.0036.90037.822
232900202700.690.7070.72443.5044.58745.70255.7957.18455.691
2421500202701.751.7931.73840.3041.30742.34036.9337.85338.799
2521200201752.202.2552.32141.8042.84543.94652.1753.47454.836
2621200201752.202.2552.41341.8042.84543.91652.1753.47453.676
2721200201752.202.2552.39141.8042.84543.92752.1753.47454.795
2821200201752.202.2552.33141.8042.84544.90552.1753.47453.678
2921200201752.202.2552.38141.8042.84543.91652.1753.47454.811
Table 5. Model for surface roughness.
Table 5. Model for surface roughness.
Sl. No.Type of MQL SystemModel for Surface Roughness
1SAE 20W40+2.36778 − 0.129083854 × w + 0.165268625 × n + 0.046581542 × d − 0.093744271 × v + 0.2430275 × w × n − 0.057975625 × w × d + 0.128701563 × w × v + 0.187117563 × n × d + 0.346799688 × n × v − 0.002626562 × d × v + 0.14542749 × w2 − 0.776037792 × n2 − 0.318537167 × d2 − 0.279312198 × v2
2Vegetable Oil
(Cashew Nutshell Oil)
2.255 − 0.20244 × w + 0.105063 × n + 0.065771 × d − 0.05894 × v + 0.056375 × w × n + 0.041 × w × d +0.125563 × w × v +0.19475 × n × d + 0.289563 × n × v − 0.00256 × d × v + 0.147771 × w2 − 0.81829 × n2 − 0.20842 × d2 − 0.24942 × v2
3Cashew Nutshell Oil + TiO22.2 − 0.1975 × w + 0.1025 × n + 0.064167 × d − 0.0575 × v + 0.055 × w × n + 0.04 × w × d + 0.1225 × w × v + 0.19 × n × d + 0.2825 × n × v − 0.0025 × d × v + 0.144167 × w2 − 0.79833 × n2 − 0.20333 × d2 − 0.24333 × v2
Table 6. Model for cutting force.
Table 6. Model for cutting force.
Sl. No.Type of MQL SystemModel for Cutting Force
1SAE 20W4054.35938 − 0.26438 × w − 8.81162 × n +0.032553 × d + 0.280113 × v −0.70917 × w × n − 0.0394 × w × d − 0.82824 × w × v − 0.01576 × n × d +0.2816 × n × v +0.782242 × d × v +0.540918 × w2 − 8.19133 × n^2 +0.505257 × d2 + 0.868723 × v2
2Vegetable Oil
(Cashew Nutshell Oil)
53.47425 − 0.328 × w − 8.99694 × n − 0.04954 × d + 0.499688 × v − 0.69188 × w × n − 0.03844 × w × d −0.53044 × w × v −0.01537 × n × d −0.43819 × n × v + 1.007063 × d × v + 0.136667 × w2 − 8.13124 × n^2 + 0.118729 × d^2 + 0.934885 × v^2
3Cashew Nutshell Oil + TiO252.17 − 0.32 × w − 8.7775 × n − 0.04833 × d + 0.4875 × v − 0.675 × w × n − 0.0375 × w × d − 0.5175 × w × v −0.015 × n × d −0.4275 × n × v + 0.9825 × d × v + 0.133333 × w2 − 7.93292 × n2 + 0.115833 × d2 + 0.912083 × v2
Table 7. Model for temperature.
Table 7. Model for temperature.
Sl. No.Type of MQL SystemModel for Temperature
1SAE 20W4044.12217 − 2.55214 × w − 0.91667 × n − 0.40274 × d − 0.62687 × v + 2.00932 × w × n − 0.31256 × w × d − 2.97064 × w × v + 0.517433 × n × d − 0.633 × n × v + 9.135184 × d × v − 0.17044 × w2 − 4.01572 × n2 − 1.23288 × d2 + 4.997327 × v2
2Vegetable Oil
(Cashew Nutshell Oil)
42.845 − 2.4899 × w − 0.89431 × n − 0.39292 × d −0.61158 × v + 1.960313 × w × n − 0.30494 × w × d − 2.89819 × w × v + 0.504813 × n × d − 0.61756 × n × v + 8.912375 × d × v − 0.06577 × w2 − 3.81727 × n2 − 1.1023 × d2 + 4.975948 × v2
3Cashew Nutshell Oil + TiO241.8 − 2.42917 × w − 0.8725 × n − 0.38333 × d − 0.59667 × v + 1.9125 × w × n −0.2975 × w × d − 2.8275 × w × v + 0.4925 × n × d − 0.6025 × n × v + 8.695 × d × v −0.06417 × w^2 − 3.72417 × n2 − 1.07542 × d^2 + 4.854583 × v2
Table 8. ANOVA for surface roughness.
Table 8. ANOVA for surface roughness.
SAE20W40
SourceSum of SquaresdfMean SquareF-Valuep-Value Prob > F
Model6.562166140.468726136.9680487<0.0001significant
Residual0.04791140.003422
Lack of Fit0.041611100.0041612.6423034780.1809not significant
Pure Error0.00629940.001575
Cor Total6.61007628
Veg Oil
Model6.434834140.4596317.1057105880.0004significant
Residual0.905586140.064685
Lack of Fit0.905586100.090559
Pure Error040
Cor Total7.3404228
Veg Oil + TiO2
Model6.124767140.4374837.1057110.0004significant
Residual0.86195140.061568
Lack of Fit0.86195100.086195
Pure Error040
Cor Total6.98671728
Table 9. ANOVA for cutting force.
Table 9. ANOVA for cutting force.
SAE20W40
SourceSum of SquaresdfMean SquareF-Valuep-Value Prob > F
Model1450.22514103.5875819.91239<0.0001significant
Residual1.768756140.12634
Lack of Fit0.2169100.021690.05590740.9999not significant
Pure Error1.55185540.387964
Cor Total1451.99328
Veg Oil
Model1473.20214105.2287322.9143<0.0001significant
Residual4.562208140.325872
Lack of Fit4.562208100.456221
Pure Error040
Cor Total1477.76428
Veg Oil + TiO2
Model1402.21514100.1582322.914327<0.0001significant
Residual4.342375140.31017
Lack of Fit4.342375100.434238
Pure Error040
Cor Total1406.55728
Table 10. ANOVA for temperature.
Table 10. ANOVA for temperature.
SAE20W40
SourceSum of SquaresdfMean SquareF-Valuep-Value Prob > F
Model824.24091458.87434862226.0325839<0.0001significant
Residual3.646558140.260468414
Lack of Fit2.879526100.2879525891.5016459420.3700not significant
Pure Error0.76703240.191757977
Cor Total827.887428
Veg Oil
Model784.34931456.02495286.1780157<0.0001significant
Residual2.740774140.19577
Lack of Fit2.740774100.274077
Pure Error040
Cor Total787.090128
Veg Oil + TiO2
Model746.5551453.32536286.178<0.0001significant
Residual2.608708140.186336
Lack of Fit2.608708100.260871
Pure Error040
Cor Total749.163728
Table 11. Multi-objective optimization results for grinding process.
Table 11. Multi-objective optimization results for grinding process.
Parameters and Objective FunctionValue
TiO2Veg OilSAE20W40
Percentage of weight2.9024
Wheel speed rpm910.0719
Depth of cut μm29.4243
Workpiece speed rpm78.6531
Surface roughness μm1.011341.10141.15263
Temperature in deg c34.160935.580134.83
Cutting force in N36.211537.142437.1465
Multi-objective function (Z1)0.99145
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Nandakumar, A.; Rajmohan, T.; Vijayabhaskar, S.; Vijayan, D. Sustainable Grinding Performances of Nano-Sic Reinforced Al Matrix Composites under MQL: An Integrated Box–Behnken Design Coupled with Artificial Bee Colony (ABC) Algorithm. Sustain. Chem. 2022, 3, 482-510. https://doi.org/10.3390/suschem3040030

AMA Style

Nandakumar A, Rajmohan T, Vijayabhaskar S, Vijayan D. Sustainable Grinding Performances of Nano-Sic Reinforced Al Matrix Composites under MQL: An Integrated Box–Behnken Design Coupled with Artificial Bee Colony (ABC) Algorithm. Sustainable Chemistry. 2022; 3(4):482-510. https://doi.org/10.3390/suschem3040030

Chicago/Turabian Style

Nandakumar, A., T. Rajmohan, S. Vijayabhaskar, and D. Vijayan. 2022. "Sustainable Grinding Performances of Nano-Sic Reinforced Al Matrix Composites under MQL: An Integrated Box–Behnken Design Coupled with Artificial Bee Colony (ABC) Algorithm" Sustainable Chemistry 3, no. 4: 482-510. https://doi.org/10.3390/suschem3040030

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