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Scientific Reports volume 15, Article number: 1598 (2025 ) Cite this article cnc plasma cutting
Higher-end science and technology facilitate the human community with a sophisticated life despite it curses by abundant pollution. The alarming demand for sustainability pressurizes the manufacturing sector to ensure sustainable manufacturing. Since Molybdenum di sulfide (MoS2) and avocado oil are known solid and liquid lubricants respectively, hence, it is a worthwhile attempt to implement the bio-based degradable avocado oil enriched with nano Molybdenum di sulfide (nMoS2) particles as a potential machining fluid for CNC-end milling. Different proportions of avocado oil and nMoS2 were used to synthesise four distinct machining fluids to assess the individual impact of avocado oil and nMoS2 particles. The emulsification and sonication were employed to synthesise the fluids. A hybrid Grey Relational Analysis (GRA) coupled with Principal Component Analysis (PCA) was followed to scrutiny the effect of novel machining fluid on machining objectives. The experimental results of physio-chemical properties revealed that avocado-rich 0.5% nMoS2 excels among others. The L16 orthogonal array experiments associated with statistical analysis explored the developed machining fluid (A6W4/0.5) that significantly impacts the machining objectives. The experimental results manifest that nearly 64.87% of surface roughness and 93.3% of tool wear have been reduced during machining in the presence of A6W4/0.5 fluid than A4W6/0.75. The improved performance of the novel machining fluid upholds its potential to replace conventional fluids and ensure green manufacturing.
A few million tons of machining fluids are yearly consumed by manufacturing industries all around the globe, out of which more than 80% are petroleum-based, and the remaining are also synthetic1. Primarily, petroleum is non-renewable, non-biodegradable and expensive. The cost involved in handling these fluids surpasses 15–17% of the production cost2, which poses a challenge to survival in the market. Additionally, the report3 revealed that the manufacturing sectors are spending 5–16% of production costs for the disposal of these fluids, subsequently, the industries have to follow a specific method of disposal, in which human and environmental safety is ensured. Apart from the non-degradability, the continuous usage of these anti-environmental fluids causes respiratory disease, skin problems, cancer risks and neurological issues to the operators4,5. However, the global agenda focuses on a sustainable environment and recycling waste. It is well-comprehended that the existing petroleum or synthetic machining fluids will no longer be in practice. It seems urgent to develop a compatible bio-degradable machining fluid, which assures cost-effectiveness, environment and operator-friendly. Consequently, many research initiatives6,7,8,9 have already been reported indicating vegetable-based oils as a potential replacement. Simultaneously, few research attempts10,11,12,13,14 were reported on the successful implementation of Minimum Quantity Lubrication (MQL), wherein less fluid consumption was recorded and the production cost was controlled. A few noticeable alternative measures, such as dry machining15 associated with advanced ceramic tool coating technology and cryogenic cooling16, were reported at the expense of added production costs. However, the involvement of mere vegetable oil also dented the performance owing to their low-temperature operation and lower thermal stability. Meanwhile, the researchers were initiated to develop nano bio-degradable machining fluid by incorporating the right nanomaterials to enhance the performance of the vegetable-based machining fluid over the machining objectives. A significant number of research attempts were made with varieties of vegetable-based oils such as Coconut oil, palm oil, Jatropha oil, sesame oil, rapeseed oil, sunflower oil, etc. infused with copper17, silver18, boric acid19, graphite20 and molybdenum di sulfide micro21 or nanoparticles22. The effect of machining variables along with the effect of the percentage of inclusion of nanoparticles within the vegetable-based machining fluids have been investigated with the implementation of conventional statistical techniques such as Taguchi’s robust design23, Grey Relational Analysis (GRA)19,24, Response Surface Methodology (RSM)25, Principal Component Analysis26 and soft computing techniques like Artificial Neural Network (ANN)27, Genetic Algorithm (GA)28, Machine Learning Algorithm (MLA)29, swarm intelligence30, GEP-PSO hybrid optimization14, Multi-Objective Particle Swarm Optimization (MOPSO)-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)31 etc. This research utilises the unexplored avocado oil as a base fluid to synthesize a nMoS2 soaked machining fluid. An avocado is a single-seed fruit cultivated all over the globe with a production scale of 11.31 million tons1. Usually, a cold-pressing technique is followed to extract the oil from the fruit whose pulps are more than 60% richer in oil content. It has a higher viscosity than coconut oil, sunflower oil, peanut oil, and sesame oil at 250 C and is thermally stable with a high smoke point of more than 2720 C32,33, hence these improved physical properties and its thermal stability make the avocado oil as a suitable candidate for cutting fluid applications to ensure the sustainability in manufacturing. The increasing usage of vegetable-based edible oil in the manufacturing sector to lubricate the tool-work interface was exhaustively reviewed along with the possible blending, modification and incorporation of nanoparticles to improve the machining performance7. The complete comprehensive review conducted by the9, manifested the physiochemical properties of distinct plant oil-based metalworking fluids and also declared the better machinability performance of different vegetable-based fluids than the conventional petroleum and synthetic-based fluids. Elsheikh et al.34 conducted turning experiments based on Taguchi’s L16 orthogonal array (OA) by implementing a rice bran oil infused with two different nanoparticles Al2O3 and CuO under Minimum Quantity Lubrication (MQL) concept. The GRA concluded that the best performance of CuO-soaked rice bran oil is in minimizing tool wear due to its excellent lubrication effect. The superior lubricative and higher thermal conductivity properties of Multi-Walled Carbon Tubes (MWCNTs) were facilitated to record lesser tool wear and cutting temperatures respectively, while MWCNTs were impregnated with synthetic fluid ethylene glycol by Patole PB35. A GRA-PCA-based hybrid statistical technique was implemented by Li M36, to evaluate the lubrication performance of graphene-infused degradable oil, while milling the TC4 alloy. The ANOVA analysis concluded that the graphene concentration significantly influenced the surface roughness, cutting temperature and tool wear, owing to its lower coefficient of friction, high thermal conductivity, and lubrication properties. Kilincarslan et al.37 prepared the synthetic (ethylene glycol) fluid added with nano additives, silver and boric acid subsequently the authors carried out a milling experiment to assess the lubricating capacity of the machining fluid over the milling force, milling temperature, surface roughness and tool wear. The result analysis performed by RSM background revealed that the hybrid nano silver–boric acid fluid. Agrawal.SM et al.38 engaged in the synthesising of bio-lubricants made out of cotton seed oil and compared its tribological performance with SAE 40 oil, subsequently, the report declared that less wear was recorded for cotton seed oil than SAE 40 oil. The impact of MQL and graphene platelets was investigated by Gong et al.39 when they experimented with the turning of Inconel 718. The authors followed five different cooling conditions such as dry, flood, MQL in the base fluid, MQL (5% graphene), and MQL (15% graphene). The MQL (5% graphene) registered the lowest surface roughness and thinnest chip thickness than rest of the other cooling systems. Few advanced soft computing modelling40,41 were also reported on the basis to evaluate the ideal parameters.
The present research focuses on synthesising avocado oil-based degradable machining fluid enriched with nano Molybdenum di sulfide (nMoS2) and evaluating the machining performance of the fluid over the key machining objectives like surface roughness, Metal Removal Rate (MRR), and tool wear. The recommendation of synthesised machining fluid is primarily to promote green manufacturing to nullify industrial pollution. Sustainability, degradability, cost-saving and operator friendliness are the target outcomes to uphold the significance of the research work. Based on a stringent literature survey, the inclusion of avocado as a possible cutting fluid was not attempted; hence the research gap is bridged. The three different feathers added to the crown of the novelty are the involvement of oil-rich and cheaper avocado oil as a base fluid, enrichment of nMoS2 as a performance-enhancing additive and the implementation of hybrid GRA-PCA statistical model to analyse the influence of nMoS2 blended avocado oil on the surface roughness, MRR and tool wear loss. The attempt to explore the lubricity of nMoS2-enriched avocado oil for end-milling operations along with the hybrid GRA-PCA analysis is never reported elsewhere.
A 5 L of Avacoda oil was procured from the grey market in Addis Ababa Ethiopia. The workpiece used for the experimentation was an AISI 4130r steel plate with a dimension of 100 mm × 100 mm × 25 mm thick. The chemical constituents and mechanical characteristics of the workpiece are supplied in Tables 1 and 2 respectively. A 16 mm diameter four flute HSS end milling cutter (DIN844), purchased from Bekele Machine Tools Addis Ababa was utilized for machining. The molybdenum sulfide nanoplatelets were purchased from Sigma-Aldrich, the size of the platelets was around 100 nm, as authenticated by the supplier.
Since normal drinking water has contamination and micro-organisms, distilled water was employed for the synthesis of machining fluid. The immiscibility which causes due to the discrete-polar nature of water and avocado oil molecules. Lecithin, a natural surfactant emanating from soybean was used to stabilize the distilled water–avocado oil and to form a fine emulsion45. The pre-measured distilled water, avocado oil, and surfactant were taken into a container and placed on the ultrasonic vibrator to mix homogeneously. The mixing time was estimated based on the degree of homogeneity achieved. Based on the literature and experimental attempts shown in Table 3. The duration of the ultrasonic mixing was set to 16 h. Table 3 depicts the details of the quantity of distilled water, avocado oil and surfactant used for the preparation, along with the observation remarks. The complete process cycle of synthesis of avocado oil-distilled water emulsion is depicted in Fig. 1.
Further, a complete nano-machining fluid was synthesised by infusing varying percentages of MoS2 nanoplatelets with further sonication. A premeasured quantity of MoS2 nanoplatelets was mixed with avocado-water emulsion, and the mixture was fed into magnetic sonication for 45 min to ensure the homogenous dispersion of MoS2 nanoplatelets. Five different machining fluids with their percentage of compositions of ingredients were synthesised as shown in Table 4.
Process cycle representing a synthesis of the avocado oil–water emulsion.
A universal CNC milling machine (Tengzhou Runfa Machinery, XQ6232WA) was pressed into service to execute the necessary machining operation in synthesised machining fluid with different designations. The complete specification of the experimental set-up is furnished in Table 5. The nozzle and pump with controller facilitate the Minimum Quantity of Lubrication (MQL) of 15 ml/min for all experiments. Three experimental cuts are machined at each designated machining fluid, subsequently, the average values are taken into account for the machining responses like Metal Removal Rate (MRR), surface roughness and tool wear. The machining was done in compliance with Taguchi’s design of experiments. The machining variables and their levels considered for the experimentation are given in Table 6.
It is imperative to assess the nature of the machining fluid, whether it is acidic or alkaline. A pH meter (Mettler Toledo) was employed to measure the pH value. A flash and fire point of the machining fluids were investigated with the help of a Pensky–Marten’s flash point tester (Flash Pointer 34000-0 Multi-flash with 34100-2 Pensky Martens Test Module) as per ASTM D-93 standard. The viscosity of the synthesised nano machining fluid was estimated by Oswald kinematic viscometer in the temperature between 40 °C and 100 °C. Thermal conductivity of the machining fluid was measured in compliance with the ASTM D5334-08 standard, by using KD2 pro thermal properties meter. The stability of the machining fluid to signify the particle dispersion homogeneity has been examined through UV-spectroscopy (Bruker MPA II). The sample of nMoS2-infused fluid was taken from three distinct locations bottom, middle, and top. The absorbance value of each sample was compared mutually to proclaim the homogeneity of dispersion of the particles.
The chief machining objectives considered during end milling operation are the Metal Removal Rate (MRR), surface roughness of the work material, and tool wear. The volume of metal removal rate and tool wear are calculated from work material loss and tool material encountered per second respectively from the concerned equations given below. The initial and final weight of both work material and tool were weighted by using a high-accuracy digital weighing balance (Model: MP-6000 g/1 g, Citizon) shown in Fig. 2a,b.
where wiw, wiw and t represent the initial weight of the work material in grams (g), the final weight of the work material in grams (g) and the machining time in seconds (s) respectively.
where wiT, wiT and t represent the initial weight of the tool material in grams (g), the final weight of the tool material in grams (g) and the machining time in seconds (s) respectively.
Machining resource and measurement of machining responses (a) CNC-end milling machine (b) MRR (c) Tool wear.
The third machining response surface roughness was evaluated through a Mitutoyo surface roughness tester (Model: 657111, SN: A00115021810). The machined slots using end milling operation during the experiments considered are shown in Fig. 3.
Workpiece used for experimental trails.
Since the parameters are four at four levels, Taguchi’s L16 OA has been deployed to assess the relationship between the cutting fluid and machining objectives along with the machining parameters. GRA is a widely used statistical-based decision-making technique introduced by Deng Julong47. It is recommended for the problems associated with the parameters and responses whose relationship is ambiguous or uncertain. Further, the technique is highly suitable for multi-response systems. The multi-responses are compressed down to a single response, called Common Grey Relational Grade (CGRG). The strongest relationship can be known through higher values of CGRG. The data by discrete behaviour and discrete sources are transformed into a single entity ranging from 0 to 1. The road map of GRA is shown in Fig. 4. The experimentally recorded responses for each trial as suggested by L16 OA are tabulated in Table 7.
The computed S/N ratio, normalized S/N ratio and Grey Relational Coefficients (GRC) of each experimental run are illustrated in Table 8.
Statistical process layout of GRA48.
PCA is a dimensionality reduction technique that preserves the deviations in the data pool49. It simplifies the model by reducing the number of variables with the capacity to retain the information of the original data set. The highest possible variances from the data are captured by each Principal Component50. The computational efficiency of PCA is also higher than soft computing predictions, hence the combination of GRA-PCA has been preferred. The number of variances is denoted by eigenvalues, subsequently, the direction of the principal component is denoted by eigenvectors. The GRC computed from the GRA theory has been utilized to construct a covariance matrix as shown below51,
The correlation array coefficient can be calculated as
Then, the following relation is used to determine the eigen values and eigen vectors,
The principal component can be computed with the help of the following relation,
The computed eigenvalues and eigenvector values of each principal component are furnished in Tables 9 and 10 respectively.
GRG for each experimental trial is manipulated by including the weightage of the principal component with a larger eigenvalue. The average value of GRGs concerning the three responses is used to reflect the performance of the three responses as a single entity and is called Common Grey Relational Grade (CGRG), further, it is referred to as Multi Response Performance Indicator (MRPI). The main effect plot and ANOVA analysis are conducted for each machining response. Table 11 shows the computed GRG values and corresponding MRPI with ranking. The experimental trial number corresponding to the largest value of MRPI is known to be an optimal set of experiments to showcase the best performance of end milling operation.
The main effect plot depicted in Fig. 5, manifests the ideal level of machining parameters including the type of machining fluid necessary to achieve the best performance characteristic of all the machining responses considered. A 650-rpm cutting speed, 132 mm/min feed rate and 1.5 mm depth of cut associated with 0.5% nMoS2 infused 60% Avocado oil are derived from the rigorous statistical modelling to yield the best performance of machining objectives, and in turn, found to be an optimal set of parameters.
The ANOVA is executed for each machining response since the impact of each parameter on the different machining responses is discrete. Tables 12 and 13, and 14 show the ANOVA for MRR, SR and TWR respectively. The proposed mathematical model seems to be adequate and accurate as the values of R2 and R2 adj are nearly more than 90% for all the machining responses.
The results obtained through this ANOVA endorse the importance of the implementation of nMoS2 infused Avocado oil machining fluid to mitigate the performance characteristics of machining objectives to the higher end. The two machining responses namely surface roughness and tool wear rate are significantly influenced by the machining fluid followed by the depth of cut. The percentage of contribution in mitigating the surface roughness and tool wear rate by the machining fluid is declared to be 96.92 and 67% respectively. The third machining objective, the metal removal rate, is driven by the depth of cut and feed rate, leaving the least significance for the machining fluid. The ranks of influence of each parameter on each objective are given in Table 15.
The implemented statistical GRA-PCA model can be validated in two ways, first, higher values of R2 and R2 adj surpassing 90% authenticate the model is adequate and accurate52. Second, the outlier plot depicted in Fig. 6 proclaims that the proposed model is adequate as most of the data points fit within the region.
Figure 7 expresses the strong correlation of the machining objectives to the principal component through the loading plot. It is witnessed from the plot that the two machining responses SR and TWR are strongly correlated with the direction and distance to the first principal component. The established strong correlation of the machining responses to the first principal component (PC1) considered highlights the adequacy and accuracy of the proposed model53.
The dispersion uniformity of the nMoS2 particles is assessed through UV-spectroscopy. Table 16 depicts the observed values of absorbance of all designated machining fluids at three distinct locations. The sample UV-spectroscopy results are depicted in Fig. 8.
UV-spectroscopy results of A4W6/0.75.
The experimental results indicate that the higher inclusion of nMoS2 limits the dispersion homogeneity. The higher value of absorbance indicates the higher inclusion of particles. However, the similarity in absorbance at different locations ensures the homogenous dispersion of particles within the fluid54. A4W6/0.75 and A6W4/0.75 fluids manifest their dissimilar absorbance values, signifying the agglomeration or sedimentation. There might be two reasons why the particle-rich fluids exhibit dissimilar absorbance values. First, higher particle content increases the specific density and viscosity of the fluids, thereby the particles are not dispersed well. The second one is the attraction and repulsion caused by Van der Waals forces, wherein the former is more dominant than the latter, that is why the agglomeration exists. Similar results were shared by the researchers55, while they were formulating three different cutting fluids made out of canola oil, sesame oil, and coconut oil enriched with nMoS2 particles.
The physiochemical properties of developed machining fluids are tabulated in Table 17. It is apprehended that the increased avocado oil content and nMoS2 augment the flash, fire point, and density of the machining fluids. The inclusion of external particles prompts an increase in the density. The flash and fire points of the machining fluids are inflated linearly with the increment of oil content and nMoS2 content. The presence of exogenous particles, which have a high melting point, retards the fluids’ inflammability, thereby switching the flash and fire points to higher. The error percentage between theoretical and experimental densities is within 0.5%. The increased constituents with a higher density like water and nMoS2 escalate the density to considerably high.
Kinematic viscosity of nano-machining fluids.
Figure 9 shows experimentally observed kinematic viscosity values for all the synthesised nano-machining fluids. It is witnessed from the results that the increased content of avocado oil and nMoS2 amplifies the kinematic viscosity value. The inclusion of nMoS2 particles imparts the resistance to the sliding of layers of the fluids, which in turn increases the shear stress, hence the viscosity is higher for particle-enriched fluids.
Thermal conductivity of nano machining fluids.
Figure 10 illustrates the experimentally assessed thermal conductivity of nano-machining fluids. The results indicate that the increased nMoS2 content magnifies the thermal conductivity values. This is ascribed to the good thermal conductivity of MoS2 in nanoplatelets form. Since a two-dimensional layer with a large surface area of nMoS2 platelets allows the molecule to transfer the heat faster56. The thermal conductivity of avocado oil is less than that of distilled water, and hence the nano-machining fluids with higher avocado oil content exhibit lower thermal conductivity.
The surface quality of the machined workpiece is the predominant outcome, which measures the good process capability. The majority of product-making environments need components with near-zero surface roughness. However, surface roughness is controlled by end milling parameters. Despite that, a noticeable significant impact of synthesised machining fluid is observed through experimental and statistical modelling.
Surface roughness result (a) Suboptimal experiment (b) Optimal experiment.
Figure 11a,b illustrate the peaks and valleys of the machined surface recorded from experiment trial No.4 (suboptimal experiment) and experiment trial No.11 (optimal experiment). It is witnessed from the observations that nearly 66.87% of surface roughness is minimized with the implementation of nano machining fluid for experiment No.11 in comparison with experiment No.4. However, experiment trial No.4 is driven by the low level of machining parameters. Interestingly, experiment trial No.7 is conducted with the same parameter level as experiment No.11, except for the machining fluid type. The implementation of A6W6/0.5 machining fluid still achieved a 63.42% surface roughness reduction compared to experiment No.7 associated with A4W6/0.75 machining fluid. The reason behind this is the uniformly distributed nMoS2 acts as a ball-bearing effect57,58 to minimize friction and the higher level of avocado oil (A6W4/0.5) concentration in the machining fluid. The higher viscosity of A6W4/0.5 is also an additional factor in imparting improved lubrication. This phenomenon reflects the discussion of the researchers59 that the outstanding lubricating properties of vegetable-based oil emerge from their molecular composition and chemical structure. Researchers state that the molecules of vegetable-based fluid are slender and heavy, and possess an amphiphilic nature to expose both attraction and repellent60. Owing to this nature, vegetable-based oils are normal to the metal surface, when they come into contact, and stay as a lubricant film. It has already been reported in many pieces of literature61 that the higher frictional existence in the tool-workpiece interface will deteriorate the surface quality. The stability analysis report manifests that the higher content of nMoS2 particles failed to disperse uniformly, despite, being agglomerated in A6W4/0.75. This may be the prime reason A6W4/0.75 machining fluid failed to perform.
Tool loss due to continued machining greatly impacts the product cost, subsequently questioning the survival of the industry. Tool wear is a clear outcome of the suboptimal parameters of the machining process. However, the present research signifies the influence of machining fluid on tool wear loss.
Comparative chart for tool wear loss.
The tool wear loss recorded by two different experiments with the same parameter but distinct machining fluid is shown in Fig. 12. A remarkable tool loss of 93.3% higher tool wear is observed for the experiment machined with A4W6/0.75 than the experiment machined with A6W4/0.5. Both experiments result from similar parameter levels, despite the content of avocado oil and nMoS2 differences. It has already been reported that A4W6/0.75 does not perform well due to the agglomerated particles, and even the avocado oil concentration is also lesser. The researchers62 revealed that the quantity of fatty acid dictates the proportions and directions of unsaturation within carbons. The fatty acid polarity determines the molecular films responsible for oiliness and lubricity in turn accelerate the attraction to metal surfaces, thus, forming a hydrodynamic lubricant layer. It has been reported63 that avocado oil has higher monounsaturated fatty acid (MUFA) than polyunsaturated fatty acid (PUFA), providing more stable lubricity at varying temperatures and oxidation resistance. However, the tool wear mechanism is adhesive-transformed abrasion wear, wherein the lubricating layer developed in aforementioned way by the machining fluid delays the adhesion effect of the tool and workpiece and further abrasion effect is exhausted due to the sliding effect offered by the nMoS2 particles between contact asperities. The combined lubrication effect at a higher level offered by both homogenously dispersed nMoS2 particles and the increased content of avocado oil drastically reduces the frictional effect. The very minimal coefficient of friction of nMoS2 particles and the higher viscosity and MUFA fatty acid of the A6W4/0.5 machining fluid jointly facilitate to retard the tool wear to a greater extent. The other machining response metal removal rate is already declared by the modelling results that the machining fluid has no hands-on mitigation.
The novel attempt to synthesise a bio-degradable machining fluid derived from nMoS2 particles infused with avocado oil and subsequent machining experimentation associated with hybrid statistical modelling draw the following remarkable conclusions.
The stability analysis conducted through a UV-spectrophotometer ensures the homogenous distribution of nMoS2 particles within the machining fluid.
The fundamental physiochemical properties observed from the relevant experiments reveal the improved density, kinematic viscosity, thermal conductivity, flash and fire points of the avocado machining fluid enriched with a higher percentage of nMoS2 inclusion.
The PCA modelling identifies the larger eigenvalue for PC 1 and transforms the weightage of multi-response into a single entity MRPI, to rank the best performance of machining objectives.
The hybrid GRA-PCA modelling explores 60% avocado oil and 0.5% nMoS2 (A6W4/0.5) as the ideal proportions to yield the best surface roughness, metal removal rate, and tool wear performance.
The ANOVA analysis conducted for individual machining responses explores the pivotal significance of the novel machining fluid to mitigate the surface roughness and tool wear, followed by the machining parameter depth of cut. Further, the adequacy and accuracy of the proposed hybrid model are validated with outlier plots and R2 values.
A substantial fall in surface roughness and tool wear is observed with the implementation of higher avocado oil and a medium level of nMoS2 (A6W4/0.5) content in the machining fluid.
The agglomeration of nMoS2 particles endorsed by the UV-spectroscopy result, is reflected in the under-performance of 0.75% of nMoS2 particles.
The remarkable improvement in machining responses delivered by the nano machining fluid, its cost-effectiveness and its degradable nature recommending the machining fluid as the right candidate for sustainable industrial lubrication.
Based on improved machining objectives for the proposed cutting fluid, under the MQL system, the manufacturing industries incur the machining objectives at an expected level with a noticeable budget.
The crucial cost-benefit analysis of the proposed fluid and the attempt to enhance the thermal conductivity by adding nano copper particles are considered to be a future research scope.
The particle agglomerations are the key limitation observed during the study.
The data involved in the findings of the result will be shared by the corresponding author based on reasonable request.
Response values of ith trial
Normalized S/N ratio for ith trial
S/N ratio for ith trial
Grey relation coefficient for ith trial
Deviation sequence for ith trial
Grey relation grade for ith trial
Covariance sequence of \(\:\left({x}_{i}\left(j\right),{x}_{i}\left(l\right)\right)\)
Standard deviation sequence of \(\:\left({x}_{i}\left(j\right),{x}_{i}\left(l\right)\right)\)
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This work was funded by the European Union under the REFRESH-Research Excellence For REgion Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Programme Just Transition. The authors extend their sincere appreciation to the Researchers Supporting Project number (RSP2025R465), King Saud University, Riyadh, Saudi Arabia.
Department of Mechanical Engineering, College of Engineering, Wolaita Sodo University, Wolaita Sodo, Ethiopia
Saints Ayza Anebo & Gezahgn Gebremaryam
AU-Sophisticated Testing and Instrumentation Centre (AU-STIC), Centre of Excellence-Advanced Material Synthesis (CoE-AMS), Department of Mechanical Engineering, Alliance School of Applied Engineering, Alliance University, Bangaluru, 562106, India
Centre of Molecular Medicine and Diagnostics (COMManD), Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College and Hospitals, Saveetha University, Chennai, 600 077, India
Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VŠB Technical University of Ostrava, Ostrava, Czech Republic
Jana Petrů & Muhammad Nasir Bashir
College of Engineering, Lishui University, Lishui, Zhejiang, 323000, China
Centre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India
Division of Research and Development, Lovely Professional University, Phagwara, Punjab, India
Department of Physics, Faculty of Natural Mathematical and Environmental Sciences, Metropolitan Technological University, Macul, 7800002, Santiago, Chile
Department of Botany and Microbiology, College of Science, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia
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A.A. and C.V.: Conceptualization, writing—original draft, writing—review and editing; K.S., G.G. and J.P.: Investigation; C.V.: Methodology; supervision; M.S., V.B., M.B. and R.R.: Writing—review and editing.
Correspondence to Venkatesh Chenrayan, Jana Petrů or Manzoore Elahi M. Soudagar.
The authors declare no competing interests.
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Anebo, A.A., Chenrayan, V., Shahapurkar, K. et al. An experimental and modelling approach to proclaim sustainable machining using avocado oil-based nano-cutting fluids. Sci Rep 15, 1598 (2025). https://doi.org/10.1038/s41598-024-84309-z
DOI: https://doi.org/10.1038/s41598-024-84309-z
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