1. Introduction
Turning of aluminum alloys have gained paramount significance in automobile and aerospace advanced manufacturing. This is due to its high strength to weight ratio [
1,
2,
3]. Turning is an important machining process for the cutting of round objects to the desired shape and size. It has the advantages of producing a good quality product, having a lower lead time, customer satisfaction and being economical [
1]. However, while machining such alloys, sustainability of the cutting operation to meet the surface finish, dimensional precision, lower cutting forces, with economic power consumption is highly desirous [
1,
2]. From this perspective, the appropriate selection of cutting parameters, especially feed rate, cutting speed, and depth of cut are essential to achieve these objectives [
1,
2]. Further, machining can be performed in dry conditions [
3,
4], near dry conditions (minimum quantity lubricant, MQL) [
3,
4], flooded coolant [
3,
4], cryogenic coolant [
3,
4], or with nanofluids [
5,
6]. However, the growing concept of green/sustainable manufacturing has shifted the paradigm of manufacturers to environmentally friendly cutting fluids [
3,
4]. Dry and MQL environment are considered sustainable in machining processes [
5]. The advantages of MQL are to reduce cutting forces/cutting power and temperature at the tooltip, improve the tool life, enhance dimensional accuracy, and improve the surface quality of machined parts [
3,
4,
5]. The most common eco-friendly cutting fluids are vegetable oil [
3,
4,
6,
7], nano cutting fluids, and ester [
3,
4,
6,
7] due to the less poisonous effects and biodegradability as compared to petroleum-based mineral oil [
5,
6,
7].
2. Literature Review
In the presented research study, a comprehensive literature review of the turning operation of aluminum alloys has been carried out under dry, MQL, and flooded conditions. For example, Patel et al. [
8] have analyzed and optimized the surface quality characteristics (i.e., circularity and cylindricity errors) and material removal rate in the dry turning of a 7075 aluminum alloy. The analysis of variance (ANOVA) showed that cutting speed is the most influencing factor in all responses. They reported that an increase in cut depth and feed rate instigate a negative impact on the surface quality, circularity, and cylindricity error, while creating a positive effect on the material removal rate. Surface quality improves with an increase in the nose radius improves while having a non-linear relationship with circularity and cylindricity error. They also optimized cutting parameters using JAYA coupled with principal component analysis (PCA). Ajay and Vinoth [
9] optimized the turning cutting parameters of 6061 aluminum alloy using high-speed steel (HSS) insert under dry condition. The ANOVA results revealed that cutting speed is the most significant factor for surface roughness, while for temperature and resultant cutting forces, cutting speed, feed rate, and depth of cut are the most significant contributing factors. The study also showed that cutting forces increase with an increase in feed rate and depth of cut. A similar study was completed by Javidikia et al. [
10]. They studied the impact of tool geometry and cutting conditions in turning the aluminum alloy 6061-T6 under a dry condition with uncoated carbide insert. Their results indicated that machining forces decrease with an increase in cutting speed; however, they increases with an increase in cutting edge radius. The temperature at the tooltip interface increases with an increase in cutting speed and decreases with an increase in rake angle from negative to positive values. Likewise, a decrease in the cutting forces was observed by increasing the rake angle from negative to positive values. An increase in feed rate increases feed forces; however, it reduces cutting forces. Kannan et al. [
11] studied the machinability of the aluminum matrix Al 7075/BN/Al
2O
3 under MQL and dry conditions using a grade K313 (WC/Co fine-grain grade) cutting tool. They investigated that machining under a MQL environment reduces the cutting force, tool wear, and improves the quality of the machined surface compared to the dry environment. Additionally, low cutting forces are observed at high cutting speed and low feed rate. An investigation on tool wear in the turning of an Al/SiCp (based on aluminum alloy 2024) composite under cooling and lubrication conditions was conducted by Duan et al. [
12]. The insert used was a polycrystalline diamond (PCD) tool. Their study showed that the type of flank wear, abrasive wear, and tool breakage could be controlled up to a greater extent under MQL (a mixture of oil and gas) and liquid nitrogen (LN
2). Kouam et al. [
13] studied the effect of MQL conditions on the machining of a 7075-T6 aluminum alloy. The cutting tool insert used was a carbide (DNGP-432 KC5410, Kennametal, Latrobe, PA, USA) with Titanium diboride (TiB
2) coating, while the coolant was Mecagreen 550 lubricant coolant mixed with 15% water at 3 and 1.75 mL/min flow rate. The effect of MQL in the machining of a 6082 aluminum alloy was studied by Yigit [
14]. The diamond-coated carbide was used as insert, while the lubricant was commercial oil Rocol A208A plus, applied at 50 mL/h and 100 mL/h. His study concluded that surface roughness, dimensional accuracy, and cutting forces improved due to the reduction in wear at the tooltip under MQL conditions. Islam [
15] analyzed surface roughness and dimensional accuracy (diameter error and circularity) in the turning of an aluminum 6061, mild steel 1030, and alloy steel 4340. The insert used was a square-shaped insert with enriched cobalt coating (chemical vapor deposition (CVD) titanium nitride (TiN) – titanium carbonitride (TiCN) – aluminium oxide (Al
2O
3) –TiN) manufactured by Stellram, La Vergne, TN, USA. The coolant was 2010 Coolube, a vegetable-based metal cutting lubricant, and sprayed in the form of mist at 1.667 × 10
−5 L/s flow rate. The ANOVA results revealed that the work material and coolant methods (MQL) have a significant effect on the dimensional accuracy and the least effect on surface roughness.
Jafarian et al. [
16] optimized the multiple responses including resultant cutting forces, insert wear, and surface roughness, in aluminum alloy turning using an integrated approach of artificial neural network, genetic algorithm (GA) and particle swarm optimization (PSO). The optimized results indicated that the proposed methodology is effective in predicting optimal cutting parameters. Agustina et al. [
17] analyzed the cutting forces in the turning of unified numbering system (UNS) A97075 aluminum alloys under dry conditions with two types of inserts i.e., DCMT11T304-F2 and DCMT11T308-F2 manufactured by SECO, Aljunied, Singapore. The most significant parameters that affect the cutting forces are feed rate, followed by depth of cut and tool type. Sreejith [
18] studied the performance of the machining of a 6061 aluminum alloy with dry, MQL, and flooded lubricant conditions. The diamond-coated carbide was used as insert, while the lubricant was commercial oil BP Microtrend 231 L. The flow rate of MQL was 50 and 100 mL/h. The results showed that machining under MQL conditions provides comparable results to flooded lubricant conditions. The thermal softening of chips during the machining of aluminum alloys affects the surface quality, cutting forces, and tooltip. However, with the application of MQL, it can be reduced to a greater extent. Reis and Abrao [
19] examined the machinability of the 6351-T6 aluminum alloy under dry turning conditions. The inserts used were cemented carbide, diamond coated carbide, and polycrystalline diamond (PCD). The results revealed that the PCD tool performed better compared to other tools. Further, cutting forces increase with an increase in feed rate and depth of cut, however, they decrease with an increase in cutting speed. For the PCD tool, the dominant force observed was the radial force, while the tangential and axial forces were lowest.
The Taguchi based signal to noise (S/N) ratios method is an experimental design technique that is suitable for the optimization of a single response variable [
20]. In the presented study, we have focused multi-responses, and consequently, in order to deal with such a problem, the multi-objective optimization based on ratio analysis (MOORA) method is selected. This method was proposed by Brauers [
21] and can successfully deal with the complex decision-making process in the manufacturing environment [
22]. It allows us to simultaneously optimize the responses, whether their objective function is conflicting (including both maximization and minimization terms) or the same (either maximization or minimization) [
23]. According to Yusuf and Sebla [
23], MOORA is robust and straightforward compared to other multi-criteria decision-making methods (MCDM) such as technique for order of preference by similarity to ideal solution (TOPSIS), Viekriterijumsko Kompromisno Rangiranje (VIKOR), grey relational analysis and weighted principal components as these methods are complex and difficult to apply in reality. Finding the weights of criteria in MCDM is important [
24]. Various methods for weight determination are proposed and are classified into objective and subjective methods. In objective weight methods, the weights of criteria (responses) are measured based on the available data without intervention of an expert’s opinion [
24]. The well-known techniques are entropy [
25], standard deviation [
26], criteria importance through inter-criteria correlation (CRITIC) [
27], and the maximizing deviation method [
28]. In contrary, the subjective methods involve the expert’s opinion [
24]. The most common methods are the pairwise comparison method, namely the analytical hierarchy process (AHP) [
29], the swing weighting method [
30], the ranking method [
31], and the simple multi-attribute rating technique (SMART) [
32]. Weight determination using criteria importance through inter-criteria correlation (CRITIC) is effective, because it accounts for both conflict and contrast in weight determination [
27]. However the other methods such as entropy, standard deviation, mean weight and maximizing deviation method don’t take into account such information in weight determination [
24]. The subjective weight methods, such as AHP, however include expert opinions but do not incorporate the uncertainty and ambiguity of the human mind [
24]. Singarave et al., [
33] optimized the turning operation of EN25 steel using MOORA coupled with entropy. Pathapalli et al., [
34] optimized the machining parameters of an Al-6063 composite using weighted aggregated sum product assessment (WASPAS) and MOORA. They concluded that both methods yielded similar results. Majumder and Saha [
35] concluded that MOORA coupled with PCA performed better compared to TOPSIS coupled with PCA in optimizing the turning of ASTM A588 mild steel. Akkaya et al. [
36] coupled MOORA with AHP to solve the problem for the industrial engineers in selecting which sector to work in, in the future.
The literature review presented herein shows that a limited number of research publications are available on the machining of aluminum alloys and their composite matrix under dry and MQL environments. Hence, it can be assumed that the presented research demonstrates the first comprehensive attempt to analyze and optimize the cutting forces and shape deviations of the aluminum alloy 6026-T9 under both dry and MQL environments using vegetable oil (namely olive oil). A study performed by Abas et al., [
37] on a similar type of material, focuses only on the optimization of surface roughness profile, material removal rate, and tool life under MQL and dry conditions. They concluded that under the MQL environment, the machining of such alloys performs better compared to the dry environment. However, the effect of cutting parameters on the component of cutting forces and shape deviations were not considered in their study. Therefore, in the present research, the Taguchi signal to noise ratio and analysis of variance (ANOVA) are applied to optimize the individual responses in order to achieve this aim. Further, the effect of cutting parameters on performance factors (responses) are studied by using the main effect plots. For multi-response optimization, an integrated approach is implemented by utilizing the Taguchi signal to noise ratio integrated with MOORA and CRITIC.