Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel

In the current study, the internal burnishing process under the minimum quantity lubrication (MQL) condition has been optimized to decrease the cylindricity (CYL) and circularity (CIC) of the burnished hole, while the surface roughness (SR) is predefined as a constraint. The optimizing inputs are the diameter of the spray nozzle (D), the spray elevation angle (A), the lubricant quantity (Q), and the pressure value of the compressed air (P). The artificial neural network (ANN) models of burnishing performances are proposed to optimise inputs. The grey relational analysis (GRA) is utilized to compute the weight value of each response. Optimal values of MQL system parameters and technological objectives are selected with the aid of an evolution algorithm (vibration and communication particle swarm optimization (VCPSO) algorithm). The results indicated that the optimal outcomes of the D, A, Q, and P are 1.5 mm, 50 deg, 140 ml/h, and 0.6 MPa, respectively. Furthermore, the CYL, CIC, and SR were decreased by 53.14 %, 57.83 %, and 72.97 %, respectively, at the optimal solution. Finally, the obtained results are expected to be a significant solution to support the machine operator in selecting the optimal MQL system parameters to improve the hole quality in the MQL-assisted burnishing process.


INTRODUCTION
The coolant was widely applied to decrease the temperature and friction for different machining operations. However, the massive application of the lubricant increases its cost far higher than the cost of cutting tools, endangers human health, and creates environmental pollution [1].
Manufacturing costs, health, and environmental issues motivate manufacturing enterprises to reduce and eliminate lubricants. Dry machining is the manufacturing operation without the use of any lubricant, which is becoming popular due to environmental safety and worker health [2]. Unfortunately, this method is restricted to the machining of low-strength material with moderate cutting conditions [3] and [4]. An alternative solution is minimum quantity lubrication (MQL), in which a very low amount of fluid (5 ml/h to 200 ml/h) is used in conjunction with compressed air. For different machining of hardened steels and titanium alloys, MQL is a feasible, efficient, and eco-friendly alternative, compared to dry and flood-cooling methods [5].
The MQL method was widely utilized to boost the technical performances of different machining processes. The optimal values of the lubricant quantity (Q), cutting speed (V), and feed rate (f) were selected to decrease the cutting force (CF) of the milling AISI 4140 steel [6]. The specific cutting energy (SCE) model was developed in terms of the V, f, Q, and depth of cut (d) for the MQL end milling process [7]. Moreover, the MQL resulted in 33 % less power consumption (PW) in comparison with the wet approach [8]. Rajan et al. [9] emphasized that the MQL exhibited 157.3 % and 40 % less tool wear (TW) for the turning operation in dry and flood conditions, respectively. Sivaiah et al. [10] revealed that the SR and TW were decreased by Le 54 % and 7 %, respectively, while the material removal rate (MRR) was improved by 27 % for the MQL turning AISI 304. Moreover, the MQL mode could help increase the feed rate by 40 % in the turning EN31 steel [11]. Zhang et al. [12] indicated that the specific grinding energy was significantly decreased using nanoparticle-based MQL. The combination of the MQL and cooled air could be applied to decrease 10.5 % grinding force and 36.3 % roundness errors [13]. A hybrid approach comprising the MQL and water could provide higher cooling-lubrication efficiency [14]. Faverjon et al. [15] stated that the MQL was effectively applied to decrease the thermal distortion and enhance the machined accuracy of the drilled aluminium part. The SR and burr height (BH) of the drilled hole were significantly reduced under the MQL condition [16]. MQL could be employed to improve the surface morphologies for the drilled composites [17].
The employment of the MQL system in burnishing operations has been rarely addressed in published investigations. The MQL-based diamond-burnishing process was developed and optimized, in which the optimal process parameters, including the spindle speed (S), burnishing force (Fb), and f, were selected to decrease the SR and improve the surface hardness (SH) [18]. Similarly, the surface integrity of the burnished titanium alloy was significantly improved using optimal MQL conditions [19]. As a result, missing gaps of the previous publications can be expressed as follows.
The influences of MQL operating parameters, including the nozzle diameter, spray angle, lubricant quantity, and air pressure on geometric errors, have not been analysed in the published investigations. Moreover, predictive models of the cylindricity and circularity regarding the MQL operating parameters for the burnishing operation have not been proposed.
The selection of optimal MQL operating parameters for decreasing geometrical deviations (cylindricity and circularity) and surface roughness for the burnishing operation has not been addressed. The diameter and number of droplets were primarily affected by the lubricant quantity, air pressure, and nozzle diameter, while the machining zone wettability was mainly influenced by the nozzle distance and lubricant quantity [20] and [21]. The spraying angle was related to the workpiece to be machined [22]. Therefore, the optimum setting of MQL system parameters may provide better penetration of the lubricant at the rollers-workpiece interfaces.

Fig. 1. Burnishing experiment
1 MATERIAL METHOD Hardened specimens labelled 5145 are drilled and turned using a computer numerical control (CNC) lathe machine (CTX 400E). The drill tool with a diameter of 16 mm is used to produce the rough hole, in which a feed rate of 0.5 mm/rev, a depth of cut of 8 mm, and a spindle speed of 400 RPM are used as the drilling parameters. Multiple turning paths using polycrystalline cubic boron nitride (CBN) inserts are adopted to turn the internal holes, in which a feed rate of 0.25 mm/rev, a depth of cut of 1 mm, and a spindle speed of 800 RPM are turning factors. The length, internal diameter, and external diameter of each workpiece are 60 mm, 28 mm, and 40 mm, respectively. The cylindricity, circularity, and surface roughness of the pre-machined hole are 70.62 µm, 18.48 µm, and 3.84 µm, respectively.
The burnishing trials are done with the aid of a milling machine, in which the workpiece is positioned and tightly clamped using a jawcentring chuck (Fig. 1). Soybean oil has been selected as the lubricant and mixed with the compressed air to form the mixture (air-oil mist), which is delivered into the machining region through MQL nozzles. The Mitutoyo Surftest-301 and ZEISS CONTURA G2 are employed to capture the surface roughness, cylindricity, and circularity values. The schematic illustration of the internal roller burnishing operation under the MQL condition is depicted in Fig. 2. The MQL system parameters, including the diameter of the spray nozzle, the spray elevation angle, the lubricant quantity, and the pressure value of the compressed air, are presented in Table 1. The ranges of each factor are determined with the aid of the MQL system's characteristics and the manufacturer's recommendations.
The optimization procedure is presented in Fig. 3, which is shown as bellows: Step 1: The burnishing experiments are executed based on the Box-Behnken design to save the experimental cost [23,24].
Step 2: The CYL, CIC, and SR models are developed in terms of the MQL system parameters using the ANN approach [25]. The output of the neuron is a function of all inputs and their weights and expressed as [26]: where Ij, wij, ti, and fn present the internal value, weight, threshold value, and transfer function, respectively.
where ya and yp are actual and predicted data, respectively. The best architecture of the ANN model with the PE value less than 10 %.
The R 2 value and root mean square error (RMSE) are named as important indicators to investigate the adequacy of the developed models and computed as: Step 3: The weight value of each response is determined using the grey relation analysis (GRA).
The correlation coefficient is calculated as: where Cov(Ii(j)) and Ii(l) presents the covariance of sequences Ii(j) and Ii(l), respectively.
The eigenvalues and consequent eigenvectors are calculated as: where λk, Vik, and Jm presents the eigenvalues, eigenvectors, and the identity matrix, respectively.
The major principal coefficient is calculated as: Step 4: The optimal outcomes of the MQL system factors and technical responses are selected using the VCPSO algorithm. Practically, the results produced by the PSO are easily failed into the local optimizing outcomes due to the lack of communication among entire particles. To obtain the global solution, communication with three operations (i.e., the mutation, crossover, and selection) is performed.
The mutation is conducted to exchange information between old and new particles. The mutation operator is expressed as: where ra, rb, and rc are random integers in the range (0, N). The F is the scaling factor. In the crossover stage, the trial vector ui son is produced to increase the diversity of the population on the basis of the mutation operator. The crossover operation is expressed as: where CR is the crossover rate with the range of (0, 1), drand is a random number from (1, 2,…, D). The selection operator is enabled to choose better particle between ui son and xi using the fitness function, which is expressed as: The equations of the VCPSO algorithm are expressed as follows: where c3 is the accelerated coefficient, while the r1, r2 and r3 are random numbers in range (0, 1), respectively.

Development of the Optimal ANN Models
The experimental data of the internal burnishing operation are presented in  Fig. 4.
The working parameters of the ANN model are the NT, NN, TF, PM, TS, NL, and LF and their levels are shown in Table 3.
To investigate the accuracy of the developed ANN model, a set of experiments is performed at random points. The comparisons between the predicted and experimental results are presented in Table 5. The errors of the CYL, CIC, and SR lie within the range of -2.05 % to 2.38 %, -2.17 % to 2.91 %, and -4.76 % to 3.70 %, respectively. The accepted deviations (less than 5.0 %) indicated that the proposed models could be applied to forecast the burnishing performances with good accuracy.   Fig. 6 indicates that the burnishing responses decrease with increasing nozzle diameter. As the nozzle diameter increases, more droplets can penetrate the burnishing region. The friction at the interfaces decreases, and the cooling-lubrication efficiency increases; hence, low CYL and CIC values are obtained. Moreover, the burnished surface is efficiently wetted and protected with a higher amount of oil mist; hence, the SR decreases. Fig. 7 presents the impact of the spray elevation angle on the CYL, CIC, and SR, respectively. For the CYL and CIC values, it can be stated that an increased spray elevation angle has a positive effect, but it turns negative at a certain amount (48 deg), while the turning point of 55 deg is applied for the SR. For low spray elevation angle, the penetration of the oil mist is ineffective, resulting in a lack of proper coolinglubrication efficiency at the interfaces. An uneven deformation is produced along the axial and radial directions; hence, higher values of the CYL and CIC are obtained. In addition, high friction at the interfaces causes a higher surface roughness. At a higher spray elevation angle, the mist flow is accurately delivered into the machining region, which results in decreased friction; hence, the CYL and CIC decreases. Higher coolinglubrication efficiency causes a reduction in surface roughness. Fig. 8 presents the impact of the Q on the CYL, CIC, and SR, respectively. A higher lubricant quantity increases the number of oil droplets entering the contact zone between the roller-workpiece.

Parametric Influences
The cooling-lubrication efficiency will be improved due to friction reduction, which causes uniform deformation; hence, the CYL and CIC significantly decrease. The burnished surface is wetted and protected due to the penetration of a higher amount of the oil mist; hence, the SR decreases.
Figs. 9 indicates that the reductions in the burnishing responses are found with increased air pressure. Higher pressure causes a reduction in the droplet size of the oil mist, while the number of droplets and spraying velocity increase. The small diameter increases the penetration ability into the burnishing region; hence, the coolinglubrication efficiency is enhanced. The friction between the burnishing tool and workpiece decreases; hence, the burnishing responses reduce.

Optimization Results
The proportions of the first principal component is 92.7 %, followed by the second component (4.7 %) and the third component (2.6 %), respectively. The weight values are calculated using the squares of subsequent eigenvectors of the three components. The weight values of the CYL, CIC, and SR are 0.48, 0.34, and 0.18, respectively.
For the high-pressure bushing components, the predefined surface roughness of 0.1 µm is selected. The 3D plot produced with the aid of the VCPSO is shown in Fig. 10. As a result, the optimal values of the D, A, Q, and P are 1.5 mm, 50 deg, 140 ml/h, and 0.6 MPa, respectively, while the corresponding outcomes of the CYL, CIC, and SR are 10.07 µm, 4.04 µm, and 0.10 µm, respectively (Table 9). The improvements in the CYL, CIC, and SR are 53.14 %, 57.83 %, and 9.64 %, respectively, as compared to the initial values. The CYL, CIC, and SR are decreased by 85.74 %, 78.14 %, and 97.40 %, respectively, as compared to the pre-machined state.

CONCLUSIONS
In this study, a new MQL-based burnishing process has been proposed and optimized. Instead of traditional process parameters, the inputs considered were MQL operating parameters, including the nozzle diameter, the spray elevation angle, the lubricant quantity, and the pressure value of the compressed air. The quality indicators of the burnished hole, including the cylindricity and circularity, were then addressed and enhanced. The ANN approach was employed to develop the performance models instead of conventional regression models. A new evolutional algorithm entitled VCPSO algorithm was applied to find the optimal solution.
The academic contributions can be expressed as follows: The proposed optimization technique comprising ANN, GRA, and VCPSO used in this study effectively solves the complex optimizing problems and determines optimal outcomes. The developed approach possesses various advantages, including low experimental costs, decreased human efforts, and easy application by means of Matlab software. The optimizing method can be extensively employed to solve the optimization issues for not only burnishing operations but also other machining processes.
The impacts of the MQL operating parameters (the nozzle diameter, the spray elevation angle, the lubricant quantity, and the pressure value of the compressed air) on the geometric deviations (cylindricity and circularity) and the surface roughness have been thoroughly analysed. The obtained knowledge can help machine operators deeply understand the physical insights in the developed burnishing operation.
The optimized findings can be applied in the burnishing operation to enhance the machining performances. Moreover, the obtained results revealed that the MQL system could be employed to enhance the machining quality of internal holes in various components.
The optimization issues addressing the cylindricity and circularity with a predefined constraint of the surface roughness are realistic 12 and reliable compared to the simultaneous optimizing three objectives.
The analysed outcomes of the current study can be effectively and efficiently utilized as significant references for future investigations and developing expert systems regarding MQLassisted internal burnishing processes.
The industrial contributions can be expressed as follows.
In this work, an efficient burnishing operation under the MQL condition has been developed to enhance the machining quality of the internal surface. The successful implementation in the hardened steel is utilized to prove the effectiveness and potential application of the proposed burnishing process. The developed finishing operation can be effectively and efficiently applied to fabricate internal holes.
The constructed ANN models of the CYL, CIC, and SR were significant and accurate. The proposed correlations could be effectively applied to forecast the burnishing responses in industrial applications. The experimental costs and efforts can be saved with the aid of the proposed ANN models.
The 3D plots showing global relationships among machining performances can support machine operators in selecting the optimal values of the MQL operating factors, circularity, and cylindricity under various constraints of the surface roughness for different purposes.
The influences of the MQL system parameters on energy consumption and production costs have not been presented. A comprehensive optimization considering more objectives will be explored in future works.