Single Response Optimization Research Articles

Artificial neural networks in the retention of anthocyanins and total phenolics in the osmotic pre-treatment of Biloxi variety blueberry (Vaccinium corymbosum L.) jam

Blueberries are a fruit that is an important source of bioactive components beneficial to the human diet, such as anthocyanins and total phenolics, which are altered by the use of high temperatures during processing. This study aimed to evaluate the use of artificial neural networks in the optimization of sucrose concentration and time for the osmotic pre-treatment of blueberries of the Biloxi variety, to retain the greatest amount of anthocyanins and total phenolics in the subsequent preparation of jam. Artificial neural networks of the feedforward type were used, with a Backpropagation training algorithm with Levenberg-Marquardt weight adjustment, to achieve the optimal predicted combination that maximizes the retention of these bioactive components. The model achieved its best performance with 11 neurons in the hidden layer, achieving an R2 coefficient of 0.98 and a mean square error of 4.76, indicating a strong ability for generalization. Artificial neural networks allowed to obtain the best optimal combination of predicted multiple responses of factors consisting of a sucrose concentration of 1.64 M and a time of 211.52 min, which retained a higher content of total monomeric anthocyanins with 70.98 mg cyanidin-3-O-glucoside 100 g-1 of jam and total phenolics with 110.54 mg GAE g-1 of jam. On the other hand, through single-response optimization was obtained that the combination of experimental factors that maximized total anthocyanins (71.59 mg cyanidin-3-O-glucoside 100 g-1 of jam) was 1.54 M of sucrose and 232.73 min and for total phenols (111.06 mg GAE g-1 of jam) 1.79 M of sucrose and 196.36 min. The use of artificial neural networks is an excellent alternative for modeling phenomena, compared to traditional methods.

Multi-objective optimization of friction stir additively manufactured Al-7075 multi-layered laminates

Friction Stir Additive Manufacturing (FSAM) is an advanced technique for non-fusion aluminum alloys, enabling the production of large, irregular-shaped parts without the typical solidification defects associated with melting. FSAM relies on a critical interplay of parameters that significantly influence the microstructure and mechanical properties of the final fabricated structure. This study focuses on developing seven-layered Al-7075 laminates following the Taguchi L9 matrix array under forced cooling. Three input parameters, namely tool stirring speed (TSS), tool welding speed (TWS), and the number of tool passes (NPs) were selected for multi-objective optimization using Taguchi Grey Relational Analysis (GRA) for higher microhardness, UTS, and % elongation. Microstructural analysis revealed fine equiaxed grains within the stir zone, indicating a substantial 95.5% reduction in grain size compared to the base metal, along with a mixed ductile-brittle fracture in samples with higher UTS. Taguchi single-response optimization identified optimal settings in sample L6 (800 rpm, 90 mm/min, and 1 NP) for increased hardness and reduced % elongation. Conversely, sample L7 (1000 rpm, 30 mm/min, and 3 NPs) exhibited the lowest hardness and UTS, along with the highest % elongation due to the high density of precipitate dissolution. Notably, sample L9 (1000 rpm, 90 mm/min, and 2 NPs) achieved the highest UTS of 415 MPa as a result of an even distribution of precipitates with low dissolution density, representing 74.3% of the base metal's strength. TWS emerged as the predominant input parameter, contributing 56.94%, 52.33%, and 47.33% to hardness, UTS, and % elongation, respectively. Moreover, Taguchi-GRA identified the optimal parameter set as TSS of 1000 rpm, TWS of 60 mm/min, and 2 NPs for higher hardness, UTS, and % elongation. ANOVA analysis of GRA demonstrated robust predictive capabilities with an R-squared value of 88.09%.

STUDY ON MECHANICAL PROPERTIES OF DEVELOPED ECO-FRIENDLY BRAKE PADS USING GRAY RELATIONAL ANALYSIS IN BOX BEHNKEN DESIGN OF EXPERIMENT

Over the years, asbestos has been a leading material for developing automobile brake pads. However, due to its hazardous nature to human health, many researchers have found that organic materials possess the potential for replacement. The organic materials used in this research were the hybrid of coconut fruit fibre CFF and oyster sea shell OSS. These materials were gathered from the river bank, dried, crushed, washed, ground, sieved, and moulded with different additives: CuO (abrasive), graphite (friction modifier or solid Lubricant), epoxy resin (binder), hardener (catalyst) by Box Behnken design BBD of L27 experiment. Therefore, an experimental test on mechanical properties was performed to check the developed brake pad's characterization. Then, grey relational analysis was used to analyze grey relational grade GRG to convert a multiple response process optimization problem into a single response optimization using maximization of maximum GRG. The optimum parameter values producing the highest value of grey relational grade were chosen as rank one, pertained to the factors setup for experiment number S3232 having moulding pressure of 11.25MPa, moulding temperature of 180oC, and heat treatment time of 180min. The optimized response values obtained from the experiment sample S3232 give the highest values on all the responses, i.e., density of 3.483g/cm3, hardness of 52.91BHN, compressive strength of 2.78MPa, ultimate tensile strength of 3.92MPa, impact energy of 17.87N, cold water absorption of 0.2863%, hot water absorption of 0.4785%, and oil absorption of 0.4402%. After that, the optimized brake pad sample (S3232) was applied to the automobile and was suitable for braking owing to its high mechanical properties.

Fabrication of Multi-Walled Structure through Parametric Study of Bead Geometries of GMAW-Based WAAM Process of SS309L.

In the present study, an attempt is made to investigate and optimize the bead geometries of bead width (BW) and bead height (BH) of SS-309L using an SS316L substrate by employing a gas metal arc welding (GMAW)-based wire-arc additive manufacturing (WAAM) process. The Box-Behnken design approach was used to conduct the trials of single-layer depositions with input variables of travel speed (TS), voltage (V), and gas mixture ratio (GMR). The developed multi-variable regression models were tested for feasibility using ANOVA and residual plots. The data obtained indicated that V had the most significant impact on BW, followed by TS and GMR. For BH, TS had the most significant impact, followed by GMR and V. The results of single-response optimization using a passing vehicle search (PVS) algorithm showed a maximum BH of 9.48 mm and a minimum BW of 5.90 mm. To tackle the contradictory situation, a multi-objective PVS algorithm was employed, which produced non-dominated solutions. A multi-layered structure was successfully fabricated at the optimal parametric settings of TS at 20 mm/s, of voltage at 22 V, and of GMR at 3. For multi-layer structures, fusion among the layers was observed to be good, and they were found to be free from the disbonding of layers. This revealed the suitability of the PVS algorithm for generating suitable optimal WAAM variables. We consider the current work highly beneficial for users fabricating multi-layer structures.

Multi-response optimization on abrasive water jet machining of Aluminum 7075 alloy using Taguchi–DEAR methodology

This research aims to conduct the multi-response optimization for abrasive water jet machining (AWJM) of Aluminum 7075 metal matrix composites. The experiments were conducted with three levels of AWJM factors such as pressure, standoff distance, and traverse speed. The experiments were designed using Taguchi's L9 orthogonal array. The influence of AWJM parameters was assessed by measuring the outcomes like material removal rate, surface roughness, and taper angle. The single response optimization was obtained through Taguchi's signal-to-noise ratio analysis, and multi-response optimization was achieved with Taguchi–data envelopment analysis-based ranking (DEAR). From DEAR, the optimal AWJM parameter 180 MPa of pressure, 3 mm of standoff distance, and 40 mm/min traverse speed was found. After optimization, the surface roughness and taper angle were reduced by 5.44% and 10.92%, respectively. The influence of cutting parameters on the outcomes was examined through the analysis of variance. It was noted that traverse speed had a significant effect and a contribution of 47.79%. After AWJM, the surface was evaluated using SEM analysis, ploughing marks, ductile rupture, and a small crack were observed.

Predicting hot wire tungsten inert gas welding parameters for joining P91 and 304HCu steel using multi-optimization techniques

PurposeDissimilar joining of austenitic stainless steels and ferritic steels is a challenging task and has a wide range of applications due to its excellent mechanical and thermal characteristics. They are joined mostly by using conventional modes. In the current investigation, the study and optimization of hot wire TIG welding parameters was carried out.Design/methodology/approachThese parameters will govern the desired characteristics of the joint. Solutions were found out through multi-response optimization by using response surface methodology and single response optimization using particle swarm optimization.FindingsOptimized input welding parameters that were achieved are electrode current 180 amps, wire feed rate 1870 mm/min and hot wire current 98 amps and the optimized UTS is 665.45 MPa. The results from PSO were compared with RSM and the optimized input welding parameters for the electrode current, hot wire current and wire feed rate exhibited maximum ultimate tensile strength which were also confirmed from response and contour plots.Originality/valueSensitivity analysis was also performed to understand the effect of each individual parameters on the response. Microstructure features were evaluated for the joints and was found that the characteristics are within the desired criteria.

Optimization of machining parameters for turning process by using grey relational analysis

This paper presents an optimization of process parameters of turning operation using multi-response optimization Grey Relational Analysis (GRA) method instead of single response optimization. These parameters were optimized based on a three level two factor factorial design with three center points was used for the experimental design with Grey Relational Analysis. The machining parameters such as cutting speed, feed rate, and depth of cut were chosen for experimentation. The performance characteristics chosen for this study are material removal rate (MRR), tool life, and surface roughness. Experiments were conducted using coated carbide tool (KC5010) as the tool and martensitic stainless steel (AISI 420) as the workpiece. Experimental results have been improved through this approach.

Optimization of Wire-cut EDM process parameters using TLBO algorithm

The novelty of the work is reflected in using Teaching Learning Based Optimization as metaheuristic algorithm in combination with Response Surface Methodology for achieving multi response optimization of two conflicting responses while machining Incoloy 800H on Wire cut EDM. It is used in materials and equipment for furnace, petrochemical furnace cracker tubes, pigtails and headers, and electric heating element sheathing. There are a number of process parameters of Wire cut EDM which affect the output responses. Here, the effects of Pulse On Time, Pulse Off Time, Peak Current and Servo Voltage on the Cutting Rate and Dimensional Deviation were studied. Experiments were performed according to Box Behnken design with 29 experiemental runs. Pulse On Time was found to be the most significant factor in affecting the Cutting Rate and Dimensional Deviation as envisaged by Analysis of Variance . First, single response optimization was performed using Teaching Learning Based Optimization Algorithm which resulted in an optimal factor level setting of Pulse On Time-120mu, Pulse Off Time-45 mu, Peak Current-200A, Servo Voltage-47V corresponding to Cutting Rate, whereas, Pulse On Time-120mu, Pulse Off Time-35 mu, Peak Current-160 A, and Servo Voltage-30V formed an optimal setting of the factors corresponding to the Dimensional Deviation. Multi-response optimization resulted in an optimal factor level setting of Pulse On Time-120 mu, Pulse Off Time-38 mu, Peak Current-190 A, and Servo Voltage-53 V corresponding to both Cutting Rate and Dimensional Deviation. Then, the optimal factor level settings for single response and multi-response optimizations were confirmed through three confirmation tests. The average of the confirmation tests was compared with predicted value and percentage error was calculated. The percentage error comes out to be less than 5% in all the cases.

Parameter Optimization and Machining Performance of Inconel 625 with Nanoparticles Dispersed in Biolubricant

Productivity and cost-effectiveness are essential components of any long-term manufacturing system. While quantity and quality are linked to productivity, the economy focuses on energy-efficient processes that produce a high output-to-input ratio. Hard-to-cut materials have always been difficult to machine because of more significant tool wear and power losses. Inconel 625 is a hard material used in aerospace and underwater applications and is milled using biolubricants with nanoparticles. Palm oil is considered a biolubricant, and titanium dioxide (TiO2) and copper oxide (CuO) are selected as nanoparticles. When the combination of biolubricants and nanoparticles is added to the workpiece’s surface, it enhanced some properties while machining. Experiments involving four factors with four levels were carried out using the Taguchi design of experiments (DoE). The feed, depth of cut, speed, and coolant with nanoparticle additives were all factors. The responses were surface roughness, spindle vibration along X, Y, and Z axes, and material removal rate. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to alter the multiresponse optimization problem to a single-response optimization problem. The S/N of TOPSIS closeness coefficients was calculated, and the optimal machining conditions were determined. Surface roughness, material removal rate, and spindle vibration were reduced by 3.10%, 6.14%, 7.54% (Vx), and 6.78% (Vz), respectively, due to the TOPSIS optimization.

Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy.

Nitinol-shape memory alloys (SMAs) are widely preferred for applications of automobile, biomedical, aerospace, robotics, and other industrial area. Therefore, precise machining of Nitinol SMA plays a vital role in achieving better surface roughness, higher productivity and geometrical accuracy for the manufacturing of devices. Wire electric discharge machining (WEDM) has proven to be an appropriate technique for machining nitinol shape memory alloy (SMA). The present study investigated the influence of near-dry WEDM technique to reduce the environmental impact from wet WEDM. A parametric optimization was carried out with the consideration of design variables of current, pulse-on-time (Ton), and pulse-off-time (Toff) and their effect were studied on output characteristics of material removal rate (MRR), and surface roughness (SR) for near-dry WEDM of nitinol SMA. ANOVA was carried out for MRR, and SR using statistical analysis to investigate the impact of design variables on response measures. ANOVA results depicted the significance of the developed quadratic model for both MRR and SR. Current, and Ton were found to be major contributors on the response value of MRR, and SR, respectively. A teaching–learning-based optimization (TLBO) algorithm was employed to find the optimal combination of process parameters. Single-response optimization has yielded a maximum MRR of 1.114 mm3/s at Ton of 95 µs, Toff of 9 µs, current of 6 A. Least SR was obtained at Ton of 35 µs, Toff of 27 µs, current of 2 A with a predicted value of 2.81 µm. Near-dry WEDM process yielded an 8.94% reduction in MRR in comparison with wet-WEDM, while the performance of SR has been substantially improved by 41.56%. As per the obtained results from SEM micrographs, low viscosity, reduced thermal energy at IEG, and improved flushing of eroded material for air-mist mixture during NDWEDM has provided better surface morphology over the wet-WEDM process in terms of reduction in surface defects and better surface quality of nitinol SMA. Thus, for obtaining the better surface quality with reduced surface defects, near-dry WEDM process is largely suitable.

Multi-Response Optimization of Friction Stir Welding of AA2050 Using Response Surface Methodology Coupled with Grey Relational Analysis and Principal Component Analysis

The third generation aluminium-lithium alloy AA2050 finds wide applications in defence and aircraft industries by virtue of its high strength-to-weight ratio and excellent corrosion resistance. Friction stir welding (FSW), relatively novel technique, is more suitable to join this alloy compared to other conventional fusion welding techniques. In this work, the overall quality of the weld joint was decided from the higher values of tensile strength, yield strength, percentage elongation, hardness of weld zone, hardness of heat affected zone, bending load and lower value of width of heat affected zone. The optimal (combined) design was used to design the experiments with four numeric factors (traverse speed, rotational speed, tilt angle and shoulder diameter) and a categoric factor (tool pin profile). The multi-response optimization problem was reduced into a single-response optimization problem using grey relational analysis (GRA); principal component analysis (PCA) was used to assign optimal weighting values for the responses in the process of dimensionality reduction. Mathematical model for the reduced single response, which can be perceived as overall weld quality, was developed by the response surface methodology (RSM) and the optimization of process parameters was also carried by the RSM. Analysis of variance (ANOVA) was carried to evaluate the significance of each parameter on the overall weld quality and the adequacy of the developed model. The confirmation tests conducted at optimum levels of parameters proved the effectiveness and robustness of the method.

Optimasi Taguchi Menggunakan Metode Vikor dalam Pemilihan Ban Mobil

The main functions of tire are to withstand the vehicle’s load, continue the steering function and control the vehicle’s direction, transmit the driving force and vehicle’s braking on the road surface and reduce shock vibrations from the road surface. Therefore, optimal tire selection is something that needs to be considered for driving perfection. Taguchi methods is one of experimental design methods that is often used because of considerations of effectiveness and efficiency. However, Taguchi methods only can be used to solve a single response optimization problem. When dealing with multi-response problems, the responses are analyzed separately. This study aims to optimize Taguchi design using VIKOR method in production of car tires. The experimental design and responses used in this study were derived from car tire production data which analyzed using VIKOR methods and selected based on the optimum value to obtain the optimal combination of factors levels. The results of Taguchi's analysis are the first and third responses indicating the 16th experiment and second response indicating the 14th experiment which is the optimal combination. It can be concluded that Taguchi methods is inconsistent to producing optimal combination of levels of factors when dealing with multi-response problems, so additional methods are needed as Taguchi optimization methods in solving multi-response problems, including using VIKOR methods which results in the 2nd experiment being optimal combination.

Multi-objective optimization of a diesel engine fueled with different fuel types containing additives using grey-based Taguchi approach.

Due to the reduction of fossil fuels' resources and their contribution to environmental problems, biodiesel fuels have attracted significant attention as substitutes for diesel fuels. However, since their NOx emissions are higher than that of diesel fuels in most cases and also because of their higher viscosity than diesel, fuel additives are used to enhance their properties and reduce emissions. In this study, the effect of n-hexane and n-hexadecane addition to biodiesel and diesel fuels on exhaust emissions and performance of a single-cylinder diesel engine was investigated by using grey-based Taguchi method. Fuel additive, the additive amount, and fuel type were considered as the operating parameters. Three fuel types including diesel, rapeseed oil biodiesel, and cottonseed oil biodiesel were used in this investigation, while n-hexane and n-hexadecane were considered as the two fuel additives. As well as, three levels were assigned to the additive amount which were 4, 8, and 12%. Based on the operating parameters and their levels, the plan of experiments was generated according to L18 orthogonal array. Using grey relational analysis, this multi-response optimization problem was first transformed into a single response optimization. Then, this single system response, which is known as grey relational grade, was utilized in Taguchi approach for statistical evaluations. The results demonstrated that rapeseed was the best selection for fuel type compared to cottonseed and diesel in order to have the optimum system responses and hexadecane gave better results for system optimization in comparison with hexane additive. As well as, the analysis of variance showed that fuel type was the predominant operating factor influencing the grey relational grade which means fuel type was the most important parameter in the simultaneous optimization of exhaust emissions and engine performance. The Taguchi results also revealed that the optimum condition of engine performance and exhaust emissions happened when engine was fueled with rapeseed biodiesel containing 12% hexadecane as an additive. The confirmation test result validated the reliability of Taguchi approach in this investigation.

Experimental examination of the machining characteristics of Nimonic 80-A alloy on wire EDM

Nimonic 80-A alloys are very hard alloys with high-temperature strength having an extensive application in the aerospace industry. Wire Electric Discharge Machining (WEDM) is one of the finest non -conventional machining processes, which due to the material removal taking place using spark erosion with Wire EDM, makes it very easy to machine hard alloys and composites. The accuracy of machining them makes it one of the most advanced non-traditional processes. Kerf width is an important output response during machining on WEDM. The experimental investigation is done to find the optimum machining parameters to minimize the kerf width. The experimental investigation was done using the Taguchi analysis considering input factors peak current, gap voltage, and pulse-on time at three levels for machining on WEDM. Taguchi L27 orthogonal array was applied to generate the required experiments to be performed on WEDM. The output response data kerf width obtained is analyzed using signal-to-noise (S/N) ratio values generated by the Taguchi design methodology. The three main input parameters like peak current (20-80A), gap voltage (30–60 V), and pulse-on time (20–70 μs) are considered within this range. The single response optimization is done of the experimental kerf width data obtained for 27 different settings of input parameters of WEDM. The influence of each of the variables on kerf width is interpreted using the S/N ratio graphs, finding the most appropriate settings to minimize the kerf width. The results were obtained with a specific range of parameters that can be analyzed further with other optimization models, to get a wider range of optimum parameter values. The research on optimizing the output response kerf width for Nimonic 80-A alloy during machining on WEDM has not been reported much in the literature. This new optimized machining data would provide better scope to machine Nimonic 80- A alloy for minimizing the kerf width.

Effect of drilling process parameters on delamination factor in drilling of pultruded glass fiber reinforced polymer composite

Drilling of FRP composite materials finds widespread applications in many engineering fields but the Fiber breakage, delamination, spalling, pull-out, fuzzing are some of the difficulties This distinguishes FRP composite material drilling from ordinary material drilling. In the current work, The Pultruded glass fiber reinforced polymer composite (PGFRP) with hybrid filler produced at optimum pultrusion process parameters. Drilling characteristics such as drill bit lip angle, spindle speed, and feed rate have all been studied in view of delamination factor in the PGFRP composites with hybrid filler. Taguchi's single response optimization approach was used to find the best drilling parameters. It was found That optimum drilling parameter for optimal delamination factor in the PGFRP composite of given composition are spindle speed 3000 rpm, feed rate 200 mm/min and 1000 lip angle of twist drill.

Multiresponse optimization of end milling process parameters on ZE41A Mg alloy using Taguchi and TOPSIS approach

The effects of CNC end-milling process parameters on the machinability of Mg-alloy are discussed in the present investigation. The optimization of the process parameters namely cutting speed, feed rate and depth of cut has been carried out for material removal rate (MRR) and thermal stresses (TS) using Taguchi design of experiment and TOPSIS approach. The milling parameters have shown significant role towards the machining attributes. Analysis of variance (ANOVA) used to evaluate and analyse the relative importance of milling process parameters. For single response optimization, the most significant factor for MRR was feed rate and for thermal stress TS, it was cutting speed. However, feed rate has the greatest impact on preference value (PI), accounting for 71.85% of contribution to multi-response optimization. The recommended milling process parameters have determined to be optimum at cutting speed: 1200 RPM, feed rate: 100 mm/min and depth of cut: 1.25 mm by using the multi-response optimization technique.

MULTI-RESPONSE OPTIMIZATION OF END-MILLING PARAMETERS FOR INCONEL 625 USING TAGUCHI COUPLED WITH TOPSIS

Using Taguchi design of experiments (DoE), experiments were conducted with 3 factors and 3 levels. The factors were the depth of cut, spindle speed, and feed. The responses were surface roughness, flank wear, material removal rate, and spindle vibration along [Formula: see text] ([Formula: see text]), [Formula: see text] ([Formula: see text]), and [Formula: see text] ([Formula: see text]) axis. To convert the multi-response optimization problem into a single response optimization problem, the technique for order of preference by similarity to ideal solution (TOPSIS) was applied. The S/N of the closeness coefficients from TOPSIS was calculated and optimum machining conditions were obtained. Further, analysis of variance (ANOVA) was performed to verify which input parameter significantly affects the output responses. From TOPSIS optimization, the responses like surface roughness and flank wear were decreased by 0.99% and 2.55%. The vibration in [Formula: see text], [Formula: see text], and [Formula: see text]-axis decreased by 3.84%, 16.87% and 12.48% respectively. This optimization significantly enhances the machining characteristics.

Characteristic analysis of dissimilar metal weld for AISI304 with SA213T22 in super heater coils

Super heater is an inevitable component of any boiler system and failure of super heater leads to breakdown of whole plant. The integration of efficient quality welding technologies for dissimilar metals will be a key component in the successful weld quality for power plant components. In this investigation, an attempt has been made to study the dissimilar material AISI304 and SA213T22 tungsten inert gas welding is performed under different welding conditions current (100, 115, 130 Amps), gas flow rate (6, 8, 10 ltr/min), speed (2, 2.5, 3 mm/sec) and micro structure analysis performed to find influence of fusion heat. The Taguchi analysis is implemented to obtain single response optimization and grey relational analysis used to attain multi response for best yield strength, the ultimate strength, Vickers hardness and the elongation of the metals.

Abrasive water jet machining of CFRPs: single response optimization using taguchi method optimization

Taguchi method was applied to assess and optimize machining parameters and their effect on kerf characteristics during abrasive water jet machining (AWJM) of carbon fiber reinforced polymeric composites (CFRPCs). The main responses selected for these analyses are kerf width, kerf taper, metal removal rate, and surface roughness, the consistent machining parameters focussed for this study are abrasive flow rate, pressure, traverse rate, thickness of the workpiece and standoff distance, each parameter has three levels. Twenty-seven experiments were conducted on a typical CFRP composite workpiece materials based on Taguchi L27 design. The response curves and response tables were used to assess the data obtained to control the major significant process factors statistically affecting the kerf characteristics. The optimal settings of process parameters for each response are set up. From the analysis, it was detected that the percentage contribution of the control factors affecting the kerf width is standoff distance, workpiece thickness, abrasive flow rate, traverse rate, and pressure correspondingly. The results exposed that the thickness, feed rate, and standoff distance were the most significant factors affecting the kerf taper, metal removal rate, and surface roughness respectively.

Investigation of surface roughness in face milling processes

This study aims to investigate the effects of dry, minimum quantity lubrication (MQL), and nanofluid cutting conditions on surface roughness (Ra) and material removal rate (MRR) for Al6082-T6. Three controllable factors, namely, feed rate (Fr), spindle speed (Vs), and depth of cut (Dc) are studied at three levels using Taguchi method. Single-response optimization is conducted using S/N ratio and contour plots. Empirical models of Ra and MRR for all cutting conditions are developed, and analysis of variance (ANOVA) is used to measure the adequacy of these models. Experimental results reveal that 26~30% improvement in Ra could be observed when experimental setup shifted from dry to MQL, and 13~16% improvement is recorded when further shifted to nanofluid cutting condition. No remarkable effect of cutting conditions (dry, MQL, and nanofluid) is observed on MRR. Additionally, Vs is observed insignificant for MRR in all cutting conditions. The appropriate cutting conditions and optimum values of input variables are proposed to the practitioners for industrial machining and production when contemplating face milling processes.