Set Of Machining Parameters Research Articles

The Effects of Alloy Composition and Surface Integrity on the Machinability of Austenitic Stainless Steels 304 and 304L

Austenitic stainless steels, such as 304 and 304L, are extensively utilized in diverse industries due to their favorable properties, including biocompatibility, high durability, ductility, toughness at cryogenic temperatures, and excellent corrosion resistance. Additionally, these steels exhibit notable resistance to fatigue and oxidation. Despite these advantages, they are challenging to machine due to characteristics such as high work hardening, built-up edge formation, and low heat conductivity. The material 304L distinguishes itself from material 304 through its lower carbon content, making it more resistance to corrosion. 304L is experiencing a consistent rise in industrial demand. It is anticipated that this advanced material will progressively supersede 304 in various applications. The variability in alloy compositions and surface integrity of blanks can influence the tool wear and may even lead to abrupt tool breakage, necessitating supervision during machining operations. This study delves into the correlation between the alloy compositions, micro structure, surface integrity, and machinability of these special steels, focusing on turning processes. Various blanks of 304 and 304L in the form of bars, sourced from different manufacturers, were utilized in the study. These blanks exhibited slight variations in alloy composition (albeit within the standard range) and differed in the state of surface integrity characterized by variations in microstructure, grain size, microhardness, and residual stress. All blanks (across this array of materials) were subjected to turning using the same tool specifications and sets of machining parameters for comparative analysis. Various machinability indicators, including cutting forces, surface roughness, burr formation, tool wear, and chip morphology, were thoroughly examined. The findings highlight that the key factors influencing machinability include the microhardness of the surface and the residual stress state in the subsurface of the bars before the turning process. In contrast, changing the alloy composition within the standard range has hardly any effect on the machinability of these steels. The machinability of the examined specimens was adversely affected when the hardness exceeded 350 HV from the surface up to 2 mm below the surface and simultaneously the surface compressive residual stress exceeded −130 MPa.

Multi-objective optimization of 3D printing parameters to fabricate TPU for tribological applications

Nowadays, polymers frequently replace metals in several applications where comparable properties are desired due to the convenience of processing them as per the requirement. The present work intends to fabricate a thermoplastic polyurethane suitable for journal bearing application using fused deposition modelling (FDM) additive manufacturing method. The cylindrical samples were prepared for experiments using central composite design to examine the effect of FDM machine parameters (such as layer thickness, infill density, and printing speed) on response such as specific wear rate (SWR), coefficient of friction (COF) and hardness. Layer thickness was noticed to be the most significant parameter for the selected responses with a percentage contribution of ∼34% to 72%. Further, as observed from interaction plots the COF and SWR are lowest, and hardness is highest at highest infill density and lowest printing speed. To obtain an optimized set of FDM machine parameters for minimum SWR, COF and maximum hardness, genetic algorithm based multi-objective optimization is performed. The optimized values are SWR of 4.97 × 10−5 mm3/N-m, COF of 0.37 and hardness value of 37.

INVESTIGATIONS ON THE MACHINABILITY CHARACTERISTICS OF Al–Zn(5.4)–Mg(2.4)–Cu(1.4) ALLOY COMPOSITE USING TOPSIS APPROACH

In this research, stir casting was used to create the aluminium alloy (AA7075) composite filled with 10[Formula: see text]wt.% zirconia (ZrO2) particles as reinforcement. Die sinking electric discharge machining (EDM) was used to examine the machining characteristics of the proposed composite. The machining experiments were executed in accordance with the [Formula: see text] orthogonal layout. Here, the material removal rate (MRR) and surface roughness (SR) were taken into account as the output responses, and the discharge current ([Formula: see text]), pulse on-time ([Formula: see text]), and pulse off-time ([Formula: see text]) were taken as the machining parameters. To determine the optimal parameter conditions for the output responses, the technique for order preference by similarity to ideal solution (TOPSIS) approach was applied. According to the experimental findings, the optimum settings of the parameters are determined at 15[Formula: see text]amps of ‘[Formula: see text]’, 750[Formula: see text][Formula: see text]s of ‘[Formula: see text]’ and 50[Formula: see text][Formula: see text]s of ‘[Formula: see text]’. ANOVA results stated that ‘[Formula: see text]’ has the most notable factor with a contribution of 90.85%. The interaction effect of parameters on the responses was shown by the contour plots. The confirmatory experiment was finally conducted at the optimum machining parameter settings, and it was found that the error only occurred in 8.4%.

Automated determination of the ion-recombination correction factor (ksat) in ultra-high dose rate electron radiation therapy.

Plane-parallel ionization chambers are the recommended secondary standard systems for clinical reference dosimetry of electrons. Dosimetry in high dose rate and dose-per-pulse (DPP) is challenging as ionization chambers are subject to ion recombination, especially when dose rate and/or DPP is increased beyond the range of conventional radiotherapy. The lack of universally accepted models for correction of ion recombination in UDHR is still an issue as it is, especially in FLASH-RT research, which is crucial in order to be able to accurately measure the dose for a wide range of dose rates and DPPs. The objective of this study was to show the feasibility of developing an Artificial Intelligence model to predict the ion-recombination factor-ksat for a plane-parallel Advanced Markus ionization chamber for conventional and ultra-high dose rate electron beams based on machine parameters. In addition, the predicted ksat of the AI model was compared with the current applied analytical models for this correction factor. A total number of 425 measurements was collected with a balanced variety in machine parameter settings. The specific ksat values were determined by dividing the output of the reference dosimeter (optically stimulated luminescence [OSL]) by the output of the AM chamber. Subsequently, a XGBoost regression model was trained, which used the different machine parameters as input features and the corresponding ksat value as output. The prediction accuracy of this regression model was characterized by R2-coefficient of determination, mean absolute error and root mean squared error. In addition, the model was compared with the Two-Voltage (TVA) method and empirical Petersson model for 19 different dose-per-pulse values ranging from conventional to UDHR regimes. The Akiake Information criterion (AIC) was calculated for the three different models. The XGBoost regression model reached a R2-score of 0.94 on the independent test set with a MAE of 0.067 and RMSE of 0.106. For the additional 19 random data points, the ksat values predicted by the XGBoost model showed to be in agreement, within the uncertainties, with the ones determined by the Petersson model and better than the TVA method for doses per pulse>3.5Gy with a maximum deviation from the ground truth of 14.2%, 16.7%, and -36.0%, respectively, for DPP>4Gy. The proposed method of using AI for ksat determination displays efficiency. For the investigated DPPs, the ksat values obtained with the XGBoost model were in concurrence with the ones obtained with the current available analytical models within the boundaries of uncertainty, certainly for the DPP characterizing UDHR. But the overall performance of the AI model, taking the number of free parameters into account, lacked efficiency. Future research should optimize the determination of the experimental ksat, and investigate the determination the ksat for DPPs higher than the ones investigated in this study, while also evaluating the prediction of the proposed XGBoost model for UDHR machines of different centers.

On learning latent dynamics of the AUG plasma state

In this work, we demonstrate the utility of state representation learning applied to modeling the time evolution of electron density and temperature profiles at ASDEX-Upgrade (AUG). The proposed model is a deep neural network, which learns to map the high dimensional profile observations to a lower dimensional state. The mapped states, alongside the original profile's corresponding machine parameters, are used to learn a forward model to propagate the state in time. We show that this approach is able to predict AUG discharges using only a selected set of machine parameters. The state is then further conditioned to encode information about the confinement regime, which yields a simple baseline linear classifier, while still retaining the information needed to predict the evolution of profiles. We, then, discuss the potential use cases and limitations of state representation learning algorithms applied to fusion devices.

Statistical modelling and process optimization of EDM electrode fabricated using pressureless microwave sintering and rapid tooling route

The fabrication of customized products necessitates specialized setups, such as tools and dies, capable of accommodating complex shapes. In the present work, rapid tooling, an indirect 3D printing process along with pressureless microwave sintering was employed to fabricate complex shaped electric discharge machine (EDM) electrode. Copper spherical particles were used as the material for sample fabrication, and central composite design technique was utilized to conduct experiments. The investigation focused on exploring the impact of microwave sintering machine parameters – sintering temperature, heating rate and holding time – on crucial electrode properties, including density, volumetric shrinkage and electrical conductivity. The dominance of the sintering temperature on responses was revealed over the heating rate and holding time. Additionally, the effect of interaction between heating rate and holding time on electrical conductivity and density showed less effect of large holding time at slower heating rate. To investigate the particle bonding behaviour, scanning electron micrographs of the fabricated samples were also studied. The adopted methodology effectively sintered the particles without the need for applying pressure. Using genetic algorithm (GA), a multi-objective optimization was conducted to achieve an optimized set of microwave sintering machine parameters, aiming for maximum density, electrical conductivity and minimum volumetric shrinkage. A density of 7.32 g/cc, volumetric shrinkage of 8.36% and electrical conductivity of 73.88% IACS was obtained from samples sintered at optimized sintering parameters. The dimensions of the fabricated electrodes and machined cavity were then compared with the computer-aided design model. The results revealed an efficient process for fabricating complex shaped EDM electrodes.

Scientific Basis for the Substantiation of Process Regulations for the Micro-Cutting of Hardened Gears

Abstract The paper considers scientific principles of technological procedure development for the selection and assignment of parameters of velocity machining by blade tool at gear milling with due regard to the required parameters of the gear surface layer. Optimum micro-cutting conditions are determined when full participation in stock removal of all mill teeth is provided. Technological regulation of selection and setting of machining parameters is spread for gear hobbing of the spur gear wheels with fine-modular and coarse-modular hardness (220...320 HV) and after heat treatment (HRC46...63). Correlation with essential parameters of the surface layer, cutting depth, feed, speed and radius of rounding of the cutting edge of milling cutters’ teeth is established. It is scientifically grounded, tested, and proved at what modes of cutting, the radius of rounding of cutting edge of the cutting tool is made metal removal at oncoming and passing high-speed micro milling. The process of determining of minimal sliding angle, or maximum value (without the use of coolant and with coolant), in which there is no micro-cutting process taking into account variation of variable parameters of cutting conditions and geometrical parameters of hobbing cutters is modelled. For the first time, we ascertained the relation between the sliding angle and the maximum contact angle of the mill tooth with the work surface, which allowed determine the efficiency of the shaping process through coefficient (without coolant and with coolant).

Experimental investigation on the superhydrophobic property of laser micromachined stainless steel 316L surface

Compared to conventional machined surfaces, superhydrophobic surfaces have advantages such as anti-corrosion, anti-bacterial, anti-biofouling, and self-cleaning qualities. In the production of biomedical, pharmaceutical, petrochemical, food processing, and marine parts that demand superhydrophobic surfaces, stainless steel 316L (SS 316L) finds use. One of the industrial procedures used to create micro-textures on the surface of a material to promote hydrophobicity is laser beam machining. The objective of this study is to use an Nd: YAG laser to create superhydrophobic surfaces through a planned set of experiments. In this work, it was analysed how the laser beam machining process parameters affected the contact angle, which is a crucial factor in determining whether a surface is hydrophobic. To find the ideal set of laser machining parameters to achieve superhydrophobicity in the shortest period, the Taguchi and ANFIS methods were used to optimise the Laser Beam Machining process parameters.

Multiobjective Optimization of Stereolithography for Dental Bridge Based on a Simple Shape Model Using Taguchi and Response Surface Methods

Stereolithography (SLA) has become an essential photocuring 3D printing process for producing parts of complex shapes from photosensitive resin exposed to UV light. The selection of the best printing parameters for good accuracy and surface quality can be further complicated by the geometric complexity of the models. This work introduces multiobjective optimization of SLA printing of 3D dental bridges based on simple CAD objects. The effect of the best combination of a low-cost resin 3D printer’s machine parameter settings, namely normal exposure time, bottom exposure time and bottom layers for less dimensional deviation and surface roughness, was studied. A multiobjective optimization method was utilized, combining the Taguchi method with response surface methodology and the desirability function technique. The predicted optimal values for the cube’s dimensional deviation and surface roughness were 0.0517 mm and 2.8079 µm, respectively. The experiments’ validation of the findings confirmed the results, which were determined to be 0.0560 and 0.064667 mm and 2.770 and 2.6431 µm for the dimensional deviation and surface roughness for the cube and bridge, respectively. The percentages of prediction errors between the predicted optimum results and the printed response were 7.68% and 1.36% for dimensional deviation and surface roughness, respectively. This study demonstrates that the robust method used produced a dental bridge with good accuracy and a smooth surface.

Understanding of X-Ray Machine Parameter setting (On X-ray controller)

The efficient and accurate operation of X-ray machines is paramount in various industries. This paper delves into the critical aspect of understanding X-ray machine parameter settings, aiming to provide insights into the complexities involved in optimizing these settings for optimal imaging quality. The paper explores the fundamental parameters that govern X-ray production, such as tube voltage, current and exposure time. It examines the intricate interplay between these parameters and their impact on image quality and material penetration. By comprehending these relationships, an X-Ray operator can make informed decisions to balance the need for quality imaging.

Simulation of fluid dynamics and turbulence during phacoemulsification using the new propeller turbo tip

PurposeTo investigate the fluid dynamics and turbulence in the anterior chamber during phacoemulsification with a new propeller turbo tip using computational fluid dynamics methods.MethodsA theoretical study, three-dimensional model with the...

Multi-Objective Optimization of AISI P20 Mold Steel Machining in Dry Conditions Using Machine Learning—TOPSIS Approach

In the present research, AISI P20 mold steel was processed using the milling process. The machining parameters considered in the present work were speed, depth of cut (DoC), and feed (F). The experiments were designed according to an L27 orthogonal array; therefore, a total of 27 experiments were conducted with different settings of machining parameters. The response parameters investigated in the present work were material removal rate (MRR), surface roughness (Ra, Rt, and Rz), power consumption (PC), and temperature (Temp). The machine learning (ML) approach was implemented for the prediction of response parameters, and the corresponding error percentage was investigated between experimental values and predicted values (using the ML approach). The technique for order of preference by similarity to ideal solution (TOPSIS) approach was used to normalize all response parameters and convert them into a single performance index (Pi). An analysis of variance (ANOVA) was conducted using the design of experiments, and the optimized setting of machining parameters was investigated. The optimized settings suggested by the integrated ML–TOPSIS approach were as follows: speed, 150 m/min; DoC, 1 mm; F, 0.06 mm/tooth. The confirmation results using these parameters suggested a close agreement and confirmed the suitability of the proposed approach in the parametric evaluation of a milling machine while processing P20 mold steel. It was found that the maximum percentage error between the predicted and experimental values using the proposed approach was 3.43%.

Evaluating machining performance of acrylonitrile-butadiene-styrene (ABS) based electrical discharge machining (EDM) electrodes fabricated by fused deposition modelling (FDM) followed by a novel metallization method

In the present study machining performance of a novel ABS P400 based EDM electrode is experimentally investigated. Three EDM parameters that is, current ( I), pulse on time ( Ton) and pulse off time ( Toff), each at three levels, are considered, and mild steel is machined using these electrodes according to the response surface methodology (RSM) based face-centred central composite design (FCCCD). Machining performances such as material removal rate ( MRR), tool wear rate ( TWR) and surface roughness ( SR) are measured. Experimental results are analysed using analysis of variance (ANOVA), response graphs and 3D surface plots. Current is found to be the dominating parameter for MRR, TWR and SR. The optimal combination of EDM parameter for multi-performance is determined using teaching-learning based optimization (TLBO) algorithm. The effectiveness of the new electrodes is established by comparing their machining performances with the already developed electrode at the same machining parameter setting. MRR resulting from the new electrode and also its TWR are found to be in good agreement with those of already produced electrodes. However, SR of the machined surface using new electrodes is found to be relatively higher.

Kinodynamic Generation of Wafer Scanners Trajectories Used in Semiconductor Manufacturing

The operation time of an ideal reliable wafer scanner model is defined at the die level where the actual exposure process takes place as the time unit per die, or at the wafer substrate level as the time unit per wafer substrate. Therefore, the machine throughput is given as the reciprocal of the operation time. The involved motion profiles of a machine, namely the step-and-scan trajectories, function as the heartbeats that drive its multidisciplinary elements, which suggests that a multidisciplinary design optimization should be involved when such profiles are selected or designed. This is also true when considering the traverse motion profiles among rows and columns within the wafer substrate. The step-and-scan trajectories affect the machine throughput, performance, and die yield. The effects of tracking such profiles appear as structural vibration, tracking errors, and thermal loading at various machine elements such as the actuators, the reticle, the wafer, and the projection elements specifically when the exposure high-energy duration and frequency are not taken into consideration while designing the reference motion. From the dynamics perspective, having a reference motion with nonzero and bounded higher-order derivatives is recommended since it enhances the tracking performance of the machine, however, its ability to increase the operation time is usually overlooked. In an attempt to understand such effects, we present a case study that outlines the aforementioned aspects using three step-and-scan profiles of mainly <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3^{rd}$ </tex-math></inline-formula> -order. Taking the dynamics of the driven stage into consideration through input shaping, both the step-and-scan and traverse motion profiles are analyzed. We provide analytical expressions that can be used to generate both types of motion profiles on the fly without additional optimization. A simulation example of a simplified wafer scanner machine shows the usefulness of the proposed framework. Note to Practitioners—Choosing the most suitable operating conditions of a lithography machine is challenging. These conditions affect machine productivity, profit margin, and maintenance. In this paper, we reveal the relation between the selection of operating conditions based on several decision variables- and the kinodynamic step-and-scan trajectory generation based on specific machine parameters and clients’ requirements. Being chart-based, the selection process of an operating point can be less practical at some points. However, using appropriate curve fitting tools, the information provided in the optimal operating charts can be put into suboptimal closed-form expressions that facilitate the selection process. Therefore, the designed trajectories parameters can be easily saved in lookup tables for ease of evaluation and future use. This helps in accommodating changes in the operation plans and flexible manufacturing systems. Also, starting with a given set of machine parameters, it is possible to calculate the optimal machine operating point when the input shaping technique is used, as illustrated in this paper.

Mechanical peck drilling of Inconel 625: Parametric analysis and grey fuzzy logic based multi-response optimization

Nickel-based superalloy Inconel 625 is considered a hard-to-cut material due to characteristics such as high hardness, strain hardening behavior, and affinity with tool materials. It is challenging to machine through micro-holes of less than Ø 500 µm with the desired quality on Inconel 625 using the mechanical micro-drilling (MMD) process under dry conditions. The machining parameters must be selected optimally to enhance hole quality and to avoid frequent drill breakages. In this study, through micro-holes are machined on Inconel 625 by the MMD process using solid carbide micro-drills under dry conditions using a peck drilling strategy. Experiments are carried out by varying machining parameters such as spindle speed (8000 rpm, 11000 rpm, and 14000 rpm), feed (5 µm/rev, 7.5 µm/rev, and 10 µm/rev), and drill diameter (Ø 300 µm, Ø 400 µm, and Ø 500 µm) at three levels based on full factorial design. Thrust force and hole quality features such as exit burr height, radial overcut, and taper angle are measured as output responses. The mean effect plots are used to study the influence of machining parameters on output responses. The drill diameter had a significant effect on thrust force and radial overcut. Whereas feed and spindle speed had a major influence on exit burr height and taper angle, respectively. Finally, multi-response optimization is carried out using the grey fuzzy logic method, and an optimal machining parameter setting for multiple responses is ascertained.

Analysis of the effect of material thickness during the WEDM processon cutting speed, topography and morphology

Wire electrical discharge machining (WEDM) is an unconventional machining technology that is indispensable in many industries. Machining is performed using the thermoelectric principle, while it is possible to machine all at least minimally electrically conductive materials. Due to the wide range of applications of WEDM, it is necessary to ensure the appropriate quality of machined surfaces, regardless of the thickness of the machined materials, while maintaining an acceptable cutting speed. For this purpose, this study was performed to analyse the effect of material thickness on the cutting speed, morphology and topography of Ampcoloy 35 material. In this study, thicknesses from 5 to 160 mm were analysed in 5 mm increments with the same machine parameter settings. The surface topography and morphology were studied using electron and light microscopy, and cross-sections of the sample were created to examine the condition of the subsurface layer. It was found that the values of Ra at the edge ranged from 1.8 to 3.2 µm and in the middle of the sample from 1.7 to 3 µm, while the trend of increasing Ra with thickness is not visible. It is clear from the morphology analysis that a rugged surface with more craters was created at the edges than in the middle of the sample, while segregated lead needles were also formed at the edges of the samples.

Long-term process stability in additive manufacturing

Laser powder bed fusion (L-PBF), also known as selective laser sintering or direct laser melting, is an additive manufacturing process in which part geometries are formed simultaneously with the underlying material. The microstructure, defect content, and surface quality are all synthesized conjointly with the part shape. While the geometric design freedom allowed by this process enables new complex features and parts with small (∼1 mm) features, challenges associated with process qualification can deter wider adoption. Furthermore, a lack of historical performance data for statistical process control of witness coupons, for either bulk material or for small features, makes the barrier to entry more difficult. Here, we demonstrate long-term, property-based process monitoring and variability assessment using both small-featured (1 mm) and larger, bulk-representative material witness coupons. Over a one-year period, more than 550 tensile bars and 80 Charpy impact bars were printed alongside 316 L stainless steel parts built using L-PBF and tested to detect shifts in the process over time. Miniature tensile bars with a 1 mm2 gage area were tested using a high throughput mechanical testing system. In parallel, a larger test coupon was used to monitor density, hardness, and Charpy impact toughness. This collection of measurements was used to determine detectable property shifts correlated to L-PBF process changes including powder feedstock, machine hardware, software versioning, and machine parameter settings. The benefits of using small featured, high-throughput samples are discussed based on process sensitivity and the number of repeat tests possible for each build. This study not only reveals the utility of property-based process monitoring but illustrates the sensitivity of these measurements to detect process changes and provides further evidence for property stability in modern L-PBF.

Evaluating CNC Milling Performance for Machining AISI 316 Stainless Steel with Carbide Cutting Tool Insert.

The present study investigates the CNC milling performance of the machining of AISI 316 stainless steel using a carbide cutting tool insert. Three critical machining parameters, namely cutting speed (v), feed rate (f) and depth of cut (d), each at three levels, are chosen as input machining parameters. The face-centred central composite design (FCCCD) of the experiment is based on response surface methodology (RSM), and machining performances are measured in terms of material removal rate (MRR) and surface roughness (SR). Analysis of variance, response graphs, and three-dimensional surface plots are used to analyse experimental results. Multi-response optimization using the data envelopment analysis based ranking (DEAR) approach is used to find the ideal configuration of the machining parameters for milling AISI 316 SS. The variables v = 220 m/min, f = 0.20 mm/rev and d = 1.2 mm were obtained as the optimal machine parameter setting. Study reveals that MRR is affected dominantly by d followed by v. For SR, f is the dominating factor followed by d. SR is found to be almost unaffected by v. Finally, it is important to state that this work made an attempt to successfully machine AISI 316 SS with a carbide cutting tool insert, to investigate the effect of important machining parameters on MRR and SR and also to optimize the multiple output response using DEAR method.

Performance analysis of EDM electrode manufactured by direct metal laser sintering during machining of titanium alloy (Ti6Al4V)

Recently, additive manufacturing based rapid tooling (RT) is gaining popularity in manufacturing industries because tool fabrication time and cost can be substantially reduced. In this regard, an experimental investigation is made to explore the machining performance of AlSi10Mg tool electrode manufactured through direct metal laser sintering and compared with commonly used copper and graphite electrodes in electro-discharge machining (EDM) of titanium alloy work piece. The influence of EDM variables on performance measures like material removal rate, tool wear rate, arithmetic mean surface roughness, surface crack density, white layer thickness and microhardness has been analyzed. The machined surface generated with the usage of different electrodes is inspected by scanning electron microscopy to study the surface integrity. It is observed that superior surface integrity can be attained utilizing AlSi10Mg DMLS electrode at lower setting of machining parameters. Energy dispersion X-ray spectroscopy analysis reveals that transfer of tool electrode elements onto the machined surface occurs along with increase in percentage of carbon and oxygen on the machined surface. From the X-ray diffraction analysis, it is found that metal carbides like titanium carbide and vanadium carbide are formed on the machined surface leading to increase in microhardness of the surface.

Near dry turning of EN8 and EN31 steel: multi-objective optimization using grey relational analysis

Steel is the most commonly employed material in various engineering applications, and their successful machining demands finding the optimized set of machining parameters along with appropriate cooling strategies. Moreover, the significance of process parameter optimization is progressively perceived in the wake of expensive CNC machine adaptation on the shop floor for machining. Further, a competent cooling strategy is essential with a minimal amount of coolant to obtain the best quality products. In the present work, the optimization of process parameters for Near Dry Turning (NDT) of two steel grades, EN8 and EN31, was done. NDT utilizes a minimal coolant with a major amount of compressed air. For competent cooling, Al2O3 nanofluid as coolant was used with compressed air. Speed, feed, and depth of cut were taken as the machining parameters for the turning process. Two response variables, the surface roughness of machined specimen and cutting zone temperature, were considered for the analysis. Three levels of each turning parameter were chosen, and the Taguchi L9 orthogonal array was adopted for the experimentation. The optimized turning parameter was found through the Grey Relational Analysis (GRA). Further, the applicability of compressed air was also presented to achieve sustainable and green machining to eliminate the negative impact on environmental footprints. For this purpose, results at the obtained optimized set of parameters were compared with plain base fluid and compressed dry air as coolants. The reduction in surface roughness of ∼12.3% and ∼14.6% for EN8 and EN31 steel were observed using nanofluid in near dry turning. Similarly, the reduction in cutting zone temperature was ∼7% in both cases. These results show the significance of process parameter optimization and the applicability of nanofluid in near dry turning of steels.