Enhancing Tool Life Research Articles

Multiple solutions of tungsten carbide-cobalt cutting tool turning performance optimization results by Taguchi's combined multiobjective approach analysis an exponentiated exponential Weibull Dagum distribution model

Manufacturers employ various techniques to enhance tool life and influence both production costs and surface finish quality. Recently, cryogenic treatment has gained attention for improving machining performance. In this study, tungsten carbide-cobalt (WC–Co) cutting tools underwent cryogenic treatment at −196 °C for 24 h followed by tempering at 200 °C for 2 h. Machinability characteristics including Tool Wear Rate and Surface Roughness (Ra) were assed for Cryo Treated (CT) cutting tool during spinning operations on Al6063 alloy. This evaluation was compared with an untreated tool. Results demonstrate that the CT tool significantly enhances tool longevity while ensuring a satisfactory surface finish. To optimize the performance of the CT tool in turning processes, multiobjective optimization techniques, namely Taguchi's Grey Relational Analysis and Technique for Order Preference by Similarity to Ideal Solution, were utilized. In this research exponentiated exponential Weibull Dagum (EEWD) model has been utilized with the goal of increasing the tractability of the DGM (Dagum) in modeling the real-world data. The main object of this work, an innovative generalization of the EEWD distribution of Quantile Function (QntFEEWD) has been derived with six parameters. This QntFEEWD is significantly influent in driving measures of position such as the Qutl (quartiles), Octl (octiles), Decl (deciles), Pecl (percentiles), and median of EEWD distribution. The T-R{.} framework has been recently used to generalize different distributions; however, the DGM distribution's viability has not been examined. The T-R{.} combines three distributions, with one acting as a standard distribution. Each distribution's combined potency, which is a weighted hazard function of the baseline distribution, would have more parameters but be able to handle dataset bimodality with great flexibility. In order to generalize the DGM distribution, this paper used the quantile function of the Weibull distribution. The current study presents the EEWD, a novel generalized six parameter (6P) model.

Investigation of machining performance in SS904L alloy under hybrid cooling conditions

ABSTRACT Hard turns in machining present substantial obstacles to chip removal. An effective heat management system is needed to avoid tool overheating and reduce cutting capacity. Therefore, the present investigates the effectiveness of a comparative study of four cooling methods, such as dry turning, minimum quantity lubrication (MQL), cryogenic (LN2), and the hybrid Cryo-nMQL approach, on machining performance during the machining of SS904L alloy. Machining conditions included 75 and 150 m/min cutting velocities and feed rates of 0.1 and 0.2 mm/rev, respectively. Results indicate that cryo-nMQL exhibited superior performance matched to the other cooling methods, with a maximum improvement of surface roughness by 57.73% and nose wear by 13.87%, flank wear by 67.48%, crater wear by 8.42%. In addition, chip production indicates a fine lamella structure with less serration under cryo-nMQL. In addition, the combination of cryo-nMQL resulted in enhanced tool life and a notable decrease in tool wear with a research gap, indicating future work to assess the benefits of hybrid cooling methods in SS904L machining, thereby showcasing the potential of these innovative technologies in mechanical engineering applications.

Tribological and investigation study of a dlc-coated H13 friction drilling tool on AZ31B magnesium alloy

IntroductionFriction drilling is an innovative method in hole-making for sheet metal applications, thin sheets of conventional structural alloy materials like copper, titanium, steel etc., even though there are other methods, such as thermal distortion for the welding of nuts, riveting of nuts, and threading. For the last hundred years, researchers have focused on studying the development of this technique to maintain strength, hole roughness, hole geometry, hardness etc. It is interested in finding solutions for wear, tool life, and plastic deformation.MethodFriction drilling is also called a green hole-making process because this process uses the friction between the rotating tool and the workpiece. In this research, instead of regular HSS and Tungsten Carbide tools, the H13 tool steel is used, because the H13 steel tool has unique chemical compositions like chromium and molybdenum, which give high toughness, hot hardness, and wear resistance. Diamond-like-carbon (DLC) coating has been used in this research to enhance tool life and AZ31B magnesium alloy is used as the work material. Initially, in this research, the wear stability of the DLC-coated H13 tool was investigated, and later, the tool surface roughness and hole quality were verified.Results and DiscussionThe material loss observed for the DLC-coated H13 steel tool in the pin-on-disk test was 0.05 g. This investigation used two different diameter tools, namely 3 and 7 mm. Research has concluded that the 7 mm tool is better for friction drilling by seeing the roughness and hole quality. However, the conditions were that the spindle should rotate at 4,000 rpm and the feed rate of the tool to be at 200 mm/rev.

Investigating the performance of the pressurized injection lubrication technique in the turning process

Efficient lubrication and cooling are crucial in machining operations to enhance tool life and workpiece quality. Sustainable methods like minimum quantity lubrication (MQL) and dry cutting often face limitations in cooling efficiency and chip evacuation, especially under high-speed conditions or when machining difficult-to-cut materials such as stainless steel. This study introduces the novel pressurized injection lubrication (PIL) technique designed to address these challenges by optimizing lubrication, cooling, and chip evacuation during the turning operations of stainless steel 304. Using flaxseed oil as the lubricant, the PIL system employs a 0.26 mm stream diameter at a pressure of 16 bar to provide the necessary cooling and lubrication to the cutting zone. Cutting temperature and surface roughness were selected as the primary responses. Experimental runs were designed using the Taguchi L9 method. Analysis of variance showed that the lubrication method significantly affected the cutting temperature, with a contribution percentage approaching 94%. The experimental results demonstrated that PIL reduced the cutting temperature by up to 55%, while MQL reduced it by about 48%, both compared to dry cutting at the highest utilized speed. The lubrication method was also found to be the most significant factor affecting surface roughness, with a contribution percentage of 72.8%. Experimentally, PIL improved surface roughness by a maximum of 16.2% compared to MQL. Additionally, PIL maintained low oil consumption (0.9 l/h) and energy usage (< 0.017 kWh). The cost-effective PIL setup, priced under 65 USD, underscores its potential as a sustainable and efficient alternative for machining processes. The system’s components are readily available, facilitating easy integration into existing metal-cutting machines. Finite element analysis (FEA) modeling was used to predict residual stresses under different lubrication methods. The FEA model indicated that PIL and MQL reduced residual stresses by about 81.2% and 76.6%, respectively, compared to dry cutting at a speed of 500 rpm. These findings suggest that PIL can significantly enhance machining performance and sustainability, offering a viable solution to modern manufacturing challenges.

Active-passive hybrid feed rate control systems in CNC machining: Mitigating force fluctuations and enhancing tool life

Real-time optimization of machining processes for aerospace structural components is imperative due to the difficult-to-cut materials and complex structures. Effective feed rate control in CNC machining plays a key role in achieving high-quality results. While current research trends in mass production emphasize the utilization of adaptive control algorithms and controllers within machining systems, there remains a need to enhance the adaptability of these control systems. This study introduces an active-passive hybrid feed rate control system designed to maintain consistently stable cutting conditions and extend tool life. The hybrid feed rate control system combines offline active pre-compensating, a scheduled pre-compensating feed rate profile, and an online feed rate passive fine-tuning with a real-time adaptive control loop in computer numerical control (CNC) machining. The response speed is enhanced by offline active pre-compensation, whereas the control precision is improved by online passive fine-tuning with a fuzzy controller. Four control cases were tested separately throughout the tool lifespan, including the conventional and adaptive control methods. The proposed adaptive control method reduced the maximum slope from 3.6 to 1.2, demonstrating superior performance compared to both its individual components and other case studies. The results showed a significant 25 % increase in tool life, with a slight decrease in machining efficiency of 7.35 % during the entire tool lifespan.

Enhancement of tool life using magneto-rheological fluid damping and tool wear prediction through deep learning model in milling

Undesirable vibrations generated during milling significantly intensify milling tool wear and reduces tool life. Hence it is imperative to employ the effective vibration control strategy during milling to optimize the overall effectiveness of the process. Thus, the present study incorporates the magneto-rheological fluid damping for vibration mitigation during the milling to enhance the life of a cutting tool under different operating conditions. Simultaneously, the data-driven modelling are employed to predict the tool wear using real-time multi-sensor machining data under different operating conditions. Accordingly, the real-time multi-sensor data was acquired at different operating conditions during normal and vibration-damped milling. Later, the extracted tine-domain features were selected using the sequential feature selection with Random Forest Regressor to obtain the most relevant time-domain features from multi-sensor data for improving the model performance. These selected features are then fed to the different deep-learning algorithms for tool wear prediction. Thus, Magneto-rheological fluid damping significantly reduces the vibrations and cutting forces in vibration-damped milling between 65 and 80% and improves the tool life by 18–21 % compared to normal milling under different operating conditions. Moreover, among the different deep learning algorithms, hybrid Encoder-Decoder Long short-term Memory is considered the optimal choice for tool wear prediction due to highest accuracy with coefficient of determinations (R2) ranging from 0.980 to 0.995 with the lowest mean squared error between 22 and 50 μm under varying operating conditions during normal and vibration-damped milling. Thus, it facilitates the significant enhancement in the tool life under different operating conditions, and robust data-driven model for the effective implementation of predictive maintenance approach.

Combination of minimum quantity lubrication (MQL) with solid lubricant (SL): challenges, predictions and implications for sustainability

This literature review explores minimum quantity lubrication (MQL) machining, emphasizing its benefits over traditional dry and flooded cooling methods. MQL is vital in manufacturing, enhancing productivity and sustainability. Adding solid lubricants (SL) amplifies MQL’s advantages. This article identifies challenges in MQL + SL machining and outlines future research opportunities. A comprehensive four-step review was conducted: identifying research gaps, determining databases and search terms, defining paper selection methods and performing bibliometric analysis. The review abstracted information from selected articles, offering in-depth discussions. Challenges in MQL machining involve forces, temperature, tool wear, roughness and lubricant performance. Studies show incorporating SL into base fluids mitigates these issues. Sustainability is a core focus in modern manufacturing, and MQL’s eco-friendly reputation is deserved. MQL with SL, often in nanoparticle form, provides benefits like reduced friction, enhanced tool life, lower energy consumption and improved material disposal. In conclusion, MQL + SL machining offers a promising path to balance productivity and sustainability. Continued research and development are essential to overcome challenges and unlock its full potential. As manufacturing evolves, MQL + SL machining exemplifies innovation and sustainability, poised to reshape the industry’s future.

Enhancing surface quality and tool life in SLM-machined components with Dual-MQL approach

Selective laser melting (SLM) can produce complex metal components with high densities, thereby surpassing the limitations of traditional machining methods. However, achieving accurate dimensions, geometries, and acceptable surface states in parts fabricated through SLM remains a concern as they often fall short compared to traditionally machined components. As a solution, a hybrid additive-subtractive manufacturing (HASM) method was developed to effectively utilize the advantages of both techniques. In this study, SLM-made 316 L stainless steel was machined under distinct cooling conditions to investigate the effects of roughness and tool wear. After a thorough investigation, the dual-MQL strategy was evaluated and compared with dry and MQL cutting strategies. The findings showed that the dual-MQL condition led to a significant reduction in flank wear by 54–56% and 29–34%, respectively, associated with dry and MQL cutting techniques, making it a highly promising key for machining SLM-made steel components. Machine learning techniques are potential tools for prediction and classification capabilities in machining processes. For milling SLM-made 316 L SS, multilayer perceptron (MLP) proved to be the most effective prediction model and for classification MLP and Random forest performed better.

Orthogonal array and artificial neural network approach for sustainable cutting optimization machining of 17-4 PH steel under CNC wet turning operations

Sustainable manufacturing strives to increase output while minimizing resource usage, costs, and environmental impact. Tool longevity is crucial, considering material, power, and resource consumption. Challenges like chip removal, heat, and friction necessitate effective solutions. Cutting fluids, like rice bran oil, reduce temperatures, friction, and enhance tool life and surface quality. Research focuses on using rice bran oil with a TiAlN tipped tool to minimize friction and cutting forces. Taguchi's robust design and L-9 orthogonal array reduce experimental trials. Controllable factors include speed, feed, depth of cut, and rice bran oil flow rate, with surface roughness as the performance metric. Taguchi analysis improves turning performance, particularly with 17-4 PH stainless steel. Rice bran oil reduces frictional forces, enabling improved surface quality across all samples, promoting sustainability. Surface roughness (Ra) is notably influenced by feed rate and rice bran oil, contributing 58.06% and 49.5%, respectively. This research underscores the potential of optimizing cutting fluid and process parameters to enhance sustainability in manufacturing.

A review of the use of cryogenic coolant during machining titanium alloys

Titanium alloys are considered difficult-to-machine materials, and much effort has been made to improve their machinability through various approaches. The use of cryogenic coolants during machining, sometimes referred to as cryogenic machining, is a popular environmentally friendly technique that promises to reduce cutting zone temperatures, reduce friction, and enhance tool life during machining operations. This review article explores the use of cryogenic coolants during titanium machining and presents an overview of research progress and knowledge gaps. The various coolants and methods for cryogenic machining are reviewed, along with the effectiveness of these methods in terms of key machinability criteria, including tool wear, tribological properties, and heat transfer. Issues in sustainability are also explored.

Investigations on tool wear behavior in turning AISI 304 stainless steel: An empirical and neural network modeling approach

Machining with a cutting edge with extensive damage or a fractured cutting edge significantly influences the machining performance. Therefore, investigations on tool wear behavior, their forms, and wear mechanisms will be very helpful in the current environment of sustainable manufacturing. On the other hand, the machining economy is negatively impacted by replacing the tool well before its useful life. This proactive maintenance planning reduces the risk of sudden tool failure and potential workpiece damage. Accordingly, the current work creates empirical and ANN models to predict flank wear growth for turning AISI 304 stainless steel using a MTCVD-TiCN/Al2O3 coated carbide tool. The experiments were designed to cover a broad range of operating conditions to ensure the model's accuracy and applicability in practical machining scenarios. An ANN was modeled using a feedforward backpropagation machine learning technique. In this study, a higher prediction accuracy of 0.9975 was achieved with ANN model as compared to the empirical model. The most common wear mechanism observed is metal adhesion, followed by fracture due to the pulling away of adhered material. The developed models have been found to be valuable for optimizing cutting parameters and enhancing tool life in machining.

Hybrid adaptive control of CNC drilling for enhancement of tool life and surface quality

Intelligent machining requires the online adaptation of the machining parameters to improve tool life and product quality and to reduce machining costs. This article presents a novel hybrid adaptive control (HAC) system for a drilling process. The HAC system is a combination of two adaptive controls: geometric adaptive control (GAC) and adaptive control by optimisation (ACO). It keeps the roughness of the holes within tolerance without compromising tool life. A response surface model (RSM) is used for modelling the drill wear and surface roughness with speed, feed, acceleration and force signals as inputs. The model predicts the wear and roughness with prediction accuracies of 97.1% and 93.6%, respectively. The roughness control is achieved through a Massachusetts Institute of Technology rule and tool wear is minimised by genetic algorithm optimisation. The adaptive algorithms are simulated and validated for the machining conditions given by the adaptive algorithms. The results show an improved tool life of 7% and surface roughness of 11%.

Tool Surface Degradation and Adhesion in Aluminium Forging

Tool surface deterioration is a crucial effect limiting the efficiency of forging processes. Prior work on wear is limited to steel forging while investigations on tribology in aluminium forging are limited to an abstract or simplified level. The presented work aims at contributing to filling this gap with a holistic analytic approach including ball-on-disc (BoD) tests, ring compression (RC) tests and serial forging (SF) tests close to industrial applications in order to gain insights on effects and influences of surface deterioration and evaluate the suitability of different tool coatings. The results show that the primary influence on surface adhesion in aluminium forging is the superposition of surface enlargement and contact pressure of the material flow. Duplex-treated tools with the combination of a nitriding in the first step and an additional coating with chromium nitride (CrN) afterwards show a significant reduction of aluminium adhesion in ring compression and serial forging tests, representing a promising approach to tool life enhancement in forging applications.

A review on minimum quantity lubrication (MQL) assisted machining processes using mono and hybrid nanofluids

PurposeBecause many cutting fluids contain hazardous chemical constituents, industries and researchers are looking for alternative methods to reduce the consumption of cutting fluids in machining operations due to growing awareness of ecological and health issues, government strict environmental regulations and economic pressures. Therefore, the purpose of this study is to raise awareness of the minimum quantity lubrication (MQL) technique as a potential substitute for environmental restricted wet (flooded) machining situations.Design/methodology/approachThe methodology adopted for conducting a review in this study includes four sections: establishment of MQL technique and review of MQL machining performance comparison with dry and wet (flooded) environments; analysis of the past literature to examine MQL turning performance under mono nanofluids (M-NF); MQL turning performance evaluation under hybrid nanofluids (H-NF); and MQL milling, drilling and grinding performance assessment under M-NF and H-NF.FindingsFrom the extensive review, it has been found that MQL results in lower cutting zone temperature, reduction in cutting forces, enhanced tool life and better machined surface quality compared to dry and wet cutting conditions. Also, MQL under H-NF discloses notably improved tribo-performance due to the synergistic effect caused by the physical encapsulation of spherical nanoparticles between the nanosheets of lamellar structured nanoparticles when compared with M-NF. The findings of this study recommend that MQL with nanofluids can replace dry and flood lubrication conditions for superior machining performance.Practical implicationsMachining under the MQL regime provides a dry, clean, healthy and pollution-free working area, thereby resulting the machining of materials green and environmentally friendly.Originality/valueThis paper describes the suitability of MQL for different machining operations using M-NF and H-NF.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0131/

Effects of process parameters on cutting forces, material removal rate, and specific energy in trochoidal milling

Trochoidal milling has been developed to enhance tool life during slot machining. It is characterized by reduced cutting forces, cutting temperature, and tool wear as compared to conventional milling processes. It is effective in machining difficult-to-cut materials, high-speed machining, and groove corners. However, this process has not been deeply investigated enough to discover its advantages and optimize its parameters. A full factorial design of 144 experiments has been applied in this paper to investigate extensively the effects of axial depth of cut, feed rate, and trochoidal step on material removal rate, cutting forces, and specific energy in trochoidal milling. Trochoidal step and axial depth of cut almost have the same contributions on cutting forces by 32% and 31% respectively, followed by feed rate by 25%. Feed rate, trochoidal step, and axial depth of cut influence the material removal rate by 37%, 30%, and by 19% respectively. The contributions of feed rate, trochoidal step, and axial depth of cut on relative specific energy are 57%, 24%, and 8% respectively. The increase of axial depth of cut increases the maximum resultant force till a threshold value, then it stabilizes. This behavior occurred due to the increase of the maximum engagement angle to a certain limit, then it does not increase any more. Both feed rate and trochoidal step linear affect maximum resultant force and material removal rate, while the relationships are non-linear for specific energy. It is recommended to machine slots in full depth with the highest possible trochoidal step and feed rate, considering the increase of tool wear and surface roughness. It is preferred to use a cutting tool of a large helix angle and small diameter to reduce the threshold axial depth of cut. Overall, this study is significant in characterizing, designing, and optimizing of trochoidal milling through experimental work.

Magnetohydrodynamic-based Internal Cooling System for a Ceramic Cutting Tool: Concept Design, Numerical Study, and Experimental Validation

The effective removal of the heat generated during mechanical cutting processes is crucial to enhancing tool life and producing workpieces with superior surface finish. The internal cooling systems used in cutting inserts employ a liquid water-based solvent as the primary medium to transport the excess thermal energy generated during the cutting process. The limitations of this approach are the low thermal conductivity of water and the need for a mechanical input to circulate the coolant around the inner chamber of the cutting tool. In this context, this paper proposes an alternative method in which liquid gallium is used as the coolant in combination with a magnetohydrodynamic (MHD) pump, which avoids the need for an external power source. Using computational fluid dynamics, we created a numerical model of an internal cooling system and then solved it under conditions in which a magnetic field was applied to the liquid metal. This was followed by a simulation study performed to evaluate the effectiveness of liquid gallium over liquid water. The results of experiments conducted under non-cooling and liquid gallium cooling conditions were analyzed and compared in terms of the tool wear rate. The results showed that after six machining cycles at a cutting speed Vc = 250 m min −1, the corner wear VBc rate was 75 µm with the coolant off and 48 µm with the MHD-based coolant on, representing a decrease of 36% in tool wear. At Vc = 900 m min−1, the corner wear VBc rate was 75 µm with the coolant off and 246 µm with the MHD-based coolant on, representing a decrease of 31% in tool wear. When external cooling using liquid water was added, the results showed at Vc = 250 m min−1, the difference between the tool wear rate reduction with the internal liquid gallium coolant relative to the external coolant was 29%. When the cutting speed was increased to Vc = 900 m min−1, the difference observed between the internal liquid gallium coolant relative to the external coolant was 16%. The study proves the feasibility of using liquid gallium as a coolant to effectively remove thermal energy through internally fabricated cooling channels in cutting inserts.

Eco-friendly sustainable machining with MoS2-based solid lubricant

Machining is one of the basic and inevitable operations in the manufacturing industry. The machining performance is estimated based on various parameters like cutting forces, surface roughness, tool–chip interface temperature, and specific energy. In the traditional approach, cutting fluids are greatly utilized to dissipate the generated heat during machining, but their utilization causes a threat to nature and human being’s health. Therefore, there emerges a necessity to determine user-friendly, sustainable, and eco-friendly substitutes for traditional cutting fluids. The field of advanced tribology has proposed the usage of solid lubricants due to their inherent properties, such as excellent tribological performance, reduced machining zone temperature due to lowered friction, and enhanced tool life. Therefore, in the present study, pure MoS2 and composite MoS2–TiO2 solid lubricants have been employed in the milling process of AISI 52100 steel with HSS end mill cutter. The machining was carried out in different conditions such as uncoated tool without use of lubricant, uncoated tool with use of lubricant, coated tool without use of lubricant, and coated tool with use of lubricant that were employed to analyze their effect on machining performance. The experimental results showed substantial improvement regarding reduced cutting forces, reduced temperature at tool–chip interface, improved surface finish, and average tool wear with the application of solid lubricants. Among the various lubricating conditions, composite MoS2–TiO2-coated tool with composite MoS2–TiO2 lubricant exhibits excellent machining performance.

Performance and analysis of sputtered CBN inserts in machining

Sputtered inserts used in facing, turning, sanding, cutting, knurling and drilling operation can increase the quality of the product. It is now possible to create nanofilms of incredible capabilitites which tends to remarkable advancements made in thin film coating processes.Wear resistant type coatings are done to provide a cost effective measure to minimize the ability of two mate jobs which gradually wear away by abrasion orelse of friction. In this case of enhancing tool life, Cubic Boron Nitride(CBN) is chosen to obtain good surface finish and increased productivity. With this sputtering coating technique, Cubic Boron Nitride(CBN) insert is selected for thin film of coating with Tungsten Carbide. The main aim of this technique is to increase the machinability and surface qualities of cutting tools to extend their life span. A small chipping in tool may entirely damage the quality of product and also increase the production cost. So, Life span of tool plays a important role in all machining operations. The Cubic boron nitride is characterized by its roughness, chemical structure composition and grain distribution.Further Microstructure characterization studies such as SEM, TEM and XRD are to be carried out in future studies.

Cutting performance of the nanotwinned cBN tool in nano-cutting of Ni-Cr-Fe alloy

As nickel-based superalloys are broadly used and difficult to machine material, it is challenging to achieve high machining efficiency while improving surface quality and enhancing tool life. Aiming at the cutting of Inconel-718 type nickel-based superalloys, a new type of tool in which the cBN tool incorporated naontwinned structures is proposed. The mechanical properties of cBN tool materials with the introduction of nanotwinned structures are studied by uniaxial tensile simulations of nanotwinned cBN (nt-cBN) and single-crystal cBN (sc-cBN) nanosheets. Compared with the sc-cBN materials, the introduction of nanotwinned structures improved the properties of strength, toughness, and stiffness of cBN materials. The tool performance of the nt-cBN tool and the sc-cBN tool have been studied when cutting a simplified nickel-based superalloys (Inconel-718) model using the molecular dynamics method. Tool wear mechanisms of nt-cBN tools are investigated by calculating and visualizing tool temperature, Von Mises stress, and shear stress distributions. The results show that the wear resistance performance of the nt-cBN tool has improved during high-speed cutting, with less plastic deformation of the tool, less diffusion wear, less adhesion wear, and especially the wear of the flank surface is significantly reduced. As a new type of tool for machining difficult-to-machine materials, nt-cBN tools have promising research prospects.

Sustainable machining of additive manufactured SS-316L underpinning low carbon manufacturing goal

Among recent developments in manufacturing industries, the laser-powder bed fusion process (L-PBF) is gaining attention for manufacturing complex and functional parts through the selective melting of powders. However, post-processing is required to improve further the quality of L-PBF parts which corresponds to machinability challenges. In this study, stainless steel grade SS 316L manufactured through L-PBF is subjected to high-speed turning to explore the influence of process variables. The cutting speed, CS (125 m/min, 175 , and 225 m/min), depth of cut, DOC (0.45 and 0.90 mm), and feed rate, FR (0.225 and 0.337 mm/rev) are used for process analysis. Machining performance (surface roughness and tool life) and sustainability aspects (energy consumption, carbon emissions, economics) are taken as response metrics. Parametric optimization to achieve desired response characteristics is carried out. The optimized parametric levels achieved an 87.66% goal of lowering machining cost, 80.94% goal of minimizing carbon emissions, 99.25% goal of decreasing specific energy, 100% goal of enhancing tool life, and 98.95% goal of reducing surface roughness. The present research can be used as a basis for comparative analysis of sustainable machining routes (lubrication-based or production tooling) and as a fundamental guideline for machinists in the metal processing industry.