Improve Machining Performance Research Articles

Nonlinear system optimization of cutting tools with dynamic vibration absorbers in deep hole boring: A stability analysis

Deep hole boring vibrations pose challenges due to low stiffness of long cantilever bars, impacting accuracy and standards. Nonlinear modeling provides deeper insights into chatter. Integrating dynamic vibration absorbers enhances boring bar stability. In this study, a nonlinear model is developed to investigate the vibration stability of the boring process, with the model's dimensionless parameters analyzed and solved using the Runge-Kutta method. The impact of nonlinear parameters associated with the damping and stiffness of the absorbers on the vibration characteristics of boring bars is thoroughly examined. The findings reveal that there exists an optimal range for the damping and stiffness of the absorbers, where vibration amplitude is significantly reduced, especially under conditions of high excitation force. Moreover, it is demonstrated that the introduction of cubic stiffness elements can further diminish vibration amplitude. These results offer valuable guidance for the design and optimization of DVA in deep hole boring, contributing to improved machining performance and accuracy.

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.

Nanofluid Minimum Quantity Lubrication (NMQL): Overview of Nanoparticle Toxicity and Safer-Design Guidelines

Minimum Quantity Lubrication (MQL) has received much attention from the research community as a potential lubricating system to reduce environmental hazards and health issues that can be commonly found in flood cooling/lubricating systems based on metalworking fluids. The addition of nanoparticles in MQL systems (NMQL) has led to improved machining performance, increasing the cooling capability and reducing friction and tool wear, and some researchers have proved the applicability of this type of system for difficult-to-cut materials. However, the mist generated by MQL systems due to both the MQL system itself and the machining operation may pose an additional hazard to operators which is being overlooked by the research community. These hazards become more severe when using nanoparticles, but unfortunately very few works have paid attention to nanoparticle toxicity as applied in MQL systems, and this issue should be clearly understood before encouraging its implementation in industry. Furthermore, current legislation does not help since regulation of permissible exposure limits when dealing with nanoparticles is still ongoing in most cases. In this work, the toxicity of nanoparticles applied in MQL systems is analyzed, and recent research on studies of nanoparticle toxicity both in vitro and in vivo is presented. A relative comparison of toxicity is provided for those nanoparticles that have been reported in the literature as potential additives for MQL. The review is focused on analyzing the main factors of toxicity of nanoparticles which are identified as size, shape, surface properties, agglomeration and solubility. This review presents guidelines for safer nanolubricant formulations, guiding practitioners towards proper NMQL implementations in industry. Furthermore, current occupational exposure limits and recommendations are provided for all the nanoparticles potentially used in MQL systems, which is of interest in terms of work safety.

Rapid manufacturing of copper-graphene composite EDM electrode for improved machining performance

ABSTRACT Recently, graphene has emerged as reinforcing material for copper-based electric discharge machine (EDM) tools because of its superior electrical, thermal and high melting properties. 3D printing methods to fabricate electrodes are convenient and faster compared to conventional methods. Therefore, this study investigated the fabrication of copper-graphene composite electrodes for EDM using rapid tooling and pressureless microwave sintering. The influence of graphene content on the machining performance was examined, revealing enhanced electrical conductivity and machining efficiency. Electrical conductivity of the composite electrodes having 0.25 and 0.5 wt% of graphene was higher than pure copper. Additionally, upto 89% improvement in material removal rate and upto 14% reduction in electrode wear rate was obtained. Optical study showed an improvement of upto 28% in surface finish of the workpiece machined using composite electrode having 0.25 wt% graphene. A complex shaped composite tool was successfully fabricated and machined cavity was obtained.

Monitoring chip color change in Ti6Al4V alloy milling and investigating the effects of cooling/lubrication on chip morphology

This study investigated how image processing technology and cooling/lubrication methods affect the change in chip color and chip shape when milling Ti6Al4V alloy. The focus was on solving the high temperature problem in the machining of titanium alloys, which are hard and have limited thermal conductivity. The experiment was carried out under the same standards as dry, coolant, MQL (minimum amount of lubrication), and cryogen. Experiment results showed that cooling and lubricating methods had an influence on chip morphology, with the chips morphing into tubular long spiral chips, conical short spirals, and short tubular chips. Dry machining produced distinctively adiabatic bands and saw-type chip formations. These structures were found to be at a lower level in the cryogenic cooling process than in other methods. Methods of cooling and lubrication were found to be effective on chip thickness, width, and radius. In addition, the color difference caused by burning in the chip depending on the machining temperature was investigated. The S (saturation ratio) of colors in the chip image processing rose in the order of cryogen, coolant, MQL, and dry machining (0.131623, 0.150836, 0.208414, 0.230374) technique. When the temperature change was evaluated according to the cooling and lubrication techniques, the lowest temperature was found in cryogenic processing; the temperature value rose by 25 % in fluid, 35 % in MQL, and 55 % in dry machining. As a result, the intention of this study is to improve machining performance by monitoring chip structures.

Analisis Kinerja Mesin Sterilizer (Rebusan) Menggunakan Metode Overall Equipment Effectiveness (OEE) di PTPN V Sei Buatan

Sterilizer (boiling) machines are important in palm oil production in the palm oil processing industry. This study analyzes the performance at PTPN V Sei Buatan using the Overall Equipment Effectiveness (OEE) method. Observation and production data were used to calculate OEE, showing that the machine experienced significant downtime and low performance efficiency and product quality. Improvement recommendations, including routine maintenance, better maintenance scheduling, and operator training, are proposed to improve machine performance. The OEE method helps identify areas of improvement to increase operational efficiency and productivity at PTPN V Sei Buatan.

Determination of Ergonomic Factors of the Backpack Blowers Used in the Windrow of Hazelnuts

Ergonomics plays an important role in the design and use of agricultural machinery to protect the health and safety of workers. Lack of ergonomic design negatively affects worker discomfort and safety. Therefore, ergonomic design should ensure that workers can work comfortably, maintain correct posture, and minimize the risk of injury. Especially workers using backpack machines are exposed to physical fatigue, posture problems, risk of injury, and ergonomic effects due to vibration and noise. Considering all these, this study was conducted to determine the ergonomic factors (ergonomic analysis of the operator's working posture, operator's fatigue values, noise level, and dust concentration) that the operator is exposed to as a result of different body positions and moving the air diverter hose while using the backpack type blowers used to windrow the kernel+husked nuts falling on the orchard ground during the hazelnut harvest period. According to this, in all three ergonomic risk methods, the operator's body posture was found to be risky, and ergonomic adjustments were needed. Again, the operator's fatigue value, noise level, and dust concentration were obtained as 10.43 kcal min-1, 91.70 dB(A)-106.20 dB(A) (average 98.95 dB(A)), 19.241-21.390 mg m-3-air ( average 20.315 mg m-3-air), respectively. The identification of ergonomic factors is also very important to improve the user experience, prevent occupational accidents, and improve machine performance in general. With the identification of these factors, optimized solutions for the safety and comfort of users will be put forward by adopting a human-centered approach in machine design.

Aerodynamic tunnel for tests of turbine annular cascades

Aerodynamic test rigs are necessary for experimental testing of turbomachines and investigation of possible ways to improve machine performance. Existing installations give higher losses and do not work efficiently at off-design operating modes. An operating part with an adjustable radial diffuser was designed in order to determine the characteristics of turbine annular cascades. Experimental studies and computational verifications showed satisfactory results at various operating modes. The regulated backwall of the radial diffuser ensured supersonic velocity values behind the cascade and overall stable operation in a wide range of Mach numbers up to 1.3 when using compressors of comparatively small capacity. The optimum positions of the regulated backwall were determined, which provided a deep vacuum behind the cascade, as well as 1.5 times Mach number increase compared to the turbine cascade without a diffuser. Changing the inlet channel geometry at supersonic modes leads to an increase in the diffuser efficiency. Additionally, it was determined that the use of the turbine vane cascade in the test rig flow path is not necessary during calculation studies, but instead an axisymmetric vaneless converging area can be applied and give satisfactory results as well as reduce the time spent on calculations. The computational model can be used to optimize the design of an aerodynamic tunnel outlet area.

On dry machining of AZ31B magnesium alloy using textured cutting tool inserts

Magnesium alloys have many advantages as lightweight materials for engineering applications, especially in the fields of automotive and aerospace. They undergo extensive cutting or machining while making products out of them. Dry cutting, a sustainable machining method, causes more friction and adhesion at the tool-chip interface. One of the promising solutions to this problem is cutting tool surface texturing, which can reduce tool wear and friction in dry cutting and improve machining performance. This paper aims to investigate the impact of dimple textures (made on the flank face of cutting inserts) on tool wear and chip morphology in the dry machining of AZ31B magnesium alloy. The results show that the cutting speed was the most significant factor affecting tool flank wear, followed by feed rate and cutting depth. The tool wear mechanism was examined using scanning electron microscope (SEM) images and energy dispersive X-ray spectroscopy (EDS) analysis reports, which showed that at low cutting speed, the main wear mechanism was abrasion, while at high speed, it was adhesion. The chips are discontinuous at low cutting speeds, while continuous at high cutting speeds. The dimple textured flank face cutting tools facilitate the dry machining of AZ31B magnesium alloy and contribute to ecological benefits.

Integrating Machine Availability and Preventive Maintenance to Improve Productive Efficiency in a Manufacturing Industry

One of the major problems existing in manufacturing industries is low productive efficiency and high frequencies of machine breakdown or downtime. However, equipment maintenance is momentous for improving productive efficiency, methods of integrating preventive maintenance (PM) and Machine availability into improving productive efficiency in manufacturing industries has attracted considerable attention. This work showcased a strategic process improvement plan that can be used to improve production process with low productive efficiency. Lean Six Sigma (LSS) is used as the method for implementation of a successful process improvement. A case study was used to show how a successful implementation of the Lean Six Sigma Define, Measure, Analyze, Improve and Control (DMAIC) approach was implemented, how statistical analysis can be used to identify defects in products, and they were significantly improved. The Study is performed by conducting a qualitative and quantitative analysis using structured questionnaires, surveys, journals with the case a study that boasts of about 600 employees. Investigations showed that an average of 68% contributing to 79% productive efficiency apparently. However, the attempts of the decreasing downtime events and improving efficiency were based on scheduled maintenance checklist plan that is supported by the overall equipment effectiveness (OEE) benchmark as an indicator for affirming improvements. The linear regression model highlighted significant relationship between machine availability and productive efficiency. Proposed solutions using statistical analysis focused on sustainability, introducing a measurement model based on DMAIC criteria that demonstrated a significant improvement of 20% in machine availability, 44.3% in quality, 29.9% in productive efficiency and 57.4% OEE of the targeted case study. The research affirmed Lean Six Sigma (LSS) as a sustainable solution for reducing defects for quality issues, and maximizing efficiency in manufacturing industries, hence, emphasizing on resource optimization and continuous improvement in various study variables. Findings shows that Lean Six Sigma (LSS) as a methodology, is a catalyst for positive change, hence, improving machine performance, and customer satisfaction.

Investigation and formulation of cobalt content of ultra-thin diamond blades and dicing performance manufactured by fused deposition modeling and sintering (FDMS)

Achieving appropriate cobalt concentration within the matrix when fabricating ultra-thin diamond blades with fused deposition modeling and sintering (FDMS) leads to improved machining performance. This study investigates the effects of cobalt content on mechanical properties such as hardness, relative density, and holding strength of ultra-thin diamond blades by FDMS. It also examines the fragmentation characteristics, chipping size, and surface quality of diced surfaces with these ultra-thin diamond blades. The experimental findings show that increasing the cobalt content of the matrix enhances its mechanical properties and reinforces the bond between the matrix and diamond particles, thereby improving the flatness, sharpness, and overall quality of the blades. Additionally, it has been observed that ultra-thin diamond blades containing less cobalt are more effective at processing ferrite magnets, whereas those containing more cobalt perform better at machining alumina ceramics. The cobalt content of ultra-thin diamond blades has an additional impact on the surface quality and surface topology of diced surfaces in various materials. This results in microstructured surfaces with the lowest surface roughness values of 0.282 μm and 0.367 μm for alumina ceramics and ferrite magnets, respectively. In conclusion, understanding the effects of cobalt content within the matrix of diamond thin blades could assist in the availability of good formulas in FDMS, improving blade quality and dicing performance, and ultimately advancing FDMS's capability to produce high-quality diamond cutting tools for a wide range of applications.

Method for controlling the area of the electrolyte outlets in the flow field simulation and experimental verification of electrochemical machining

Electrochemical machining (ECM) is regarded as a promising and cost-effective manufacturing method for difficult-to-cut materials with complex shapes and structures. Many methods have been successfully developed for the flow field state of machining gaps, which is a key factor affecting machining performance in ECM engineering practice. However, little attention has been given to the area of the electrolyte outlets. This study attempts to present a method for controlling the area of the electrolyte outlets in the flow field simulation and experimental verification of electrochemical machining. The simulation results indicate that the convergent outlet mode can effectively enhance the turbulent effect of the fluid in the inter-electrode gap, especially in the near-wall region, and improve the mass transfer effect of electrolyte. Meanwhile, the simulation results also showed that better material removal rate (MRR) and surface roughness can be obtained when the convergent degree is about 50–70% combined with the effects of bubble volume compression and flow velocity reduction. The experimental results confirmed that the convergent outlet mode can obtain better MRR and surface roughness. Energy-dispersive X-ray spectroscopy results of machined specimens showed that oxides with strong adhesion on the specimen’s surface were effectively eliminated due to stronger turbulence effect under convergent outlet conditions. This should be considered as the main reason for improving machining performance by changing the electrolyte outlet area. These conclusions can provide some useful assistance for ECM engineering practice.

As how artificial intelligence is revolutionizing endoscopy.

With incessant advances in information technology and its implications in all domains of our lives, artificial intelligence (AI) has emerged as a requirement for improved machine performance. This brings forth the query of how this can benefit endoscopists and improve both diagnostic and therapeutic endoscopy in each part of the gastrointestinal tract. Additionally, it also raises the question of the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. There are two main categories of AI systems: computer-assisted detection (CADe) for lesion detection and computer-assisted diagnosis (CADx) for optical biopsy and lesion characterization. Quality assurance is the next step in the complete monitoring of high-quality colonoscopies. In all cases, computer-aided endoscopy is used, as the overall results rely on the physician. Video capsule endoscopy is a unique example in which a computer operates a device, stores multiple images, and performs an accurate diagnosis. While there are many expectations, we need to standardize and assess various software packages. It is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice. In summary, AI represents a breakthrough in digestive endoscopy. Screening for gastric and colonic cancer detection should be improved, particularly outside expert centers. Prospective and multicenter trials are mandatory before introducing new software into clinical practice.

An evaluation of cutting head design of current tunnel Boring machines and suggestions to improve machine Performance

ABSTRACT This paper evaluates cutterhead design of current tunnel boring machines (TBMs) in considerations with the actual cutting action of discs observed on the most common cutter heads. The discs on the current TBM cutter heads were found to predominantly perform asymmetrical cutting action, which generates excessive sideway forces impairing steering action of the machine. The effective s/p ratios related to actual cutting action of discs emerge to be significantly higher than those of the laboratory-determined values, and this consequently leads to adverse cyclic deepening effect. The gross lateral loads arising from excessive side forces were stated to be minimised if the adjacent discs are arranged to cut in symmetrical position by adopting appropriate cutterhead design methods suggested in this paper. Furthermore, a dynamic analysis was carried out in terms of reaction forces on cutter heads of different geometries, and it was indicated that flat face cutter heads have better steering abilities than conical or domed types, in mixed face conditions. Similar analysis on disc angular spacing also inferred that scattered type disc arrangements with equal angular spacing are preferable to cutterheads with spoke or star type lacing, in terms of cutterhead balance.

Development and Implementation of a Damage Model for Potato Tuber Blackspot in Discrete Element Method to Analyze Harvesting and Handling Processes

Highlights Method for determining the discoloration of potato tissue samples induced by mechanical stress. Model of the discoloration susceptibility of specific potato tissue. Particle segmentation algorithm in the Discrete Element Method for contact localization in post-processing. Development of the "Discrete Element Method Blackspot Potato Tuber Model" capable of predicting internal blackspot damage in potato tubers. Abstract. Blackspot damage caused by mechanical load during the harvesting and processing stages of potato tubers remains difficult to mitigate. Field tests are inadequate as it takes around two days to develop blackspot discoloration, making it unclear when and where the initial damage occurs. Particle simulation, particularly with the Discrete Element Method (DEM), offers opportunities to improve machine performance and reduce crop losses. However, the DEM cannot predict local blackspot damage, making it unsuitable for reducing potato tuber damage. To address this, the “DEM blackspot potato tuber model” presented in this contribution integrates a discoloration function and a segmentation algorithm of DEM particles to predict and analyze blackspot damage during harvesting and processing. Based on mechanical experiments on tuber tissues with different loading conditions, a discoloration function was proposed dependent on strain and strain rate. The segmentation algorithm adapts to the varying potato tuber particle size by varying the number of segments to generate segments of average blackspot size. Two different validation tests are used to identify the single and multiple impact capabilities of the model. The preliminary results show that the discoloration induced by single impact events is well predicted in terms of the number of damaged tubers as well as the average damage per tuber. Damage of multiple impacts is less accurately predicted since the model does not take into account the accumulation of damage. Keywords: Blackspot, Damage model, Discrete Element Method, Potato handling, Simulation.

Influence of singular and dual MQL nozzles on sustainable milling of Al6061-T651 in different machining environments

Al6061-T651 alloy is widely used in the aerospace industry due to its superior properties and is a very difficult-to-cut material with high quality because of tool wear and thermal softening. This situation causes low surface quality, high cutting force, and limited tool life, as well as an increase in total consumed energy, total carbon dioxide emission, and total machining cost. Therefore, it is necessary to sustainably increase this aerospace alloy's machining performance. In the presented study, for the first time, milling performance and sustainability assessment of Al6061-T651 alloy under dry, minimum quantity lubrication (MQL), nano molybdenum disulfide (MoS2) reinforced nanofluid-assisted MQL (N-MQL) cutting conditions were investigated using the single nozzle and dual nozzles, two different MQL flow rates, and three different cutting speeds. In this context, the results of surface roughness, cutting force, feed force, cutting tool flank wear, total consumed energy, total carbon emission, and total machining cost were obtained. As the cutting speed increased, the cutting force, feed force, total consumed energy, total machining cost, and total carbon emission decreased by 12.9 %, 18 %, 7.8 %, 6 %, and 0.1 %, while the surface roughness increased by 34.9 %. In the MQL and MoS2 N-MQL cutting conditions at the same flow rate, using dual nozzles instead of a single nozzle improved machining response and sustainability indicators. In addition, increasing the flow rate improved machining performance and sustainability indicators. Using the double nozzle MoS2 N-MQL method reduced the surface roughness, cutting force, feed force, flank wear, total consumed energy, total carbon emission, and overall machining cost by 74.2 %, 63.9 %, 68.4 %, 66 %, 27.9 %, 0.4 %, and 6.39 %, respectively.

Orthopedic patient analysis using machine learning techniques

Orthopedic patients have been increasing in hospital because of road traffic accidents, advanced age, a lack of exercise, inadequate nutrition, and other factors. The suggested article uses Machine Learning (ML) techniques to examine the patient reports. The ability to mimic the human actions is called ML. It is a subclass of AI that solves a number of healthcare-related issues. Here ML algorithms are used for health-related data. It solves a number of healthcare-related issues. ML is the process of a machine imitating intelligent human activities. It belongs to the Artificial Intelligence (AI) subclass. ML algorithms are used for medical data such as Logistic Regression, Support vector machine, K-Nearest Neighbor, Random Forest, Decision Tree, Artificial Neural Network to predict orthopedic illnesses such as Normal, Hernia and Spondylolisthesis orthopedic. ML techniques have increased the speed and accuracy for diagnosis. The most serious and urgent cases require rapid care. It improves patient care by lowering human error and stress on medical staff. Our primary objective is to improve machine performance and decrease incorrect categorization.

Procedures and performance of a robotic machining system with metrology-in-the-loop feedback control

In recent years, the use of industrial robots in advanced manufacturing applications has become increasingly popular. With larger workspaces and lower costs relative to more traditional machine tools, the use of robots has significant upsides in machining applications. However, due to their relatively low accuracy, the use of industrial robots in these applications is limited. One way to improve robot accuracy is to provide feedback control via external sensors such as laser trackers. This paper presents several techniques used to identify key parameters for a laser tracker controlled robotic machining system. Specifically, transformations between the coordinate frames within the system are crucial to its performance since error in these parameters are generally not observable by the feedback system. The identification methods are used to perform full-scale machining experiments. These experiments demonstrate the improved machining performance of the laser tracker controlled robotic machining system when compared to the machining performance without feedback control.

Application of Overall Equipment Effectiveness and Failure Mode Efffect and Criticality Analysis Methods to Improve Machine Performance Effectiveness

PT XYZ is a company that produces hot rolled coils (HRC) steel products. One of the machines used in the process is a rough mill machine in the milling process. The problem is downtime on the rough mill machine that disrupts the production process time. The purpose of this study is to determine the value of the effectiveness of the machine, find the largest losses that affect the value of the effectiveness of the machine, identify the cause of the biggest losses and provide recommendations for improvement steps. The methods used are overall equipment effectiveness (OEE), six big losses, and the failure mode effect and criticality analysis (FMECA). The results show that the average OEE value of PT XYZ is 80%, but this value was still not following the ideal standard. By identifying the six big losses, the most significant losses are reduced speed losses, with a total percentage value of 86% and an average time loss of 5,020 minutes. FMECA results obtain three components that cause the biggest losses: the engine motor, gearbox, and spindle. Recommendations for improvement include making and setting schedules, implementing predictive maintenance and preventive maintenance on rough mill machines, and regularly checking and replacing component spare parts.

Experimental investigation on the machining performance of AISI D2 tool steel during ultrasonic-assisted turning under dry and MQL conditions

Ultrasonic-assisted turning (UAT) is a hybrid machining method that improves difficult-to-cut materials’ machinability and surface integrity. Minimum quantity lubrication (MQL) is a cooling condition employed in metal cutting operations to improve machining performance and environmental sustainability. This article presents the experimental investigations on the effect of machining and vibration parameters and cooling conditions on the surface roughness (Ra), flank wear (Vb), and surface hardness (HV) during UAT of AISI D2 tool steel. Experiments were designed based on L36 orthogonal array. Two levels of spindle speeds, feed rates, depth of cut, and three levels of percentage intensity of ultrasonic power (PIUP) were employed, and two cooling conditions, like dry and MQL. Analysis of variance (ANOVA) was used to find the significant input parameters that affect each response. ANOVA output revealed that feed rate was the most influential factor on Ra, the PUIP is the dominant factor on HV, and spindle speed is the significant factor on Vb. Optimisation is performed to find optimal parameters as per Taguchi multi-response method. The optimal parameters are 450 rpm spindle speed, 0.05 mm/rev feed rate, 0.1 mm depth of cut, 100 PIUP and MQL cooling resulted in minimum surface roughness of 0.439 μm, maximum hardness of 283 HV and minimum flank wear of 168 μm. Flank wear morphology results showed that abrasion and chipping are the tool wear mechanism in dry UAT, whereas minor abrasion alone was found in MQL UAT compared to CT. Chip morphology study showed that UAT produced continuous spiral-type chips under both cooling conditions compared to CT. A novel design approach is implemented to design the UAT tool. Along with the UAT tool, MQL is also given to improve the machinability of D2 tool steel. This UAT tool can machine intricate metals with a critical cutting velocity of less than 75.4 m/min.