Depth Of Cut Research Articles

Improvement of Machinability of Hardened Steel in Turning Operation Using Cryogenically Treated Cutting Tool

Hardened steels need special cutting tools like PCBN and ceramic to be machined. But these cutting tools aren’t cost-effective and require machine tool structures that are stiff and don’t let vibrations through. The current trend is to find other, less expensive ways to make these materials easier to work with. Cryo-treating cutting tools is an effective way to improve the way the tool materials work when they are cut. Cryogenically treated tungsten carbide inserts in dry turning operations were looked at in this study. For hard turning of hardened mild steel (48 HRC), the performance of cryogenically treated Tungsten Carbide (WC) inserts was compared with that of untreated inserts in terms of chip-tool interface temperature and surface roughness under dry cutting conditions. The cutting tool (Untreated and Treated), and cutting speed (375, 512, 706 rpm) were selected as experiment parameters at a constant feed rate of 0.0841mm/rev and depth of cut 1mm. The chip-tool interface temperature analysis results revealed that temperature increases with the increasing cutting velocities. A better surface finish can be found at a higher cutting speed. The lower value of Ra was found at 1.75 μm (without cryogenic treatment) and 1.05 μm (with cryogenic treatment) for cutting speed at 706 rpm.

Surface Roughness Analysis and Optimization for ST 37 Steel in Lathe Operation using Design of Experiments Method

The machining process is one of the processes used to process steel materials. The value or level of material roughness is one of the values that is considered in the machining process, according to the desired roughness value. One example is in the turning process, spindle speed, depth of feed, and feed motion are factors that can affect the level of roughness of a material. The level of roughness that is not according to demand is one of the problems in the manufacturing industry, especially in the results of the turning process. One of the causes of the discrepancy in the roughness value is because the parameters used are not suitable. Therefore, this study aims to determine the optimal parameters in the turning process to obtain the optimal roughness value, and to determine the parameters that most influence the roughness of the turning process. In this study, ST 37 steel was used. This study used an experimental design method with Orthogonal Array L8. The experiment was carried out with 5 control factors and 3 noise factors. Each factor has 2 levels and is repeated 8 times. In this study using a conventional lathe and for roughness testing using a Surface Roughness Tester. Based on the results of data analysis, the movement factor has the highest influence on the surface roughness value. The ranking factors that affect the roughness value are as follows: spindle speed of 345 rpm, feed motion of 0.051 mm/rad, depth of cut 1 mm, dry cooling, and automatic sledding. The Design of Experiments (Taguchi) method use quality characteristics to determine the results of the analysis. In this study, the quality characteristic is smaller the better. Smaller the better characteristics is where the smaller value, then better the quality. In variations of the spindle rotation speed and feed motion, we get where if the spindle speed is high and the feed motion is low it will produce a low level of roughness, whereas if the spindle speed is low and the feed movement is high it will produce a high level of roughness.

Optimization of Turning Process Performance Characteristics during Machining of AISI 304 Material

Turning process variables significantly affect the turning process performance. In the present work, Taguchi technique was applied to optimize the turning process performance during machining of AISI 304 material under conventional cooling method. Turning process variables such as cutting speed, feed rate and depth of cut were considered for the study. Taguchi L9 orthogonal array (OA) experimental design plan was considered for carrying out the experiments. Cutting temperature, tool wear and surface roughness were considered as output responses. From the results, it was found that the Taguchi technique determined optimum process parameters significantly improved the turning performance. It is recommended that metal cutting industries could use these optimum cutting conditions to reduce the material waste and increase the productivity while machining of AISI 304 material.

Leveraging IoT in Auto Component Manufacturing to Monitor Surface Roughness and Tool Temperature

Surface finish is an important requirement in Auto Component Manufacturing that defines the quality of work piece obtained from turning operations. Thus to reduce downtime due to rejection of work piece for its surface finish a continuous monitoring of surface roughness based on its cutting parameter and vibration signal is required. Similarly it is also important to monitor the cutting tool temperature to have a longer tool life. With the emerging trend of Internet-of-Things (IoT) it has become very easy to collect the required data, storing it, analysing it and also alerting the required personnel through the mobile application, e-mail and SMS. The stored data and analysis could reveals in depth the process, which leaves a large scope for improvement of the system. The paper discusses the implementation of IoT to collect data of vibration signal and cutting tool temperature of dry turning operation, for cutting parameter like 1) spindle speed 2) feed rate and 3) depth of cut for three different material mild steel (MS), brass and aluminium (Al). A correlation was obtained to predict surface roughness and tool temperature. The system developed could give a real time monitoring of surface roughness on a smart phone that can be used as a tool for optimizing the turning process to give improved quality and increased production volume.

Multi-objective optimization for balancing surface roughness and material removal rate in milling hardened SKD11 alloy steel with SIO2 nanofluid MQL

In manufacturing practice, manufacturers always strive to achieve both quality and productivity targets simultaneously. In the first part, this study examines the relationship between input factors, including cutting speed, depth of cut, and feed rate, and the output response, which is surface roughness, when milling hardened SKD11 alloy steel under minimum coolant lubrication conditions using SiO2 nanofluid. The input parameters are divided into four levels to determine their influence on surface roughness and to find the optimal conditions for achieving the minimum surface roughness. The experimental design was conducted using an L16 array. A second-order regression model was developed to describe the relationship between the input variables and the output response. In the second part, multi-objective optimization was performed to simultaneously achieve the minimum surface roughness and the maximum material removal rate (MRR). The Response Surface Methodology (RSM) was employed in this study. The results indicated that to achieve the minimum surface roughness, machining should be performed at a cutting speed of 100 m/min, a cutting depth of 0.2 mm, and a feed rate of 0.01 mm/tooth. With these settings, the predicted surface roughness could reach 0.0451 µm. On the other hand, for the multi-objective optimization, to achieve the minimum surface roughness and the maximum MRR simultaneously, machining should be carried out at a cutting speed of 100 m/min, a cutting depth of 0.36 mm, and a feed rate of 0.0168 mm/tooth. With this cutting condition, the predicted surface roughness could reach 0.1069 µm, and the predicted MRR could reach 775.06 mm3/min

Dry milling of stir-cast and heat-treated EV31A magnesium alloy: An experimental investigation based on the multi-criteria optimization method

The purpose of this study is to examine the impact of various milling parameters and sample preparation methods on the end-milling of EV31A Mg alloy under as-cast, T4, and T6 heat-treated conditions. The test specimens were prepared through stir-casting and heat-treated according to T4 and T6 conditions. The CNC vertical milling machine with the HSS tool was used for the end-milling process based on Taguchi's L9 orthogonal array. Three levels of variation are applied to the process parameters, including spindle speed (S), feed rate (f), depth of cut (d), and test specimen (as-cast, T4, and T6 EV31A). The parameters of surface roughness (Ra, Rz, Sa, Sz), the rate of material removal (MRR), and the cutting force (Fr) were similarly measured. For surface roughness, optical profilometry was used, while the weight loss method and dynamometer were employed, respectively, to assess MRR and Fr. In order to improve the process parameters, TOPSIS (techniques order preferences by similarity to ideal solution) is employed. This method reduces several responses to a single measurable response (the closeness coefficient). S = 1500 rpm, f = 150 mm/min, and aP = 1.5 mm are the ideal process parameters to achieve the maximum closeness coefficient (Ci) of 0. 917129 under as-cast circumstances. Furthermore, analysis of variance (ANOVA) verifies that sample preparation methods of 24.59 % and depth of cut of 38.19 % significantly impact response characteristics.

Surface Quality Related to Face Milling Parameters in 3D Printed Carbon Fiber-Reinforced PETG

Three-dimensional printing technology holds significant potential for enhancing the flexibility and cost-efficiency of producing carbon fiber-reinforced polymer composites (CFRPs). However, it faces limitations such as challenges in achieving high surface qualityand precise dimensional accuracy and managing the distinctive anisotropic mechanical properties that it demonstrates. This study aims to explore the machinability of 3D printed PETG infused with 20% short carbon fiber and to assess the resulting surface roughness and burr formation. Employing a Design of Experiments (DoE) approach, three factors were considered: rotational speed, feed rate, and depth of cut. These factors were tested at varying levels—rotational speeds of 3000, 5500, and 8000 rpm; feed rates of 400, 600, and 800 mm/min; and depth of cut values of 0.2, 0.4, 0.6, and 0.8 mm. The evaluation of machinability relied on two key response parameters: surface roughness (Sa) determined from the milled surface and burr height measured on both sides using a roughness meter. The findings revealed a significant influence of milling parameters on both roughness and burr formation. However, the ideal conditions for minimizing roughness and reducing burr formation did not align. Furthermore, a comparative analysis was conducted between these results and the machinability of PETG under similar conditions.

Microtexturing of industrial surfaces via radial ultrasonic vibration-assisted machining: An analytical model and experimental validation

The aim of this paper is to develop an analytical model to predict different texture patterns obtained with Radial Ultrasonic Vibration Assisted Machining (RUVAM) and estimate the dimensions of their characteristic features. It is also validated experimentally by performing advanced surface metrology on C45 specimens. The model is based on considering the machining of craters along radial tracks as the main texturing mechanism due to the change in the effective depth of cut provoked by the ultrasonic vibration of the insert. Mathematical formulae composing the model are then developed by considering how the machined craters in those tracks interact radially and axially, describing the final texture as the result of a multidirectional geometrical interaction. To predict how those interactions take place depending on the manufacturing parameters chosen, threshold values for the feed rate and the depth of cut are proposed. They also allow the validation of the formulae that estimate the different geometrical descriptors of craters (radial and axial heights and widths). According to the different combinations of parameters, the model succeeds in proposing diverse potential texturing scenarios. In the second part of this paper, a full factorial design is planned and applied on C45 specimens to validate the results with a RUVAM tool of 40 kHz and a 1 μm-amplitude. The plan includes three factors (insert nose radius, depth of cut and feed rate). Their levels are defined according to the threshold values for feed and depth of cut defined by the model to reproduce different textures. Results prove that the proposed threshold values allow a very good assessment of the resulting microtexturing structures on the surfaces. The measured values of widths and heights are in agreement with the values predicted by the model. With this technique, the heights of the textured surfaces span from 5 to 400 μm and widths from 45 to 720 μm, depending on the selected parameters. In the radial direction, the vibratory motion of the insert is robustly imprinted on the material, with radial heights and widths of around 2 μm and 39.6 μm, agreeing with the model. In overall, errors comparing the experimental measurements and the values predicted by the model rise up to 9%, although this error can reach 15% at some cases on radial descriptors because of their higher sensitivity to the F-filtering. A last texturing mechanism can be attributed to RUVAM related to the shaping of craters, as different oval permeters can be obtained by combining the parameters adequately. All these results lead to potential future studies related with the functional implications of all the detected textures.

Carboxylated cellulose nanofibrils based on banana rachis as additive in oil-based emulsion coolant for effective flank wear reduction in lathe operation

Nitro-oxidized carboxylated cellulose nanofibrils (NOCNFs) from the banana rachis are employed in oil-emulsion coolant as an additive. The concentrations of NOCNF were 0.1, 0.2, 0.3 wt. %. The cutting parameters include the spindle speeds and depth of cut. The temperature and wear reduction were significant. The temperature at the tool flank decreased from 119°C to 76°C, corresponding to an 11% reduction in wear at the depth of cut co. 2 mm and spindle speed of 1330 rpm. The viscosity of the nanofluid and the corrosion of the low-carbon mild steel specimen are discussed. Arguably, a stable NOCNF nanofluid suspension is good against wear even at elevated temperatures. Interestingly, these results occurred at a lower NOCNF concentration co. 0.3% wt. compared to 1–2 wt. % from Al2O3 nanoparticles in literature. In this paper, banana rachis nanofibrils are attractive for developing of effective and environmentally friendly additives from biomass.

Optimizing SKD-61 steel machining for marine component manufacture: A grey-Taguchi study on nanofluid minimum quantity lubrication

SKD-61 steel finds extensive application in various sectors, including the marine industry where materials must withstand harsh aquatic conditions. In this study, we employ the Grey-Taguchi approach to optimize surface roughness (Ra), cutting force (Fc), and material removal rate (MRR) when hard-milling SKD-61 steel in pure minimum quantity lubrication (MQL) and nanofluid MQL environments, aligning with the demands of marine engineering. The orthogonal array method generates a set of experiments based on four input parameters: cutting speed, depth of cut, feed per tooth, and cutting condition, which are essential factors in marine component manufacturing. Our approach combines Grey relational analysis (GRA) with the Taguchi method to enhance multi-objective outcomes in machining. The results of this marine-focused study reveal that the lowest surface roughness, cutting force, and highest material removal rates are attained when the cutting conditions align with the principles of nanofluid MQL at a cutting velocity of 80 m/min, a depth of cut of 0.2 mm, and a feed per tooth of 0.01 mm/tooth. Keywords: hard milling, nanofluid MQL, optimization machining parameters; Grey relational analysis; Taguchi

An investigation on using artificial intelligence models to predict crater wear of tungsten carbide tool

In this study, artificial intelligence (AI) tools were utilised to predict and analyse the progression of crater wear in cutting tools made of tungsten carbide during machining of aluminium 7075 alloy with a CNC lathe. The study investigated the impact of corner radius, feed rate, cutting speeds, and cut depth on the wear of the tools. Thirty experiments were conducted, with 24 used for training 11 independent AI models and the remaining 6 used for testing. This study stands out for its novelty as it pioneers the evaluation of 11 distinct AI models for the prediction of tool wear. With a high level of accuracy and a lower average deviation, the most effective model identified in this study was the gradient boosting model. By integrating AI algorithms into manufacturing processes, the monitoring of tool wear becomes more efficient, leading to reduce experiments, minimise testing costs, predict tool life, prevent failures, and boost productivity.

Effect of Minimum Quantity of Lubricant on Carbide Tools and Surface Integrity in the Machining of Titanium Aluminides

Titanium aluminides are being explored as potential materials for the aeronautical sector. However, their application is limited by the high costs of processing and their difficulties in machining. This research evaluates the effectiveness of the minimum quantity lubrication technique (MQL) on the turning process of Ti48Al2Cr2Nb aluminide in terms of tool wear, tool life, cutting forces, surface integrity, and temperature. It was found that MQL conditions can improve the process efficiency, reducing the thermally induced wear mechanisms and enlarging the tool life compared to dry machining. Furthermore, it allows the cutting speed to be incremented, leading to lower processing times. However, MQL seems to not be effective in the reduction of the strain-hardening effect near the machined surface and, although the number of microcracks is reduced, defect-free surfaces cannot be obtained. Moreover, similar microstructural alterations as for dry cutting were detected. The best cutting conditions in terms of surface quality were assessed using the central composite face (CCF) design and surface response methodology. Optimization of the surface roughness under industrially viable cutting conditions was achieved with an average surface roughness value, Ra, of 0.29 µm (feed rate of 0.05 mm/rev, a cutting speed of 54.6 m/min and a depth of cut of 0.125 mm).

Modelling and Optimization of Machined Surface Topography in Ball-End Milling Process.

In order to optimize machined surface topography, this paper presents a novel algorithm for simulating the surface topography and predicting the surface roughness of a ball-end milling process. First, a discrete workpiece model was developed using the Z-map method, and the swept surface of a cutter edge was represented using triangular approximation. The workpiece surface was updated (i.e., material removal process) using the intersection between the vertical reference line and the triangular facet under a cutting judgement. Second, the proposed algorithm was verified by comparing the simulated 3D surface topography as well as 2D surface profile and average roughness (Sa) with experimental measurements. Then, numerical simulation examples planed by the Box-Behnken design methods were carried out to investigate the Sa in the ball-end milling operation. The correlations of Sa and cutting parameters were represented by a response surface reduced quadratic model based on the ANOVA results. Finally, the feed per tooth, radial depth of cut, and tilt and lead angles were optimized for improving the machining efficiency under the Sa constraints. This study presents an effective method for simulating surface topography and predicting the Sa to optimize the cutting parameters during ball-end milling process.

Experimental investigation of the effects of cooling-lubricating methods on the slot milling process of GTD-111 type nickel-based superalloy

Machining of nickel-based superalloys still poses several problems, especially slot milling, because the high work hardening, high metal carbide content and poor thermal conductivity cause the tool to wear quickly, with frequent breakages. This paper has dealt with the investigation of the effects of flood cooling, cryogenic cooling with Liquid Carbon Dioxide (LCO2), and the Minimum Quantity Lubrication and LCO2 combination on the cutting force, tool wear, chip morphology, surface roughness and the width of the milled slots. The authors have developed a new measurement method to measure the edge radius wear as a function of the depth of cut. The importance of this measurement method lies in the fact that at each measurement level, it is possible to determine the degree of wear of each edge radius and the homogeneity of the wear of the tool edges, which allows for determining how close the tool is to failure. Initially, the cooling had a positive effect on the cutting force for both LCO2 and LCO2 + MQL, but towards the end of the machining length, the cutting force was lower for flood cooling. The smallest tool wear resulted from the flood cooling, followed by LCO2 + MQL combination and LCO2. These results suggest that lubricating is more important than cooling for tool life. However, cooling plays a major role in chip breakage and surface quality, and in both cases, the best results were obtained by milling with the LCO2 + MQL combination.

Toward understanding the damage behavior during orthogonal cutting of 2.5D C/SiC composite based on multi-scale modeling and high-speed photography

2.5D C/SiC composite has been a critical high temperature material for aerospace filed due to its excellent wear, high temperature and oxidation resistance. Understanding the cutting damage behavior is crucial for achieving the high reliability application. This paper presents an in-depth study on damage behavior and material removal mechanism during orthogonal cutting of 2.5D C/SiC composite based on multi-scale modeling and high-speed photography. A three-dimensional numerical micro-macro multi-scale model is established considering the characteristics of the brittle SiC matrix, the isotropic carbon fiber reinforcement, and the pyrolytic carbon (PyC) layer. The orthogonal cutting experiments of 2.5D C/SiC composite with high-speed photography technology is carried out. The results show that the proposed model can accurately predict the microscopic deformation and fracture of the fiber. Meanwhile, surface fiber spring-back phenomenon is found based on high-speed photography, and its mechanism is first explanation based on the stress evolution analysis of the multi-scale model. In addition, it indicates that the increase of the depth of cut has a significant impact on the chip shape evolution, transitioning from powdery, needle-like, to block-like or strip-like shape. The paper covers some new sights for low-damage cutting of 2.5D C/SiC composite materials.

Enhancing Machining performance in Stainless Steel Machining using MXene Coolant: A Detailed Examination

Metal cutting, a complex process in manufacturing, involves various factors that significantly affect the quality of the final product. Notably, the turning process is crucial, with outcomes that heavily depend on multiple machining parameters. These parameters encompass speed, depth of cut, feed rate, the type of coolant used (specifically, high heat transfer MXene coolant), and insert types, among others. The material of the workpiece is also a critical factor in the metal-cutting operation. This study focuses on achieving optimal surface quality and minimizing cutting forces in the turning process. It recognizes the substantial impact of numerous process parameters, directly or indirectly affecting the product's surface roughness and cutting forces. Understanding these optimal parameters can lower machining costs and improve product quality. Our research concentrates on turning a stainless-steel alloy workpiece using a carbide insert tool. We employ the Response Surface Method (RSM) to optimize cutting parameters within a set range of cutting speed (100, 125, 150 m/min), feed rate (0.1, 0.2, 0.3 mm/rev), and depth of cut (0.4, 0.8, 1.2 mm). Additionally, we use various tool geometries and the RSM design of experiments to enhance and analyze the multi-response parameters of surface roughness and tool life. Optimal machining parameters for MXene-NFC involve a cutting speed of 140 m/min, a feed rate of 0.05 mm/rev, and a depth of cut of 0.5 mm. These settings ensure minimal surface roughness, maximum tool life, and the greatest total length of cut, achieving a composite desirability of 0.695.

Evaluation and Performance Improvement of Environmentally Friendly Sustainable Turning of 6063 Aluminum Alloy in Dry Conditions Using Grey Relational Analysis

Sustainable machining has gained importance in recent years due to its environmental, economic, and societal implications. Aluminium (Al) 6063 alloy involves turning operation to make it suitable for various applications. The work's novelty is assessing the machining characteristics along with sustainability indicators. This study aims to find the best-turning parameters for machining Al 6063 alloy. The turning parameters considered were cutting speed, feed rate, and depth of cut. A cutting speed of 200 m/min, feed rate of 0.05 mm/rev, and depth of cut of 0.25 mm were the best parameter combinations for achieving a good machining response. From the response value of mean grey relational grade (GRG) and analysis of variance (ANOVA), the depth of cut ranks one with 34.38%, which is the most dominating parameter in achieving the sustainable machining of Al 6063 alloy. Through grey relational analysis, optimized machining parameters resulted in a 72.84 percent reduction in carbon emissions, 72.82 percent reduction in energy consumption, 18.58 percent reduction in cutting power, and 6.83 percent reduction in surface roughness considering the initial parameter settings and best machining parameters. The enhancement in total GRG was 0.1702, indicating improvement in the desired responses. As a result of this study, it is clear that appropriate machining parameter selection aids sustainable machining of Al 6063 alloy.

Investigation on Surface Integrity in Milling of Inconel X750: A Comprehensive analysis of Cutting Edges and Machining Parameters

For over a century, machining has been a crucial manufacturing method, with milling standing out as a versatile and effective process for various engineering materials. Ni-based superalloys, renowned for their exceptional properties, present challenges in machining, causing excessive tool wear and surface quality issues. Techniques such as heat treatments, environmental adjustments, and tool geometry alterations are employed to mitigate tool wear and affect surface integrity, crucial for determining the quality of machined work materials, especially in high-strength superalloys where shear deformation induces grain misorientation. In this research article, an experimental study was carried out to find the impact of the different cutting edges on surface integrity while milling Inconel X750. Response Surface Methodology was employed using a central composite rotatable design of experiments to investigate the surface roughness of the machined sample under various cutting conditions. A thorough analysis was undertaken to explore the surface integrity of Inconel X750, involving variations in milling machining parameters (i.e., low, mid, and high) and the utilization of diverse cutting tool inserts. The investigation centred on analysing the sub-surface microstructure, texture, and residual stresses of the machined surface, aiming to achieve a comprehensive understanding of the material's surface characteristics. The double-coated cutting tool T2, with a diameter of 0.8 mm, exhibited superior performance in surface quality and grain distribution, particularly at low cutting parameters (feed rate: 0.15 mm/rev, cutting speed: 35 mm/min, depth of cut: 0.1 mm), showing fine equiaxed and lognormal grain distribution alongside low residual stresses (−36.8 MPa compressive, maximum shear residual stresses: 7.2 MPa) and a surface roughness of 0.17 μm.

PENGARUH JENIS MEDIA PENDINGIN TERHADAP GETARAN DAN NILAI KEKASARAN PERMUKAAN PADA PEMBUBUTAN MATERIAL BAJA AISI 1045

The coolant in the turning process functions to stabilize the temperature in the cutting process in addition to the temperature on the workpiece as well as the temperature on the tool, where an increase in temperature will certainly cause changes in mechanical properties. This research aims to find a correlation between surface roughness and vibration with variations in cooling in the AISI 1045 steel turning process. The research method for the AISI 1045 steel turning process is with a depth of cut 1 mm and a feed rate of 0.21 mm/rev and a rotation of 1,160 rpm, cooling using with variations, without cooling, dromus, and air coolant. Vibrations are detected in the tool during the turning process using a vibration meter and the surface roughness results are measured using a surface roughnees tester. The results show that the highest vibration value was produced in the process without cooling at 4.8 mm/s2, then the coolant cooler produced a vibration value of 4.3 mm/s2, and the dromus cooler produced the lowest vibration value at 3.9 mm/s2. Meanwhile, the highest surface roughness value was produced in the turning process without cooling at 6.247 µm, then the coolant cooling media produced a surface roughness value of 5.442 µm, and the dromus cooler produced the lowest surface roughness value at 4.848 µm. The vibration value and surface roughness show that there is a correlation that the higher the vibration, the higher the surface roughness value produced by turning AISI 1045 steel.

Optimization of Surface Roughness of Aluminium RSA 443 in Diamond Tool Turning

Context—Rapidly solidified aluminium alloy (RSA 443) is increasingly used in the manufacturing of optical mold inserts because of its fine nanostructure, relatively low cost, excellent thermal properties, and high hardness. However, RSA 443 is challenging for single-point diamond machining because the high silicon content mitigates against good surface finishes. Objectives—The objectives were to investigate multiple different ways to optimize the process parameters for optimal surface roughness on diamond-turned aluminium alloy RSA 443. The response surface equation was used as input to three different artificial intelligence tools, namely genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), which were then compared. Results—The surface roughness machinability of RSA443 in single-point diamond turning was primarily determined by cutting speed, and secondly, cutting feed rate, with cutting depth being less important. The optimal conditions for the best surface finish Ra = 14.02 nm were found to be at the maximum rotational speed of 3000 rpm, cutting feed rate of 4.84 mm/min, and depth of cut of 14.52 µm with optimizing error of 3.2%. Regarding optimization techniques, the genetic algorithm performed best, then differential evolution, and finally particle swarm optimization. Originality—The study determines optimal diamond machining parameters for RSA 443, and identifies the superiority of GA above PSO and DE as optimization methods. The principles have the potential to be applied to other materials (e.g., in the RSA family) and machining processes (e.g., turning, milling).