Machining Conditions Research Articles

Tool Wear Analysis During Turning with Single and Dual Supply of LN2

In the presented research work, LN2 supplied directly at rake face and in another localised machining condition, LN2 supplied at rake and flank face simultaneously. The DoE of performance of experiments was in accordance with Taguchi S/N ratio L18. It was found that flank wear length and crater wear width at LN2 supply at rake & flank face simultaneously declined by 23-38% and 20-30% respectively as compared to LN2 supply at rake face only. ANOVA gave the highest effect of contribution in the percentage to LN2 supply at rake & flank face simultaneously as 76.06% and 77.67%, next in decreasing order followed by speed, feed and depth of cut. SEM images depicted that flank wear length and crater wear width in both machining conditions. Tool wear was low during turning LN2 supply at rake and flank face. Optimized values of each response was confirmed by repeating the experiments.

Tribological behavior of Inconel 718 alloy when cutting with a carbide insert prepared using the SPS technique under dry and lubricated sliding conditions

The paper presents the results of the wear mechanisms research of uncoated cutting inserts of tungsten carbides (WC) without Co matrix made by the SPS technique in finish turning Inconel 718. The tests were performed with the application of coolant/lubrication and in dry condition. Moreover, morphology of the obtained chips was evaluated, as well as the topography of the machined surface. The coefficient of the chip shortening and the frequency of the chip vibration during machining were also determined. For the inserts used in the test, the friction coefficient, µ, has also been determined. Significant differences in the wear of the tool flank face for various feed speeds where the dominating sign was abrasive wear and oxidation. It has been found that the dominating mechanism of the wear of the rake face was adhesion of the material under machining whose surface exceeded beyond the field of the chip cross section regardless of value of feed and machining conditions (machining with cooling or without cooling). SEM analysis has shown that, in addition to adhesion, the major symptom of wear at lower feed values was delamination of the inserts; at higher feeds the major symptom of wear was chipping growing with the increase of feed. Machining with SPS inserts without the cooling and lubricating liquid has generated surfaces with lower average values of the roughness heights, Sa. It has been found that the chip shortening coefficient depends on the conditions of cooling/lubrication and that it decreases with the increase of the feed. The chip vibration frequency shows higher values in dry machining.

Experimental investigations and sustainability analysis on the performance of vegetable oil-based MQL drilling of SS316L

ABSTRACT Recently, efforts have been made to reduce the use of conventional cutting fluids. Minimum Quantity Lubrication (MQL) has become a favourable approach to enhance machining performance and sustainability. This study investigated the impact of various cutting conditions on the drilling performance of SS316L, considering surface roughness, tool wear, and chip forms. The drilling tests are performed under dry, flood, and vegetable oil-based MQL (VMQL) conditions. The drilling under the VMQL condition resulted in a desirable surface finish, as indicated by a lower Ra value of 1.81 µm for 20 holes drilled compared to the other conditions. The results highlighted the efficacy of VMQL in reducing tool wear during drilling. Furthermore, the analysis of chip quality demonstrated that VMQL produced favourable chip forms compared to flood cooling and dry machining. The enhanced lubricating and cooling mechanism enabled the creation of desired chip forms under VMQL. In summary, the study clarified the specific benefits of different machining conditions, highlighting the crucial importance of lubrication in achieving desired surface finish, chip quality, and tool wear in various machining environments. The sustainability assessment is conducted using the Pugh matrix approach, and the superiority of VMQL over other drilling conditions is found.

Surface topography and corrosion resistance of AISI 630 stainless steel after finish turning under different cooling methods

AISI 630 stainless steel (SS) is widely used for medical devices. This paper describes the effects of dry, flood and minimum quantity lubrication (MQL) machining conditions on the surface topography and corrosion resistance (CR) of AISI 630 SS after finish turning. Small areas of valleys and large areas of peaks were observed on the surfaces with minimum surface roughness (SRmin) after dry machining at the feed rate (f) of 0.1 mm/rev and at the f = 0.14 mm/rev using the MQL method. Only small areas of peaks were observed after flood machining at the f = 0.1 mm/rev. Regardless of cooling methods, regularly distributed deep valleys and high peaks were observed on the surfaces with maximum surface roughness (SRmax) machined at the f = 0.35 mm/rev. Surfaces of the samples with SRmin and SRmax under flood machining and with SRmax under MQL machining had high oxygen content in the range of ∼ (20−30) %wt. during 7–72 days of the sample storage in SBF. Such an oxygen content is observed on the machined surfaces for which maximum peak height Sp parameters were in the range of ∼ (10–16) μm and maximum valley height Sv – ∼ (8–10) μm. Compared to SRmin, high ion diffusion was observed on surfaces with SRmax. This means that surfaces with regular deep valleys and high peaks provide high ion mobility between them. In addition, compared to dry and MQL machining, higher resistivity was obtained for the samples under flood machining. Depending on the industrial application requirements, the passivity and CR of AISI 630 SS can be modulated by selecting machining conditions and surface topography. It has been established that in order to achieve favorable CR of the AISI 630 SS surface after finish turning, the flood method is recommended.

An effective investigation of chatter prediction system on Al6061 alloy in an end milling process

Visual examination of the surface topography, in conjunction with the other sensors, may confirm the existence of chatter. Online chatter detection during real machining operations is possible with the use of sensors, and the presence of noise in their output and restricted bandwidth are the major drawbacks of these sensors. Productivity drops and manufacturing costs go up when there is a lot of chatter in the machining process. In the present paper, an integrated spindle tool system is modeled using finite element method with Timoshenko beam theory including rotational and shear deformation effects. To maximize the average stable depth of cut in an end milling process while simultaneously minimizing the chatter vibration levels, real time and offline strategies have been investigated. Machining experiments performed on Al6061-alloy specimens provide an empirical confirmation of the stability boundaries. The surface topography methods such as scanning electron microscope (SEM) and optical microscope images along with vibration levels are considered, to identify chatter marks under various machining conditions, which helps to assure cutting process stability. Stability lobe diagrams are plotted with these derived conditions and observed at an incremental level in the axial depths of the cut. The methodology shown in this paper improves the machining stability of the end milling with the reduction in the tool tip vibrations.

SURFACE MORPHOLOGY OF SILICON NITRIDE (Si3N4)-TITANIUM NITRIDE (TiN) COMPOSITES USING GRA-RSM STRATEGY ENCOMPASSED WITH EDMed PARAMETERS

This investigation explores the application of electrical discharge machining (EDM) to ceramic composites constructed from Si3N4–TiN (Syalon 501) by testing both square and cylindrical electrodes. Various experimental methods have been employed, including design-of-experiments (DoE), Grey relational analysis (GRA), analysis of variance (ANOVA), confirmation testing, and scanning electron microscopy (SEM), in an effort to optimize machining settings and comprehend their effect on performance. While ANOVA revealed crucial parameter levels influencing overall machining conditions as measured by the grey relational grade (GRG), the sophisticated method of GRA considerably improved the machining characteristics. Significant improvements in the EDM process were shown experimentally, resulting in outstanding results for material removal rate (MRR), surface roughness (SR), and electrode wear rate (EWR). In particular, numerous benefits of the square electrode layout over the more common cylindrical arrangement stood out. All of the aforementioned resulted in an impressively high MRR (measured at 0.0281[Formula: see text]gm/min), a low EWR (at 0.0020[Formula: see text]gm/min), and a significant improvement in SR (at 0.3150[Formula: see text][Formula: see text]m). The GRG value was 0.0163 for the square electrode design, which was excellent, and 0.0025 for the cylindrical electrode, which was significantly better. The optimized parameters were successfully used, especially with the square electrode arrangement, leading to significant improvements in MRR and decreases in EWR. On the other hand, surface quality was negatively affected by larger parameter values, which increased the development of microfractures, particularly when using the square electrode design. In any case, our study provides strong proof that adjusted settings can greatly improve EDM performance, allowing for accurate machining of Si3N4–TiN composites with increased MRR, great surface texture, and reduced electrode wear. In conclusion, our study provides useful information to improve EDM procedures for Si3N4–TiN ceramic composites, as well as practical insights into increasing the efficiency and quality of EDM operations in a variety of industrial sectors.

Unraveling the complex interplay between elastic recovery, contact pressure, and friction on the flank face of the micro tools via plunging-type testing

A good understanding of the interplay between the cutting tool edge radius, elastic recovery, friction, and contact pressure is essential for better modeling of ploughing forces during micro-scale cutting. This study conducts plunging tests on an ultra-precision CNC with engineered tungsten carbide cutting tools on commercially pure titanium alloy. The cutting tool edge radius is prepared to be around 3.5–4 μm, which resembles those cutting tools used in micro scale machining. During plunging tests, the micro cutting tool is given a sinusoidal movement with an amplitude close to edge radius of the tool as the work material is rotated at a constant speed. The residual depth profiles of the webs corresponding to the commanded depths were investigated in detail to identify elastic recovery rate. The cutting and thrust force measurements during plunging experiments together with identified elastic recovery rate was employed in an analytical model of micro scale machining to obtain the variations of contact pressure and coefficient of friction as a function of commanded depth. Due to the scale of the experiments that were performed, the effects of surface topography of the cutting tool and possible alignment errors are also considered in the analytical model. A linear relationship between the contact pressure and elastic recovery has been identified during ploughing-dominated machining conditions for the work material and the cutting tool pair considered in this study. The proposed experimental technique is shown to be promising in terms of modeling ploughing forces during micro-scale cutting.

Comparative Study of Machining Parameters of Single and Double Cutting Tools During Turning of AISI 1045 Steel

Background:: The patent of cutting operations is carried out with a cutting tool that is fed parallel to or at right angles to the work axis. The main objective of this study is to minimize surface roughness and MRR. Objective:: The effect of cutting parameters on surface roughness and material removal rate is investigated using AISI 1045 steel as a workpiece material, and single and double carbide cutting tools are used under dry machining conditions. Methods:: The cutting speed, feed rate, and depth of cut are considered input parameters for experimental purposes. Taguchi L9 orthogonal array design of experiments is used for designing the experiments. Parameters are optimized using Taguchi L9 orthogonal design of experiments and Analysis of Variance (ANOVA). MINITAB 17 software is used to solve the coefficients of the regression model. Results:: The result indicates that the cutting speed was the most significant influencing factor that affects the surface roughness, followed by feed rate and depth of cut for both single and doublecutting tools. Conclusion:: The minimum surface finish for the best cutting parameter was 0.95 μm for a single and 0.92 μm for a double-turning tool. The highest material removal rates for single and double turning were 6456 mm3/min and 6603 mm3/min. The result shows that while using double tools, the rate of material removal rate increased and the machining time decreased.

Recent Patents on Thermal Characteristic Analysis and Modeling of Machine Tools

Abstract: Modern machining machines are becoming more sophisticated and automated, but the problems of thermal deformation caused by machining have also come to the fore. CNC machine tools are used in high-speed, high-feed rate machining conditions, so the impact of thermal deformation factors will lead to a more prominent loss of accuracy. In this context, the design and manufacture of future CNC machine tools need to take further account of the errors caused by thermal deformation and the accompanying loss of accuracy. The principle of thermal characterisation and thermal error modeling of machine tools is to measure and reasonably analyse the machine tool at different temperature points and make corrections to the analysis data in order to get a value as close as possible to the real value and improve machining accuracy. Several representative patents and studies on thermal characterisation and thermal error modeling of machine tools at home and abroad are reviewed. This study presents a selection and summary of a large number of recent patents and studies on machine tool thermal characterisation and thermal error modeling, focusing on the selection of the machine tool thermal error measurement point area, thermal error modeling methods, spindle thermal displacement compensation, and other aspects, and discusses the future trends in the mainstream methods for reducing the thermal error of machine tools. The analysis and modeling of thermal errors in machine tools have an integral influence on further improvements in machining accuracy and efficiency in the future.

선삭 절삭력 예측을 위한 지식 기반 툴 개발

In this study, a knowledge-based tool is developed to enhance the manufacturing efficiency of mechanical components, particularly those requiring the removal of large components, such as the struts of airplane landing gears. Using this tool, knowledge is extracted and implemented during the turning process. The cutting forces and material-removal quantities are calculated based on the obtained knowledge. User-friendly graphical user interfaces are provided for user interactions. They can be used to input various parameters, such as the workpiece, cutting tool, and machining conditions, including the cutting material, rotational speed, and cut depth. The tool outputs essential parameters, such as the cutting forces, torque, power consumption, and material-removal rate, under different cutting conditions. More importantly, the tool enables users to conduct trade-off analyses, thus enabling the identification of near-optimal cutting conditions while accounting for factors such as tool life and production efficiency. This study contributes to the machining of high-strength materials by providing a useful tool for efficient industrial manufacturing.

Analyze the effects of CNC machining parameters on the surface roughness (Rz) of Anatolian chestnut

The effect of processing parameters on the surface roughness parameter Rz (mean peak-to-valley height) in CNC machining of Anatolian chestnut (Castenia sativa Mill.) wood species used in the woodworking and furniture industry was investigated. The machining conditions were determined to obtain the lowest Rz value. The experi-ments were carried out at the speeds of 8000, 12000, and 16000 rpm and the feed rates of 1000, 1500, and 2000 mm/min with 3 and 4 machining layers by using two different cutters with a diameter of 8 mm. Rz measurements were made on the obtained surfaces using the needle scanning method according to TS EN ISO 21920-2 (2021). In the measurements perpendicular to the fibers, the lowest Rz values were obtained with cutter type 2 (the cutter of the three cutting edge straight end mill), at 16000 rpm speed, 1000/2000 mm/min feed rate, and four machining layers. In the measurements made parallel to the fibers, the lowest Rz values were obtained at the cutter type 2 (the cutter of the three cutting edge straight end mill), at the speed of 16000 rpm and the feed rate of 1000/2000 mm/min and 4th step-down. A smooth surface (lower Rz value) was obtained with fourth cuts (step down, cut layer) instead of third cuts.

Detection of electrolyte supply state in the electrochemical discharge machining of micro hole based on deep learning

Electrochemical discharge machining (ECDM) is an effective technique for machining hard and brittle insulating materials due to its characteristics that the occurrence of electrochemical discharging is independent of the electrical conductivity of the workpiece. In ECDM, the machining performance is significantly influenced by the real-time machining state. However, the real-time machining state still cannot be effectively detected and fed back until now. To improve the performance of ECDM and establish a useful closed-loop control strategy, it is essential to find a new signal that characterizes the machining state. Electrolyte supply is critical for ECDM and can be used as a closed-loop control factor. In this paper, a novel method for detecting the electrolyte supply state in the machining zone based on current signals was proposed. To achieve the method, a R-BiLSTM-A (ResNet-BiLSTM-Attention) model was designed for electrolyte supply state detection. The designed model was demonstrated to perform better than the other general artificial intelligence models. The relationship between the contact force and the electrolyte supply state detection results was investigated. It was found that the electrolyte supply state was insufficient when the tool-workpiece contact occurred. This state was successfully detected by the model, which verified the effectiveness of the designed model. The proposed model was then applied to detect the electrolyte supply state under different machining conditions. The tool electrode feed rate and electrode retreat parameters were optimized based on the detection results of the proposed model. The hole depths were improved by 126.50 % and 301.69 % with optimized machining parameters at the voltage of 39 V and 43 V, respectively.

Three-dimensional characterization of abrasive chips using micro-computed tomography

Chip formation is considered one important indicator to evaluate machining processes. In particular, geometric features of chips may provide important insights for the assessment of machining stability and productivity. In fixed-abrasive machining processes, such as grinding and honing, chips are simultaneously produced by many of the geometrically undefined cutting edges. Despite being “undefined,” geometric features of abrasive grains can still be statistically characterized or described. Accordingly, it can be assumed that, under stable machining conditions, the geometric features of abrasive chips may also conform to certain statistical patterns. However, statistical characterization of abrasive chips can be very challenging due to their large quantity, irregular shapes, minuscule size, and sometimes tangled condition. In this study, an analysis method combining metallographic preparation and micro-computed tomography (micro-CT) has been developed to characterize abrasive chips produced by a honing stone. The results regarding the geometric characteristics of the obtained massive abrasive chips, i.e., their sizes and shapes, were presented and statistically described. It was found most chips had a size around 50 µm and the shape being a slightly elongated and curved cone. Most of the geometric features could meet a positive skewness distribution.

Influence of Al2O3 nanoparticle mass concentration and aerosol formation parameters on tool vibration during turning of Ti6Al4V titanium alloy

Machining difficult-to-cut materials involves challenging machining conditions, including higher temperatures in the cutting zone, cutting forces and friction. Another important phenomenon is vibration, which is undesirable when manufacturing high quality workpieces. One way to reduce vibration in the cutting zone is to use cooling methods. Due to its environmentally friendly nature, the minimum quantity lubrication (MQL) method has already been widely used in metalworking. However, when combined with nanofluids, it improves the ability of the aerosol to dissipate more heat and increase lubrication in the cutting zone. This paper presents the effect of a polyol ester-based Al2O3 nanofluid due to the varying mass concentration of nanoparticles on the vibration during turning of Ti6Al4V alloy and compares the results with dry cutting and the MQL method without nanoparticles. Four concentrations (0.25−1 wt%), variable nanofluid flow rate E = 0.388−1.182 g/min and air flow rate P = 10−40 l/min were considered. According to the statistical analysis, the most important factor influencing tool vibration was the mass concentration of nanoparticles in the cutting fluid. By combining the MQL method with 0.5 wt% Al2O3, the vibration acceleration RMS values were found to be the lowest. When compared to the MQL method without nanoparticles, the RMS values for dry cutting ranged from 17.8% to 24.9%, and for wet cutting they were reduced by about 10.9-18.5%.

Predictive modelling of residual stress in turning of hard materials using radial basis function network enhanced with principal component analysis

This study proposes a hybrid machine learning (ML) model that combines a radial basis function network (RBFN) with principal component analysis (PCA) to predict residual stress (RS) in the machining process. Higher temperatures and plastic deformation can generate RS conditions in the machining of hard materials, significantly influencing the quality of machined parts, particularly their surface integrity. It is crucial to evaluate and guarantee machining conditions that ensure reliable surface integrity that yields compressive RS conditions. Incorrect parameter settings can lead to poor surface quality, resulting in tensile RS conditions that impact both the quality and lifetime of manufactured products. The methodology involved experimental machining trials for data acquisition, with conditions selected for hard material machining tests conducted using a computer numeric control (CNC) lathe under dry cutting conditions. The cutting force components were measured, and RS was evaluated using X-ray diffraction. A total of 60 trials were conducted. Data preprocessing, including normalization and removal of outliers, was executed before constructing the ML algorithm, leaving 59 valid tests for model training and testing. Separate datasets for training (70 % of the data) and testing (30 %) were randomly selected from the valid experimental tests. PCA enhanced the RBFN model’s generalization by reducing data dimensionality and providing the hidden units parameter. The eigenvectors obtained through PCA served as an efficient initial reference for RBF centroids, highlighting principal directions of variation. The PCA-RBFN model, using a multiquadric radial function, exhibited robust performance in capturing underlying patterns during training and demonstrated balanced results in the tests. In the PCA-RBFN model training stage, it exhibited a coefficient of determination (R2) of 76.66 %, a mean average error (MAE) of 72.15 MPa, and a root mean square error (RMSE) of 84.94 MPa. In the testing stage, the model showed increased metrics, indicating its ability to predict RS values accurately, with an R2 of 80.54 %, MAE of 51.25 MPa, and RMSE of 70.36 MPa. The evaluation between the training and testing stages highlighted the model’s coherence in performance, demonstrating its ability to generalize well to new data. A second comparative analysis was conducted with a conventional RBFN approach using stochastic method for centroid determination. The hybrid PCA-RBFN method exhibited lower computational effort, requiring only three iterations compared to 221,923 iterations in the conventional approach. Despite the conventional approach yielding better metrics in the training stage, it exhibited signs of overfitting, a common concern in machine learning models. The hybrid PCA-RBFN consistently outperformed it regarding generalization and fit during testing. The proposed methodology holds practical implications for the manufacturing industry, allowing the integration of a pre-trained predictive model into machine controllers or smart sensors. The hybrid PCA-RBFN’s computationally lightweight approach also supports incorporating real-time adaptive algorithms into production systems, enabling continuous real-time adjustments to the model factors and weights, thereby improving automated control.

Machinability investigation of grinding 2.5D-SiCf/SiC composites under nanofluid minimum quantity lubrication

SiCf/SiC composites are now the preferred option for the thermal structures of next-generation advanced aero-engines, owing to their high-temperature strength, and creep resistance. Grinding is currently a highly reliable and mature method for SiCf/SiC processing. However, the harsh, dry machining conditions lead to degradation of surface quality, and the dust generated can harm both humans and the environment. Nanofluid Minimum Quantity Lubrication (NMQL) maximizes heat transfer and lubrication in grinding and is environmentally and human-friendly. Based on this, this study thoroughly investigates the effects of dry, flood grinding, minimum quantity lubrication, and different nanofluids (MoS2 and carbon nanotubes (CNTs)) on grinding performance at different grinding depths. Compared with dry grinding, the results show that the normal grinding component force Fn is reduced by 56.9 %, 56.8 %, and 71.6 %, and the tangential grinding component force Ft is reduced by 48.4 %, 50.5 %, and 57.5 % for NMQL-CNTs lubrication condition at grinding depths of 0.2 mm, 0.4 mm, and 0.6 mm, respectively. Meanwhile, NMQL-CNTs can significantly improve the surface quality and help to obtain a surface morphology with fewer surface defects, and the stabilized lubricant film formed by NMQL-CNTs can significantly reduce the specific grinding energy. Although brittle removal was observed in different lubrication methods, NMQL-CNTs could significantly reduce surface damage due to their filling and friction reduction effects. Due to the high viscosity of NMQL-CNTs and the low intermolecular forces, they have strong lubrication stability and wear resistance. This study provides theoretical guidance for the green processing of SiCf/SiC composites.

Optimizing the Electrical Discharge Machining Process Parameters of the Nimonic C263 Superalloy: A Sustainable Approach

Engineers continue to be concerned about electrical discharge-machined components’ high energy consumption, machining debris, and poor dimensional precision. The aim of this research is to propose a hybrid neuro-genetic approach to improve the machinability of the electrical discharge machining (EDM) of the Nimonic C263 superalloy. This approach focuses on reducing the energy consumption and negative environmental impacts. The material removal rate (MRR), electrode wear ratio (EWR), specific energy consumption (SEC), surface roughness (Ra), machining debris (db), and circularity (C) are examined as a function of machining parameters such as the voltage (V), pulse on time (Ton), current (I), duty factor (τ), and electrode type. By employing the VIKOR method, all the responses are transformed into a distinctive VIKOR index (VI). Neuro-genetic methods (a hybrid VIKOR-based ANN-GA) can further enhance the best possible result from the VIKOR index. During this step, the hybrid technique (VIKOR-based ANN-GA) is used to estimate an overall improvement of 9.87% in the response, and an experiment is conducted to confirm this condition of optimal machining. This work is competent enough to provide aeroengineers with an energy-efficient, satisfying workplace by lowering the machining costs and increasing productivity.

LEVEREGING TECHNOLOGICAL HEREDITY TO INCREASE PRODUCTION EFFICIENCY OF FERROCERRAMIC PRODUCTS DURING FINAL MACHINING

The quality state of grinded surface of ferro ceramic products is formed under the influence of thermomechanical phenomena which occurs during final machining and depends on the technological conditions for workpiece procurement. The mathematical model was formulated to regulate and optimize thermomechanical processes during the acquisition of ferroceramic workpieces. This mathematical model describes thermomechanical processes during workpieces sintering. Thermomechanical processes have a direct influence on defects formation in the workpieces. Grinding can cause the appearance of burns, cracks, tensile stresses in the surface layers of the products. These defects can significantly spoil the quality of products during their operation. High thermal stress of diamond-abrasive processing brings thermophysical aspects as a dominating factor for quality characteristics of processed surface. Existing grinding methods for products made from ferrocerramic materials are not able to fully eliminate the defects in the surface layer. Such defects are inherited from preceding machining operations, particularly from workpiece procurement. Material structure itself is prone or defect occurrence due to micro-heterogeneities, packaging defects, dislocations, and structural transformations. Analysis of the thermomechanical processes that run inside the surface layer made it possible to formulate calculation dependencies for defining technological conditions for eliminating burns and cracks during grinding of ferrocerramic products. Device for automatic stabilization of thermomechanical characteristics that accompany grinding of ferrocerramic products. This is achieved through the selection of optimal technological conditions for machining of the products that have heredity inhomogeneities inside the surface layer. Thus, this approach helps to achieve maximum efficiency within required quality citeria.

Study of the effect of spark radius model upon the performance characteristics in EDM process

The evaluation of performance characteristics is of key importance to the EDM process technological reliability. By correctly estimating the power fraction and plasma flushing efficiency, it is possible to improve the accuracy of the surface integrity prediction of the EDM discharge models. In the present research, a combined computational and experimental approach is employed to study the effect of spark radius model upon the performance characteristics in EDM of Ti-6Al-4V alloy. Two famous spark radius relationships have been implemented and the effect on power fraction and removal efficiency was examined for a range of machining conditions. It has been found that the sparking radius relationship accounting for discharge current effect captures the essential dynamics of the EDM process and offers improved prediction accuracy. In such a case, the power fraction going to the cathode ranges from 8.5 to 24 %. When a cooper electrode is utilized, removal efficiency of up to 12 % was observed on the cathode surface. For specific machining conditions, an approximate constant ratio of 2.5 is seen between the power fractions transmitted to the cathode and tool-anode. Crater volume was computed and compared to the experiment to validate the proposed approach. The results of this comparison challenge the EDM model underlying assumption viability, which considers similarity between recast layer and electrode materials thermophysical proprieties when a hydrocarbon-based dielectric fluid is used. From a qualitative perspective, though, the outcomes are still encouraging and indicate that surface integrity in the EDM of Ti-6Al-4V alloy may be predicted using the performance characteristics found in this study.

Evaluation of the turning parameters of AISI 5115 steel in dry and MQL cutting environments with the use of a coated carbide cutting insert: An Experimental Study

This study investigates the effects of cutting parameters on turning AISI 5115 steel in both dry and MQL environments using a coated carbide insert. The cutting parameters are determined using a full factorial design. A comprehensive full factorial experimental design was executed in order to investigate the effect of cutting parameters, including cutting speed, feed rate, and depth of cut, on surface roughness, cutting force and cutting temperature. Following the completion of the turning trials, surface roughness measurements were meticulously recorded. Also cutting force and cutting temperature were measured. The results of the study indicated that the most significant influence on surface roughness is exerted by the feed rate. Moreover, the impact of the depth of cut became more significant as the cutting speed decreased. While the surface roughness increased by 23% in the dry environment due to the increased feed rate at low cutting speed, the increase in the MQL environment was 32%. The cutting temperature is influenced by a number of factors, including the cutting parameters and the material properties. The maximum temperature for turning in the MQL environment was 381°C compared with an average cutting temperature of 430°C in dry machining conditions. The application of high-speed cutting in a dry cutting environment was found to result in a 10% increase in cutting temperature. The influence of cutting speed on the outcome was less pronounced in the MQL environment. At high cutting speeds and low parameter values in the MQL environment, the cutting force decreased by 75% in contrast to the low cutting speeds and high cutting parameters in the dry environment. The optimal cutting conditions for minimising cutting force were identified in the MQL environment, characterised by high cutting speeds and low feed rates.