Machining Conditions Research Articles

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.

Experimental Investigations and Optimum Performance Evaluation of Wire-EDM Characteristics of Aluminium 6061-Magnesite Composites

It is essential to determine the most suitable machining method for magnesite-reinforced Aluminium 6061 composites, which possess excellent mechanical properties, especially notable tensile strength and hardness. The composites were produced using a stir-casting technique, incorporating reinforcements of lightly-calcined magnesite, dead burnt magnesite, and waste magnesite in weight fractions of 2.5%, 5%, and 7.5% within an aluminium 6061 matrix. Wire electrical discharge machining was employed to investigate the machining characteristics of these composites, using controllable process parameters such as cutting speed, pulse-on and pulse-off times, and the weight fraction of magnesites. Two performance indicators such as surface roughness and material removal rate were tested for various parameter combinations by central composite design. To comprehend the impact of the study parameters, contour charts were drawn. MRR increases at a high cutting speed of 2 mm/min when the pulse-on time changes from 120 μs to 125 μs. SR increases when the pulse-on times above 120 μs at all cutting speeds. High cutting speeds make high MRR irrespective of the weight fractions of reinforcement. High pulse-on times make the material melt more, which increases the material removal rate. Because specimen surface material erodes quickly and forms microcracks, high pulse-on time also results in high surface roughness. To optimize the WEDM machining conditions for each composite, hybrid SSO-DF and DFO-DF optimizers were developed by combining the desirability function with Salp-swarm optimization and Dragonfly optimization algorithms. The cutting speed of 2 mm/min and the pulse-on time of 114 μs produce the best performances on the composites.

Additively manufactured Ti-5Al-5V-5Mo-3Cr: Understanding defect-fatigue relationships

Ti-5Al-5Mo-5V-3Cr (Ti-5553), a near-β Ti alloy, possesses an excellent combination of strength, toughness, and strength-to-weight ratio, and due to its poor machinability characteristic, it is poised to be additively manufactured for key structural applications. This study examines the micro-/defect-structure and mechanical behavior, including both tensile and fatigue, of laser powder bed fused Ti-5553. The tensile and fatigue specimens are fabricated in two locations on the build plate with respect to the powder feed direction, i.e., west (away from powder reservoir) and east (near powder reservoir), to examine the location dependence of the mechanical properties. In addition, fatigue specimens are furnished in both un-machined and machined surface conditions. It is found that the volumetric defect content is higher for the west specimens than the ones in the east; however, tensile and fatigue properties are not affected by part location. The insensitivity of fatigue lives to the part location can be ascribed to specimens having statistically equivalent defect size distribution near the surface, as fatigue failures are primarily initiated by critical defects on or in the vicinity of the surface in un-machined and machined specimens.

Minimization of residual stress, surface roughness and tool wear in Electro Discharge Machining of inconel 625

Electro Discharge Machining (EDM) is a well-known non-traditional machining technique which is widely used to make die castings and turbine blades out of difficult-to-cut materials like Inconel 625. The machined part's surface quality and dimensional accuracy are affected by the EDM electrode's wear. Moreover, surface roughness and residual stress augmentation of EDM-machined components can reduce the workpiece's lifespan by decreasing the fatigue life of machined parts. To enhance the precision and durability of EDM machined components, residual stress, tool wear, and surface roughness should be evaluated and minimized. The majority of published research works for the assessment and optimization of EDM machining parameters are based on experimental works which are limited to testing, workpiece materials, and machining conditions. In order to improve the quality of machined surfaces and reduce tool wear and residual stress during Inconel 625 EDM processes, a virtual machining approach has been developed in the research work. To predict the cutting temperature during EDM operations, the modified Johnson Cook model of Inconel alloys is used. The finite element approach is then used to calculate the generated residual stress during the EDM process. The proposed virtual machining approach is also applied in order to predict the surface roughness of EDM machined parts. To minimize the residual stress, tool wear, and surface roughness during EDM operations of Inconel 625, the machining parameters of gap voltage, peak current, pulse-off time, and pulse-on time are optimized using the Taguchi optimization approach. Experiments and simulations are then conducted to verify the developed virtual machining system in the study. So, using the optimal machining parameters, the residual stress, surface roughness of machined items and wear of EDM electrode are minimized by 24.5 %, 25.4 %, and 25.4 % respectively. Thus, the quality and reliability of components made using EDM processes may be enhanced by the suggested virtual machining system.

Perbandingan Keutuhan Permukaan Bahan Inkonel 718 dalam Pemesinan Kriogenik dan Kering

Inconel 718 is a nickel-based alloy that has been developed with superior mechanical strength, creep resistance, and corrosion, as well as erosion resistance at temperatures above 649 ˚C. It is suitable to be used for aircraft engine components such as turbine discs. However, it also has low heat conductivity and the presence of hard carbide particles in its alloy worsens the machining conditions with high cutting temperature, and high shear force and promotes work hardening. Thus, this study aims to evaluate the surface integrity of Inconel 718 in terms of surface roughness, plastic deformation, and micro-hardness alterations after the turning process. Cryogenic CO2 cooling was supplied along the cutting process to reduce cutting temperatures and the machining performances were compared with dry machining. The experimental works show that the dry machining resulted in lower surface roughness by up to 42.15% as compared to cryogenic. However, the depth of plastic deformation under dry machining was much more severe than in cryogenic machining driven by higher cutting temperatures and pressure from the worn tool. The depth of plastic deformation became more distinct as the cutting speed increased. While the microhardness alteration of the machined surface was higher under cryogenic machining due to extensive cooling capacity by the CO2 flow. Thus, this study reveals that cryogenic cooling during metal cutting can produce products that have better wear resistance as well as higher surface hardness.