Search Results (121)

In the process of machining an aluminum matrix silicon carbide (SiCp/Al) composite material using wire electric discharge machining (WEDM), the thermal conductivity and dielectric properties of working fluid, such as discharge medium and cool carrier, directly determine the material removal rate (MRR) and surface roughness (R_a). In this paper, graphene-working fluid is innovatively used as working medium to optimize the discharge process due to its high thermal conductivity and field emission characteristics. The single-factor experiments show that graphene can increase the MRR by 11.16% and decrease the R_a by 29.96% compared with traditional working fluids. In order to analyze the multi-parameter coupling effect, an L16 (4⁴) orthogonal test is further designed, and the effects of the pulse width (T_on), duty cycle (DC), power tube number (PT), and wire speed (WS) on the MRR and R_a are determined using a signal-to-noise analysis. Based on a gray relational grade analysis, a multi-objective optimization model was established, and the priority of the MRR and R_a was determined using an AHP, and finally the optimal parameter combination (T_on = 22 μs, DC = 1:4, PT = 3, WS = 2) was obtained. Full article

Wire electrical discharge machining (WEDM), as a significant branch of non-traditional machining technologies, is widely applied in fields such as mold manufacturing and aerospace due to its high-precision machining capabilities for hard and complex materials. This paper systematically reviews the research progress in WEDM process optimization from two main perspectives: traditional optimization methods and artificial intelligence (AI) techniques. Firstly, it discusses in detail the applications and limitations of traditional optimization methods—such as statistical approaches (Taguchi method and response surface methodology), Adaptive Neuro-Fuzzy Inference Systems, and regression analysis—in parameter control, surface quality improvement, and material removal-rate optimization for cutting metal materials in WEDM. Subsequently, this paper reviews AI-based approaches, traditional machine-learning methods (e.g., neural networks, support vector machines, and random forests), and deep-learning models (e.g., convolutional neural networks and deep neural networks) in aspects such as state recognition, process prediction, multi-objective optimization, and intelligent control. The review systematically compares the advantages and disadvantages of traditional methods and AI models in terms of nonlinear modeling capabilities, adaptability, and generalization. It highlights that the integration of AI by optimization algorithms (such as Genetic Algorithms, particle swarm optimization, and manta ray foraging optimization) offers an effective path toward the intelligent evolution of WEDM processes. Finally, this investigation looks ahead to the key application scenarios and development trends of AI techniques in the WEDM field for cutting metal materials. Full article

Wire electrical discharge machining (WEDM) is a standard micro-manufacturing technology. In WEDM, surface roughness (SR), deviation dimension (DD), and machining time (MT) are critical requirements that impact machining quality and are affected by various input parameters. The workpiece often performs multiple machining steps (roughing, semi-finishing, and finishing) to achieve high accuracy. Each machining step directly affects the accuracy and machining time, and the preceding machining step influences the subsequent machining step parameters. Many input control parameters regulate WEDM’s performance. Thus, optimizing process control parameters at each step is essential to achieve optimal results. This study investigates the influence of input parameters, including pulse on time (Ton), pulse off time (Toff), and servo voltage (SV), on SR, DD, and MT when machining AISI D2 mold steel through rough, semi-finish, and finish cutting. Taguchi and Analysis of Variance (ANOVA) are applied to analyze and optimize this WEDM process. The results display that the optimal surface roughness values for rough, semi-finish, and finish-cut stages are 2.03 µm, 1.77 µm, and 0.57 µm, corresponding to the parameter set of Ton = 6 μs, Toff = 10 μs, and SV = 30 V; Ton = 3 μs, Toff = 15 μs, and SV = 60 V; and Ton = 21 μs, Toff = 45 μs, and SV = 60 V, respectively. In addition, in the finish-cut stage, the parameters for optimal DD of 0.001 mm (0.04%) are Ton = 3 μs, Toff = 15 μs, and SV = 40 V. In contrast, those values for optimal MT of 218 s are Ton = 3 μs, Toff = 30 μs, and SV = 40 V. All optimal input values are confirmed by the manufacturing mold and die parts. Full article

Understanding wire-cut electrical discharge machining (WEDM) parameters’ impact on surface roughness (Ra) is crucial for optimizing processes. This study uses artificial neural network (ANN) techniques to estimate the surface roughness of Al/SiC composites during WEDM, examining how process parameters affect the roughness. The experiment used a stir casting aluminum alloy with a 7.5% silicon carbide metal matrix composite (MMC), adjusting parameters like the wire tension (WT), servo voltage (SV), peak current (IP), pulse on time (TON), and pulse off time (TOFF). An ANN model was created to forecast the surface roughness. The study developed an ANN model to forecast surface roughness in Al/SiC composites during WEDM, demonstrating its accuracy in identifying the link between surface finish and input parameters, thereby improving the surface quality. The ANN model accurately predicted the surface roughness based on WEDM parameters, with strong correlations between predictions and actual data, demonstrating its ability to estimate surface quality accurately. Full article

9Cr18MoV stainless steel is widely employed in cutting-tool applications owing to its exceptional hardness and corrosion resistance. In this study, we systematically optimized the wire electrical discharge machining (WEDM) process parameters for 9Cr18MoV stainless steel through an L₁₆ (4⁴) orthogonal experimental design. The key parameters investigated include pulse width (T_on), pulse interval (T_off), peak current (IP), and wire feed speed (WS), with cutting efficiency (CE) and surface roughness (R_a) serving as the primary optimization objectives. A signal-to-noise ratio (SNR) analysis was applied to assess the effects of the individual parameters and derive single-objective optimal configurations. Subsequently, grey relational analysis (GRA) integrated with analytic hierarchy process (AHP)-based weighting was employed to establish a multi-objective optimal parameter set, which was experimentally validated. The results reveal that the optimal multi-objective performance was attained at T_on = 28 μs, T_off = 3 μs, IP = 9 A, and WS = level 3. SEM characterization confirmed that this parameter combination yields a more uniform surface morphology, with diminished oxidation and molten debris deposition, thereby significantly enhancing surface integrity. The adoption of this optimized parameter set not only ensures superior machining efficiency but also results in improved surface quality. Full article

Wire electrical discharge machining (WEDM) enables the production of complex parts with tight tolerances, although maintaining dimensional accuracy in corners and tapers remains challenging due to wire deflection and vibration. This study optimizes WEDM parameters for achieving high accuracy in machining complex geometrical parts and taper cuts in 6061 aluminum alloy using an Excetek W350G WEDM machine with a copper wire electrode. Parameters including wire tension, pulse on-time, pulse off-time, wire feed rate, open circuit voltage, and flushing pressure were varied using a L18 Taguchi orthogonal array and the response graph method to identify optimal cutting conditions. Experimental results indicated that feature-specific optimization is crucial, as different geometrical features (rectangular fins, triangular fins, gears) exhibited varying critical parameters. Key findings highlighted the importance of wire tension and pulse on-time in maintaining cutting accuracy, although at varying levels for specific features. Response graphs demonstrated the effects of major WEDM parameters on corner and profile accuracies, whereas Taguchi analysis provided the optimum settings of parameters for each feature and taper cutting. These findings will help enhance precision, efficiency, and versatility of the WEDM process in machining complex profiles and corners, contributing to precision manufacturing. Full article

Since one of the effective methods for producing the form-cutting tools used in the form-turning process involves utilizing a wire cut machine, the effect of the geometric characteristics of the form contour on reducing the negative effects of the recast layer was investigated in this research. The basic assumption of the components for each type of profile form is based on a combination of four modes, i.e., concave arc, convex arc, flat surface, and oblique surface. Based on this, samples were fabricated as cutting tools with three different radii: a convex arc, a concave arc, and a flat surface. During the wire electrical discharge machining (WEDM) operation, one-pass mode was used to create a rough surface, two passes resulted in a semi-finished surface, and three passes resulted in a finished surface. Furthermore, the difference between the surface quality of the recast layer in the two areas above the workpiece or the wire entry point and the bottom area of the workpiece or the wire exit point was studied. Finally, the effect of the direction, size of the curvature and the number of passes in the electric discharge process of the wire on the recast layer was shown, and it was observed that with the increase in the number of passes in WEDM, the thickness of the recast layer was reduced, along with the uniformity of the cutting contour section in the areas close to the cutting region. The entry of the wire was greater than that in the areas near the exit of the wire. Full article

Current approaches in the process of evaluating the quality of the machined surface during wire electrical discharge machining (WEDM) generally do not include the assessment of micro- and macro-geometric indicators of both parts of the cut. In practice, however, there are specific cases when it is necessary to use both halves of the cut. In such cases, it is necessary to choose a special approach not only in the machining process but also when evaluating the quality indicators of the machined surface. Therefore, experimental measurements were aimed at the identification of these micro- and macro-geometrical indicators in symmetrical WEDM. Within them, qualitative indicators of flat and curved surfaces were assessed. The identification of individual characteristics was carried out using Suftes, Roundtest Mitutoyo, and a 3D coordinate measuring device. The design of the experiment followed the full DoE factorial design method, and the obtained results were processed using the Taguchi method. Based on the obtained results, the response of macro and micro-geometric parameters was characterized by means of multiple regression models (MRM) in symmetrically machined surfaces of tool steel EN X37CrMoV5-1 (Bohdan Bolzano, Kladno, ČR) by WEDM technology. They revealed the mutual dependence of the output qualitative indicators of the eroded area on the input variables’ main technological parameters (MTP). Subsequent multi-parameter optimization resulted in a suitable level of setting of the MTP input variable parameters I, t_on, U, and t_off (9 A, 32 μs, 15 μs, and 70 V), through which the greatest agreement of macro and micro-geometric output indicators of symmetrically machined surfaces can be achieved. By applying the optimized levels of MTP settings for symmetrical WEDM of tool steel EN X37CrMoV5-1, their agreement was achieved at the level of 95%. Full article

Machining thicker workpieces in the process of Wire Electrical Discharge Machining (WEDM) can result in a concave phenomenon known as a “drum shape error” due to the vibration of wires and accumulation of debris, which leads to secondary discharge in the middle of the workpiece. Reducing the drum shape error typically requires a longer finishing process. Finding a balance between precision and machining time efficiency has become a challenge for modern machining shops. This study employed experimental analysis to investigate the effect of individual parameters on the shape error and machining removal rate (MRR). Key influential parameters, including open voltage (OV), pulse ON time (ON), pulse OFF time (OFF), and servo voltage (SV), were chosen for data collection using full factorial and Taguchi orthogonal arrays. Regression analysis was conducted to establish multiple regression equations. These equations were used to develop optimization rules, and subsequently, a user-friendly human–machine interface was developed using C# based on these optimization rules to create a shape error and MRR optimization system. The system can predict the optimal parameter combinations to minimize the shape error and increase the MRR. The results of the verification experiments showed that the prediction accuracy can reach 94.7% for shape error and 99.2% for MRR. Additionally, the shape error can be minimized by up to 40%. Full article

This study aims to optimize the Wire Electrical Discharge Machining (EDM) process parameters for aluminum 6061 alloy reinforced with Mg and MoS₂ using the Box–Behnken (BBD) design and the non-dominated sorting genetic (NSGA-II) algorithm. The objective is to enhance the machining efficiency and quality of the composite material. The Box–Behnken (BBD) design was utilized to design a set of experiments with varying levels of process parameters, comprising pulse-on time, servo volt, and current. The material removal rate and surface roughness were considered as machining responses for optimization. These responses were measured and used to develop a mathematical model. The NSGA-II, a multi-objective optimization algorithm, was then applied to search for the optimal combination of process parameters that simultaneously maximizes the material removal rate and minimizes the electrode wear rate and surface roughness. The algorithm generated and evolved a set of Pareto-optimal solutions, providing a trade-off between conflicting objectives. The results of the optimization process were analyzed to identify the optimal process parameters that lead to improved machining performance. The study revealed optimal Wire Electrical Discharge Machining (WEDM) parameters for Al6061/Mg/MoS₂ composites using NSGA-II. The optimized parameters, including a pulse-on time (Ton) of 105 µs, servo voltage (SV) of 35 V, and peak current (PC) of 31 A, resulted in a Material Removal Rate (MRR) of 7.51 mm³/min and a surface roughness (SR) of 1.97 µm. This represents a 15% improvement in the MRR and a 20% reduction in the SR compared to non-optimized settings, demonstrating the efficiency of the BBD-NSGA-II approach. Full article

The integrity of the machined surface in precision wire electrical discharge machining (WEDM) of electrically conductive materials is one of the most important quality indicators. The integrity parameters of the machined surface are primarily monitored in terms of micro and macro geometry parameters. This paper presents the results obtained as a part of experimental research aimed at evaluating surface crack density (SCD) when machining EN HS6-5-2C using WEDM technology. The aim was to find a combination of main technological parameters (MTP) in order to minimize the qualitative indicators SCD and Ra of the eroded surface. The results of experimental research within the framework of the evaluation of SCD and Ra indicators were processed using the Taguchi method. The integrity of the eroded surface was examined by scanning digital microscope (SDM) after application of full and multiple offset cuts with an AC Brass LP 1000 brass wire electrode. Based on the experimental measurements performed, significant facts were discovered. It was found that the largest surface integrity defects are present after the application of full cuts and the first two offset cuts. At the same time, it was found that lower values of the SCD parameter in WEDM of EN HS6-5-2C steel were recorded at thicknesses above 130.0 mm. The SCD parameter was also confronted with the Ra parameter, and it was found that they are significantly influenced by MTP. The higher value of the peak current I (19 A) and the longer duration of the discharge t_on (32 μs) result in an increase in the value of the SCD parameter from 0.005 μm·μm⁻² to 0.0256 μm·μm⁻². The resulting solutions are mathematical regression models (MRM), which allow the prediction of both monitored qualitative indicators with respect to their minimization. Full article

SiC particle reinforced aluminum matrix composites (SiCp/Al) are widely used in aviation, weaponry, and automobiles because of their excellent service performance. Wire electrical discharge machining (WEDM) regardless of workpiece hardness has become an alternative method for processing SiCp/Al composites. In this paper, the temperature distribution and the discharge crater size of the SiCp/Al composite are simulated by a thermophysical model during a single-pulse discharge process (SPDP) based on the random distribution of SiC particles. The material removal mechanism of the SiCp/Al composite during the multi-pulse discharge process (MPDP) is revealed, and the surface roughness (Ra) of the SiCp/Al composite is predicted during the MPDP. The thermophysical model simulation results during the MPDP and experimental characterization data indicate that the removal mechanism of SiCp/Al composite material consists of the melting and vaporization of the aluminum matrix, as well as the heat decomposition and shedding of silicon carbide particles. Pulse-on time (T_on), pulse-off time (T_off), and servo voltage (SV) have a great influence on surface roughness. The Ra increases with an increase in T_on and SV, but decreases slightly with an increase in T_off. Moreover, compared with experimental data, the relative error of Ra calculated from the thermophysical model is 0.47–7.54%. This means that the developed thermophysical model has a good application and promotion value for the WEDM of metal matrix composite material. Full article

Wire Electrical Discharge Machining (WEDM) technology represents an unconventional but vital manufacturing technology in many different industrial branches. The automotive industry and its many significant requirements bring the need to manufacture inserts and mould segments for plastic injections from Albromet W130 material, with a required roughness, Ra, from 4.5 to 5 µm so that subsequent profile etching can be eliminated. A planned experiment of 60 rounds was carried out to discover the optimal machining parameters, namely, the pulse-off time, gap voltage, discharge current, pulse-on time, and wire speed in order for the thickness of 10 to 100 mm (after 10 mm) to demonstrate the required roughness. The goal was to evaluate the surface roughness, maximise the cutting speed, and manufacture it without surface or subsurface defects. The evaluation of the planned experiment led to the establishment of optimised WEDM machining parameters with which thicknesses of 10–100 mm will always be produced with the required roughness, Ra, from 4.5 to 5 µm and with the highest possible cutting speed. It was also proven that the machining does not lead to surface or subsurface defects, and thus, the service life of the manufactured parts will not be affected. Full article

Wire Electric Discharge Machining (WEDM) is an unconventional machining technology that uses electrical impulses to generate very high temperatures to cut material. The WEDM process hence causes some unfortunate defects, such as cracks and burnt cavities, which can impact the correct functionality of the machined pieces and shorten their service life. This study was carried out to understand which materials remain defect-free after WEDM. The examined materials were the Ampcoloy 35 copper alloy, the high-entropy steels FeCoCrMnNi and FeCoCrMnNiC_0.2, and the B1914 and Nimonic 263 nickel alloys. The influence of the machining parameters, namely the pulse off time, gap voltage, discharge current, pulse on time, and wire feed, on the cutting speed and the surface topography of the machined piece was investigated. The surface morphology, the state of the subsurface layer in a cross-section, and the number of diffused elements from the wire electrode were analysed. All the analysed materials were found completely suitable for WEDM machining as they do not form any surface or subsurface defects. Full article

The Wire Electrical Discharge Machining (WEDM) process is an accurate method for manufacturing high-added-value components for industry. Continuous developments in the process have resulted in specialized machines used in sectors such as aerospace and biomedical engineering. However, some fundamental aspects of the discharge process remain unresolved. This work aims to study the influence of discharge location and bubble expansion on the occurrence of subsequent discharges. A high-speed video camera observation system was constructed to capture images of each discharge. From the acquired images, an algorithm was devised to determine the discharge location based on grayscale analysis. Moreover, the voltage and current waveforms of the discharges and the framing signals of the high-speed video camera were then obtained using an oscilloscope. Synchronizing the observation images and signals allowed for calculating the delay time for each single discharge. The results indicate that most of the discharges occurred near the boundary of the bubble and during bubble expansion. This finding has been observed for a variety of machining conditions and can be explained by the effect of the debris particles concentrated at the bubble boundary. This study provides useful information for better understanding the discharge process in WEDM. Full article