Wire Electrical Discharge Machining Process Research Articles

Automatic Characterization of WEDM Single Craters Through AI Based Object Detection

Wire electrical discharge machining (WEDM) is a process that removes material from conductive workpieces by using sequential electrical discharges. The morphology of the craters formed by these discharges is influenced by various process parameters and affects the quality and efficiency of the machining. To understand and optimize the WEDM process, it is essential to identify and characterize single craters from microscopy images. However, manual labeling of craters is tedious and prone to errors. This paper presents a novel approach to detect and segment single craters using state-of-the-art computer vision techniques. The YOLOv8 model, a convolutional neural network-based object detection technique, is fine-tuned on a custom dataset of WEDM craters to locate and enclose them with tight bounding boxes. The segment anything model, a vision transformer-based instance segmentation technique, is applied to the cropped images of individual craters to delineate their shape and size. Geometric analysis of the segmented craters reveals significant variations in their contour and area depending on the energy setting, while the wire diameter has minimal influence.

Investigation of machining rate and surface roughness in wire EDM of Al6063/WC/ZrO2 composite using response surface methodology

The aim of the current work is to explore the machinability behavior of Al6063/WC/ZrO2 metal matrix composites (MMC) produced through a stir casting process through wire electrical discharge machining (WEDM) process. In order to examine the effects of process parameters such as voltage (V), pulse on time (Ton) and pulse off time (Toff) on material removal rate (MRR) and surface roughness (SR), the experiments were conducted by adapting Response Surface Methodology (RSM) in conjunction with the central composite design (CCD). A second-order regression model has been developed to predict the response parameters and an analysis of variance (ANOVA) was performed to validate the significance of the models. Using the desirability function approach, the parameters were set for the highest MRR and the minimum SR value. The prediction was within a tolerable average error range. A total of 19 sets of tests were developed to obtain six possible solutions. The most suitable solution among these six has been found by a confirmatory experiment. The results indicate that voltage and its interactions have significantly affected both the responses than Ton and Toff. Overall, it can be said that the study of the Al6063/WC/ZrO2 MMCs using WEDM process parameters demonstrated improved casting and machining qualities.

Influence of generator parameters on cutting width during WEDM process

The primary objective of this paper is to rigorously analyse the influence of selected parameters on the cut width in the context of Wire Electric Discharge Machining (WEDM). The parameters under investigation, each varied at different levels, include pulse width, servo reference mean voltage, time interval between two pulses, discharge frequency, and wire feed speed. As the workpiece material, sintered carbide K10 was used. Data for the response variable, cut width, was meticulously collected using two high-precision instruments: the Zoller Genius 3s and the Alicona InfiniteFocusSL. Among the parameters studied, the mean reference voltage emerged as the most influential variable. It holds a commanding lead with a percentage contribution of as much as 82.4 %, making it nearly 15 times more impactful than the second most influential parameter, which is pulse width. At its lowest setting, or first level, the mean reference voltage results in a record low cut width. Conversely, at its highest setting, or third level, it leads to a record high cut width. This comprehensive analysis not only quantifies the relative importance of each parameter but also provides valuable insights into the optimal settings for achieving desired cut widths. The findings have significant implications for improving the efficiency and precision of WEDM processes.

Pool boiling heat transfer performance of micro-embossing molds for the fabrication of polymer wicks

As flexible heat dissipation devices are urgently needed for flexible electronics, polymers with the advantages of good flexibility, insulation, and low density have attracted wide attention. It would be interesting to fabricate thermal functional structures on polymers to overcome their low thermal conductivity. This study aims to design and fabricate a micro-embossing mold to process polymer three-dimensional microgroove wicks (3D-MWs), which are applied in flexible heat pipes and achieve high thermal conductivity. Three kinds of tungsten steel molds were processed by using two wire electrical discharge machining processes (WEDM), two electrical discharge shaping machining processes (EDSM), and a combination of WEDM and EDSM. The 3D-MWs of the tungsten steel mold were reprinted onto the polymer films by a two-step embossing process. To investigate the effects of microstructures on boiling heat transfer performances, tungsten steel molds fabricated by different methods were tested to reflect the heat dissipation potential of the polymer 3D-MWs. The results showed that the 3D-MWs fabricated by the combination of WEDM and EDSM exhibit good boiling heat transfer performance and high processing efficiency, with a critical heat flux of 1040.1 kW/m2 and a maximum heat transfer coefficient of 38.4 kW/m2·°C. These values are 46% and 611% higher than those of the smooth tungsten steel surface, respectively, making it a superior choice for micro-embossing molds.

An experimental investigation of F53 super duplex stainless steel using wire cut EDM process

This research focuses on the machinability of F53 super duplex stainless steel using the Wire Electrical Discharge Machining (WEDM) process. F53 Super Duplex Stainless Steel is known for its exceptional corrosion resistance and mechanical properties. It is a popular choice for critical applications in various industries, such as offshore, chemical, and petrochemical. The wire EDM process is chosen for its ability to achieve precise and intricate cuts in hard-to-machine materials like super duplex stainless steel. This study considered important process variables like wire feed rate, servo voltage, pulse-on and pulse-off time, and material removal rate and surface roughness as output responses. Box Behnken method is used to analyse all possible interactions between input and output variables are taken into account, and three different levels of parameter conditions are adopted. It was found that MRR increased by 51.30 % with a gradual increase in pulse on time and applied wire feed and SR decreased by 62.86 % gradually with increasing servo voltage and pulse off time. The optimized values are found to be Ton = 11 µs, WF = 13 m/min, SV = 40 V and Toff = 8 µs. The microhardness study confirms the formation of the melted layer on the machined surface. This research contributes to a deeper understanding of the wire EDM process's applicability to the challenging material properties of F53 super duplex stainless steel, offering insights for the manufacturing and engineering industries aiming to improve the efficiency and precision of machining operations for this advanced material.

Process parameters optimization and performance analysis of micro-complex geometry machining on Ti6Al4V

The aeronautical industry relies on high-performance equipment that demands materials with exceptional engineering attributes, such as the Ti6Al4V alloy. However, the complexity of the parts used in these applications presents challenges related to stack-up tolerances during assembly. Therefore, conventional machining processes are not economically viable due to their two-step nature solution (machining and surface treatment) and their inability to achieve the required accuracy level, particularly given the alloy’s hard-to-cut nature. In this matter, wire electric discharge machining (WEDM) emerges as an attractive alternative for fabricating complex geometries. This research focuses on evaluating the potential of the WEDM process for machining micro-complex profiles. A comprehensive range of process parameters, including servo voltage (Vs), pulse on time (Ton), pulse off time (Toff), and wire speed (Ws), are studied using a Taguchi-based design of experiments. The results are analyzed through parametric significance analysis, parametric control analysis, surface morphological analysis using scanning electron microscopy, and modified layer analysis. Additionally, both mono-objective and multi-objective process optimization techniques are employed to achieve superior accuracy and speed. The findings indicate that Ton and Ws have the most significant influence on both cutting speed and spark gap, whereas Vs and Toff play a crucial role in determining the accuracy index. In addition, adequate flushing, reduced wire speed (economically viable), and stability of the spark are recommended to attain a lower spark gap and higher accuracy. The confirmatory experiments show that the optimal parametric conditions of Vs = 60 V, Toff = 30 μs, Ton = 8 μs, and Ws = 6 mm/s provide the highest speed of 3.4 mm/min, minimum spark gap of 0.344 mm, and accuracy index of 98.72%. The findings will contribute to enhancing manufacturing efficiency, precision, and cost-effectiveness in the aeronautical industry, meeting the demand for high-quality components with tight tolerances.

Comprehensive review on wire electrical discharge machining: a non-traditional material removal process

A highly advanced thermo-electric machining technique called wire electrical discharge machining (WEDM) can effectively produce parts with varying hardness or complicated designs that have sharp edges and are very difficult to machine using standard machining procedures. This useful technology for the WEDM operation depends on the typical EDM sparking phenomena and makes use of the commonly used non-contact material removal approach. Since its inception, WEDM has developed from a simple approach for creating tools and grown to an outstanding option for creating micro-scale components having the greatest degree of dimensional precision and surface finish characteristics. The WEDM method has endured over time as an efficient and affordable machining alternative that can meet the stringent operating specifications enforced by rapid manufacturing cycles and increasing expense demands. The possibility of wire damage and bent, nevertheless, has severely hindered the process’ maximum potential and decreased the precision as well as effectiveness of the WEDM process. The article examines the wide range of investigations that have been done; from the WEDM through the EDM process’ spin-offs. It describes WEDM investigation that required variables optimization and an assessment of the many influences on machining efficiency and accuracy. Additionally, the research emphasizes adaptive monitoring and control of the process while examining the viability of multiple approaches to control for achieving the ideal machining parameters. Numerous industrial WEDM applications are described with the advancement of hybrid machining techniques. The paper’s conclusion examines these advancements and identifies potential directions for subsequent WEDM research. The investigation on WEDM of metal matrix composites (MMCs) is also reviewed; along with the impacts of various cutting variables like wire feed rate (F), voltage (V), wire tension (WT), and dielectric flow rate on cutting processes outcomes like material removal rate (MRR), kerf width (Kw) and surface roughness (SR). In the present article, future directions for WEDM research were also suggested.

Prediction of surface roughness and depth of cut in abrasive waterjet milling of alumina ceramic using Machine learning algorithms

In abrasive waterjet (AWJ) milling process, the ability to accurately predict outcomes such as surface roughness (Ra) and depth of cut (DoC) holds immense significance for cost reduction, resource optimization, and minimizing material wastage. However, a major challenge lies in the limited availability of AWJ milling datasets, hindering the development of robust machine learning models. To address the predicament of outcome, the present study employs a bootstrapping data augmentation technique to expand small AWJ milling datasets. Augmented data is used to train machine learning models, and hyperparameter fine-tuning is conducted using grid search and 5-fold cross-validation.In the prediction of surface roughness (Ra) for AWJ milling of alumina ceramic, the study finds that a Multi-Layer Perceptron (MLP) Regression model with a 4–100-100–1 architecture, 'relu' activation function, 'adam' solver, alpha value of 0.0001, and 'adaptive' learning rate outperforms the decision tree and random forest regression models. In the case of prediction of AWJ milling depth of cut (DoC) in alumina ceramic, the random forest regression model surpasses MLP and decision tree regression models. It employs hyperparameters including 100 estimators, 'gini' criterion, 'log2′ for maximum features, a maximum depth of 50, a minimum sample split of 10, a minimum sample leaf of 2, bootstrap as ‘True’, and a minimum impurity decrease of 0.The methodology followed to develop the machine learning models in this study is also tested in wire electrical discharge machining (WEDM) process in the machining aspects of metal matrix composites (MMC) for the prediction of surface roughness (Ra). The experimental dataset was adopted from (Satishkumar et al., 2011). In case of prediction of surface roughness (Ra) in WEDM, the random forest regression model outperforms MLP and decision tree regression models. These developed machine learning models hold the potential to greatly assist manufacturing engineers in selecting optimal input parameters for AWJ milling and WEDM, thereby reducing the wastage of machine resources.

Investigation of Al-7075/Al2O3/SiC by swirl casting and WEDM

The research, engineering, military, automotive, sports, and telecommunications industries are just a few of the many that make heavy use of aluminium metal matrix composites. The unique synergistic qualities of these composites, such as reduced weight density, improved performance, and thermal conductivity, make them highly desired. Because of its reputation for small but noticeable structural improvements, Al-7075 aluminium has seen extensive use in the aerospace industry. This research followed international criteria for evaluating specimens in terms of positional stiffness, deformation, impact resistance, and wear. It used the stir casting process to add SiC/Al2O3 to Al-7075 in percentages ranging from 2% to 8%. Hybrid Al-HMCs are the product of an aluminium metal matrix composite that was able to include 8% SiC/Al2O3via the stirring process. Because it combined aluminium oxide with silicon carbide, this Al-HMC showed improved mechanical qualities and stiffness. Aluminium metal composites with increasing amounts of Al2O3/ SiC showed reduced fatigue and frictional strengths. The effect on hardness remained small even when the MMC's SiC/Al2O3 proportions grew significantly. To further investigate the Wire Electrical Discharge Machining (WEDM) process, researchers used the L9 Arrays Design of Experiments (DOE) technique to incorporate new process variables. Our objective was to enhance the rate of material removal from Al-7075/SiC/Al2O3 while simultaneously improving the marginal surface roughness. Finding the best WEDM process parameters, including the ideal percentages of SiC/Al2O3, to achieve the desired surface roughness while minimising material removal rate was the goal of this investigation. To produce Al- 7075/SiC/Al2O3 composite materials with varying additive percentages in a production environment, this study will determine the best and most accurate WEDM process variables to use.

EXPERIMENTAL INVESTIGATION AND OPTIMIZATION OF WIRE EDM PROCESS TO CREATE DIFFERENT GEOMETRICAL PROFILES IN METAL MATRIX COMPOSITES

Machining of accurate geometrical profiles like triangles, squares or circles with good surface finish and high productivity in metal matrix composites is required for various industrial applications. Thermoelectic principle-based wire electric discharge machine (WEDM) can be conventionally used to overcome the difficulty which occurs during the machining of composites due to the presence of hard abrasive particles. Therefore, in this investigation, three geometrical profiles possessing equal perimeters were produced in 5 mm thick aluminum metal matrix composites (AMMCs) by WEDM. In order to analyze the effect of reinforcements three different AMMCs containing Al6061 as a matrix were produced by stir casting method. The fabricated composites, respectively, contain 10% alumina ([Formula: see text]), 10% silicon carbide (SiC) and a mixture of 5% [Formula: see text] with 5% SiC. The individual effect of various input parameters like pulse-on time ([Formula: see text] = 30-50 [Formula: see text]s), pulse-off time ([Formula: see text] = 6–12 [Formula: see text]s), current ([Formula: see text] = 1–5 A) and geometrical shapes (circle, triangle, and square) on cutting velocity (CV) and surface roughness (SR) were investigated. Furthermore, parametric analysis using regression models was also performed to evaluate the simultaneous effect of two interacting parameters on CV and SR. The study reveals that in the case of hybrid composite, CV decreases with [Formula: see text] and SR is always higher for triangular profiles when [Formula: see text] and [Formula: see text] change. The regression plots indicate that the interaction effect of [Formula: see text] × [Formula: see text] plays a major role in CV for all three composites. Multiobjective optimization using a composite desirability approach reveals a marginal increase in CV (8.58%) and a significant reduction (57.72%) in SR at optimal input parameters for Al6061–10% SiC. The morphological analysis at optimal input parameters indicates a significant reduction in resolidified metal drops and microcraters.

Laser polishing and simultaneous hardening of the electrical discharge machined Zr-based metallic glass surface

Wire electrical discharge machining (WEDM) is highly suitable for processing the hard-brittle materials like metallic glasses (MGs). However, the electro-thermal energy conversion involved in the WEDM process inevitably introduces some surface defects including bulges, discharge craters, and recast materials on the WEDMed MG surface, which significantly impedes its practical application. Here, an emerging automated polishing technology, laser polishing (LP), was performed in the nitrogen-rich environment to simultaneously improve the surface quality and hardness of the WEDMed Zr-based MG surface. The detailed effects of LP parameters on the surface roughness and morphology were systematically investigated. The morphology evolution and temperature distribution of the molten pool during LP were analyzed by simulation analysis. The experimental results indicated that after single LP of four representative WEDMed Zr-based MG surfaces using the optimized laser parameters, the surface roughness (Sa) was remarkably reduced from 1.27, 2.10, 3.49, and 4.58 μm to 0.09, 0.14, 0.17, and 0.21 μm, respectively, accompanied by an increase in surface hardness from 6.30 GPa to 7.39, 7.67, 8.51 and 8.99 GPa. This study demonstrated that LP could effectively achieve simultaneous enhancement of the quality and hardness of WEDMed MG surfaces, which would contribute to expanding the application prospects of MGs.

Wire Electric Discharge Machining of Aluminium Hybrid Composite: Renewable Energy Based IoT Approach

Wire Electric Discharge Machining (WEDM) has been recognized as one of the optimum methods for machining of harder aluminum-based hybrid metal matrix composites (AHMMC). This method is used to optimize the major control aspects of a machine and they are current, pulse duration, and rate of feed of wire on kerf width (KRW) and Surface roughness (Ra) of hybrid composites made of aluminum Al6351 as the metal matrix (AMMHC). The AMMHC has been created via a stir casting technique by adding SiC and B4C with an Al6351 matrix. Box-Behnken design (BBD) has been used to conduct tests in order to parametrically optimize the WEDM process. The optimization of KRW and Ra is identified using 3-D surface plots, graphs and response table of ANOVA as well as by employing Response Surface Methodology (RSM). Internet of Things (IoT) is implemented to monitor the quality of electrolyte that is used in WEDM. It has been identified that the current has a major contribution in both KRW and Ra factors. A lower current is preferred for a lower KRW whereas higher current improves Ra value.

THE COMPARATIVE EFFECT OF WIRE ELECTRODES ON WIRE BREAKAGE FREQUENCY AND CUTTING RATE IN WEDM OF INCONEL 706

Non-traditional machining methods like wire electric discharge machining (WEDM) appear to be an ideal choice for machining high strength super alloys like Inconel 706, because of their capability to generate intricate profiles with high accuracy. However, wire breakages are a common problem in the WEDM process and adversely affect the productivity, accuracy, and surface quality. Therefore, the aim of this work is to investigate and comprehend the phenomenon of wire breakage using Inconel 706 as work material and brass wire, diffused brass wire, and zinc-coated brass wire as electrode materials. Six process parameters—pulse-on time, pulse-off time, peak current, spark gap voltage, wire feed rate, and wire tension—have been varied experimentally to determine their effect on the frequency of wire failure. According to the findings, the zinc-coated brass wire breaks with the lowest frequency when compared to the other two. Additionally, an ideal range of input variables has been proposed for efficient WEDM of Inconel 706 without unexpected wire breakages. This range can also be utilized for further research work related to modeling and optimization in WEDM of Inconel 706.

Wire Electrical Discharge Machining of AISI304 and AISI316 Alloys: A Comparative Assessment of Machining Responses, Empirical Modeling and Multi-Objective Optimization

This research investigates the multi-response of both material removal rate (MRR) and surface roughness (Ra) for the wire electrical discharge machining (WEDM) of two stainless steel alloys: AISI 304 and AISI 316. Experimental results are utilized to compare the machining responses obtained for AISI 316 with those obtained for AISI 304, as previously reported in the literature. The experimental work is conducted through a full factorial experimental design of five running parameters with different levels: applied voltage, transverse feed, pulse-on/pulse-off times and current intensity. The machined workpieces are analyzed using an image processing technique in order to evaluate the size of cut slots to allow the calculation of the MRR. Followed by the characterization of the surface roughness along the side walls of the slots. Different mathematical regression techniques were developed to represent the multi-response of both materials using the MATLAB regression toolbox. It was found that WEDM process parameters have a fuzzy influence on the responses of both material models. This allowed for multi-objective optimization of the regression models using four different techniques: multi-objective genetic algorithm (MOGA), multi-objective pareto search algorithm (MOPSA), weighted value grey wolf optimizer (WVGWO) and osprey optimization algorithm (OOA). The optimization results reveal that the optimal WEDM parameters of each response are inconsistent to the others. Hence, the optimal results are considered a compromise between the best results of different responses. Noteworthily, the multi-objective pareto search algorithm outperformed the other candidates. Eventually, the optimal results of both materials share the high voltage, high transverse feed rate and low pulse-off time parameters; however, AISI 304 requires low pulse-on time and current intensity levels while AISI 316 optimal results entail higher pulse-on time and current levels.

Optimisation of Electrical Discharge Machining Processing for AZ91 Magnesium Alloy using Coupled AHP-Taguchi Analyses-GA Method with the Rank Selection Approach

Despite being contemporary, the wire electrical discharge machining (EDM) industry is burdened with complicated and challenging problems. However, the double optimisation method involving Taguchi analyses and genetic algorithms is a powerful tool to help tackle some of these problems. This article evaluates the wire EDM process through a rank-based genetic algorithm coupled with the AHP-Taguchi analyses using the AZ91 magnesium alloy for the first time in the literature. The rank selection method was used at the selection stage of the operations. Six parameters, namely pulse on time, pulse off time, wire feed, wire tension, pulse current and gap voltage, were the process parameters. For all the methods, the total values were computed and compared for the selection, cross-over and mutation operations. It was found that the total values at the selection stage for each of the methods, namely AHP-Taguchi-GA, AHP-Taguchi-Pareto-GA and AHP-Taguchi-ABC-GA methods, were 2750, 4176 and 6306 (best value as Part A), respectively. For all the methods, there was a 25.35% improvement in total value at the cross-over stage compared with the selection stage. The improvement in the total values of the mutation over cross-over and mutation over selection was 53.84% and 92.84%, respectively. These improvement values were for the AHP-Taguchi-GA method but also turned out to be the same for the AHP-Taguchi-Pareto-GA and AHP-Taguchi-ABC methods. The principal advantage of the rank selection method introduced in the present study is to avoid quick convergence. This article is beneficial to the process engineers aimed at improving the wire electrical discharge machining process.

Experimental Study of the Surface Quality of Form-Cutting Tools Manufactured via Wire Electrical Discharge Machining Using Different Process Parameters.

Form-cutting tools are an economical choice for turning parts with defined profiles in mass production. The effects of the form contour of these tools-produced by the wire electrical discharge machining (WEDM) process-on tool quality were investigated in this research. This study focuses on reducing the adverse effects of the recast layer induced by WEDM on form-cutting tools. The basic component types of profile forms in form-cutting tools can be summarized by a combination of four modes, i.e., concave and convex arcs and flat and oblique surfaces. Hence, sample cutting tools with three different radii of convex and concave arcs and a flat surface were produced. During the WEDM operation, one-pass mode was used for roughing, two passes for semi-finishing, and three passes for finishing. Furthermore, the difference between the percentage of oxygen and carbon elements on the recast layer in the two areas above the workpiece or wire entry point and the bottom area of the workpiece or wire exit point was investigated. Finally, the influences of the direction, size of the curvature, and the number of passes in the wire electric discharge process on the recast layer were analyzed. It was observed that the recast layer thickness could be reduced by increasing the number of WEDM process. Additionally, the uniformity of the cutting contour was superior in the entry region of the wire going into the workpiece compared with the exit region of the wire.

Experimental Investigation of the Effects of Machining Parameters on the Performance of Form-Cutting Tools Manufactured by Wire Electrical Discharge Machining (WEDM) and Grinding Processes.

In this research, a comparison between two methods of grinding and WEDM and the chip formation of each form tool was studied through a set of designs of experiments. A multi-functional form tool with different cutting-edge shapes was designed to compare different production methods, and a grinding machine and a five-axis wire-cutting machine were made. The form tools by the wire cutting method were made with three different machining states, rough, semi-finish, and finish. The results of the experimental test showed that the chip formation of the finished surface of the wire cut tool was close to the ground tools. Additionally, the tool life in wear generation was assessed, revealing that the tool generated through the wire-cutting method with three passes exhibited superior performance compared to alternative approaches. Furthermore, employing the wire-cutting technique with high surface finishing yielded optimal outcomes for producing form-cutting tools featuring complex profiles.

MOGA and TOPSIS-based multi-objective optimization of wire EDM process parameters for Ni50.3-Ti29.7-Hf20 alloy

Conventional machining techniques face challenges in processing Ni-Ti-Hf alloys, which exhibit superior properties and are increasingly considered promising materials for high-temperature shape memory actuator applications. Thus, this article focuses on investigating the effect of Wire Electric Discharge Machining (WEDM) input parameters, namely discharge time (TON), pause time (TOFF), gap voltage (SV), and wire travel speed (WF), on the surface quality and shape memory properties of these alloys. These parameters were optimized to obtain a better removal rate (MRR) and surface finish quality (Ra) by employing a hybrid approach of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Objective Genetic Algorithm (MOGA). TON emerged as the most influencing parameter for both MRR and Ra, and the sample machined using optimal parameter setting, which had a MRR of 5.287 mm3/min and Ra of 2.335 µm, showed better surface quality with fewer surface defects and irregularities, lower recast layer thickness of 10.057 µm, and better shape memory properties with less than 15 % deviation in their latent heat of transformation values and a less than 5ºC change in their austenite and martensite transformation temperature values, which indicates MOGA was successful in finding a trade-off between the two responses.

A fuzzy TOPSIS method for surface integrity criteria ranking using the wire electrical discharge machining process

The objective of this study is to propose a method called the fuzzy technique for order preference by similarity to the ideal solution (F-TOPSIS) to select parameters of the wire electrical discharge machining (WEDM) process. Consequently, the parameters principally influencing the outputs of the WEDM process were identified and examined using the F-TOPSIS framework where the inputs of three decision makers, representing their opinions are incorporated into the analysis. The idea of parametric selection in a WEDM process is multicriteria-based when the production of nitinol-60 smart memory alloy (SMA) is critically considered. Current approaches of TOPSIS and analytic hierarchy process (AHP) evaluations to select the WEDM parameters fail because of the constraints of linguistic evaluations, but the use of F-TOPSIS crosses over the restriction to choose the best parameters in a WEDM process for nitinol-60 SMA. In this work, the experimental results obtained from published research were utilised to validate the proposed method. The validation of the suitability of F-TOPSIS, aided by the published work on the WEDM process, analysed the surface integrity of nitinol-60 SMA. From the five outputs, the closeness coefficients of the best and worst are found to be 0.7567 and 0.2838, respectively. This research aids the process engineer in optimising the outputs in the WEDM process, to select the best output. Hence, the research showcased how the WEDM process could select outputs efficiently, thus aiding process engineers to maintain the process to optimise parametric resource allocations and guarantee utmost productivity.

Ignition Delay Time Modeling in Wire-EDM

This study presents a comprehensive investigation and modeling of the ignition delay time (td) in wire-EDM (WEDM). The research focuses on the influence of gap distance, discharge energy, and piece height on the stochastic distributions of td, providing important insights into the complex properties of these distributions. Observations indicate that these parameters exert significant yet intricate influences on td, with a particular emphasis on the gap distance. A critical value was identified, around 8μm to 10μm, that divides the stochastic behavior. To capture the binomial nature of td, a mixture probability model consisting of two Weibull distribution curves was developed and validated through extensive experimentation and a data analysis. The model demonstrated strong agreement with observed cumulative probability curves, indicating its accuracy and reliability in predicting td. Further, a sensitivity analysis revealed regions of fast change, emphasizing the challenges and importance of careful parameter selection in control of WEDM processes. The findings of this study contribute to a deeper understanding of WEDM processes and provide a modeling approach for predicting td. Future research directions include refining the model by incorporating additional input parameters, investigating the influence of other process variables on td.