Perspective and Prospects of Wire Electric Discharge Machining (WEDM)

Wire Electric Discharge Machining (WEDM) is a non-traditional machining method that is widely used in the manufacture of aerospace/aircraft and medical equipment for conductive materials. WEDM products are expected to have good dimensional accuracy, surface roughness, and geometry. Many researchers have done experiments on various materials to optimize the process, which has many parameters and response characteristics. This paper provides an overview of the WEDM process on alloy steels in order to understand the impact of input process variables on output responses and optimization techniques for selecting optimal process parameters. This paper also highlights WEDM process trends as well as workpiece materials, wire varieties, wire diameters, and optimization approaches. This work is expected to be useful in initiating further research on WEDM by documenting substantial research works confirming the latest scenario

Perspective and Prospects of Wire Electric Discharge Machining (WEDM)

Wire Electric Discharge Machining (WEDM) is a non-traditional machining method that is widely used in the manufacture of aerospace/aircraft and medical equipment for conductive materials. WEDM products are expected to have good dimensional accuracy, surface roughness, and geometry. Many researchers have done experiments on various materials to optimize the process, which has many parameters and response characteristics. This paper provides an overview of the WEDM process on alloy steels in order to understand the impact of input process variables on output responses and optimization techniques for selecting optimal process parameters. This paper also highlights WEDM process trends as well as workpiece materials, wire varieties, wire diameters, and optimization approaches. This work is expected to be useful in initiating further research on WEDM by documenting substantial research works confirming the latest scenario

Optimizing The Machining Process of IS 2062 E250 Steel Plates with The Boring Operation Using a Hybrid Taguchi-Pareto Box Behnken-teaching Learning-based Algorithm

In this article, a new method termed the Taguchi-Pareto-Box Behnken design teaching learning-based optimization (TPBBD–TLBO) was developed to optimize the boring process, which promotes surface roughness as the output. At the same time, the speed, feed, and depth of cut are taken as the inputs. The case examines experimental data from the literature on the boring of IS 2062 E250 steel plates. The proposed method draws from a recent idea on the Taguchi-Pareto-Box Behnken design method that argues for a possible relationship between the Taguchi-Pareto method and the Box Behnken design method. This idea was used as a basis for the further argument that teaching learning-based optimization has a role in the further optimization of the established TPBBD method. The optimal solutions were investigated when the objective function was generated using the Box Behnken design in a case. It was replaced with the regression method in the other case, and the python programming codes were used to execute the computations. Then the optimal solutions concerning the parameters of speed, feed rate, depth of cut, and nose radius were evaluated. With the Box Behnken as the objective function for the TLBO method, convergence was reached at 50 iterations with a class population of 5. The optimal parametric solutions are 800 rpm of speed, 0.06 min/min of feed rate, 1 min for depth of cut, and 0 min for nose radius. On the use of the regression method for the objective function, while the TLBO method was deployed, convergence was experienced after 50 iterations with a class population of 200 students. The optimal parametric solution is 1135rpm of speed, 0.06 min/min of feed rate, 1024 min of the depth of cut, and 0.61 min of nose radius. The speed, depth of cut, and nose radius showed higher values, indicating the use of more energy resources to accomplish the optimal goals using the regression method-based objective function. Therefore, the proposed method constitutes a promising route to optimize further the results of the Taguchi-Pareto-Box Behnken design for boring operation improvement

Multi- Criteria Optimization of PMEDM Process Parameters for MRR, SR and TWR Using TOPSIS Method

Multi-objective optimization in PMEDM remains a very complex problem, so it continues to cause the attention of many research. In this paper, the authors presented the results of each specific optimization and simultaneous 4 quality characteristics of electrical discharge machining using titanium powder mixed in the dielectric fluid (PMEDM). The methods is used to optimize the Taguchi method and TOPSIS. The process parameters are used to investigates: workpiece material, tool material, polarity, pulse-on time, intensity of discharge, pulse-off time, powder concentration. This approach proved successful method for improving the processing efficiency of the study subjects

Particle Swarm Optimisation of Hole Quality Characteristics in Laser Trepan Drilling of Inconel 718

Inconel-718 is a nickel based super alloy and is extensively in use for working at very high temperature (upto 2000 °C) such as aero engine gas path equipment, nuclear equipment etc. Drilling micro size hole in such material with laser beam has been a proven choice and laser drilling process produces geometrically and dimensionally improved hole. Hole geometrical features can be improved further if laser drilling system operated at optimal input parameter setting. This paper experimentally investigates the behavior of hole geometrical features hole circularity and hole taper in laser trepan drilling of Inconel -718 sheet. Optimal value of laser input parameters for improved hole circularity and reduced hole taper have been suggested with the help of computational intelligence technique particle swarm optimisation. The effect of each laser input parameter on hole quality characteristics are also discussed and demonstrated graphically. Finally the experimental validation of the predicted results has been carried out

Machining of Stainless Steels and Alloys Using Non-Traditional Machining Processes

Stainless steels and alloys are characterized primarily by their corrosion resistance, high strength, ductility, etc. used for various advanced applications like automotive and aerospace, sugar refineries, construction materials, etc. Many advanced high-speed machineries /systems need fine quality of parts to provide good performance in its working conditions. The machining of stainless steel and its alloys is of interest, because, of its excellent mechanical properties. Stainless steels and alloys are machined generally by traditional machining processes. But complex shapes and features on products are difficult task with the use of traditional metal cutting techniques. To machine the advanced materials to produce high dimensional accuracy and generation of intricate shapes in difficult-to-machine materials like stainless steels and alloys, nontraditional machining (NTM) techniques are now attractive the viable choices. To attain improved machining performance of the NTM processes, it is always necessary to find the optimal combinations of various process input parameters of those processes. In the present chapter, some aspects of machining of stainless steel and alloys using NTM processes such as electric discharge machining (EDM) and wire EDM, are discussed and some concluding remarks have been drawn from the study

Experimental analysis and optimization of EDM parameters on HcHcr steel in context with different electrodes and dielectric fluids using hybrid Taguchi-based PCA-Utility and CRITIC-Utility approaches

Industries demand stringent requirements towards economical machining without hindering the surface quality while cutting high carbon high chromium (HcHcr) steel. Electrical discharge machining (EDM) of HcHcr steel aims at reducing machining cost (i.e., maximize material removal rate (MRR) and minimize tool wear rate (TWR)) with good surface quality (i.e., minimize surface roughness (SR)). A comparative study was carried out on EDM of HcHcr D2 steel (DIN EN ISO 4957) by applying Taguchi L18 experimental design considering different electrode materials (copper, graphite, and brass), dielectric fluids (distilled water and kerosene), peak current, and pulse-on-time. The process performances were analyzed with respect to material removal rate, surface roughness, and tool wear rate. Pareto analysis of variance was employed to estimate the significance of the process variables and their optimal levels for achieving lower SR and TWR and higher MRR. Hybrid Taguchi-CRITIC-Utility and Taguchi-PCA-Utility methods were implemented to determine the optimal EDM parameters. Higher MRR of 0.0632 g/min and lower SR of 1.68 µm and TWR of 0.012 g/min was attained by graphite electrode in presence of distilled water as dielectric fluid compared to the brass and copper. Additionally, a metallographic analysis was carried out to study the surface integrity on the machined surfaces. Micrographic analysis of the optimal conditions showed lower surface roughness and fewer imperfections (lesser impression, waviness surface, and micro-cracks) compared to worst conditions

TEACHING-LEARNING-BASED PARAMETRIC OPTIMIZATION OF AN ELECTRICAL DISCHARGE MACHINING PROCESS

Due to several unique features, electrical discharge machining (EDM) has proved itself as one of the efficient non-traditional machining processes for generating intricate shape geometries on various advanced engineering materials in order to fulfill the requirement of the present day manufacturing industries. In this paper, the machining capability of an EDM process is studied during standard hole making operation on pearlitic SG iron 450/12 grade material, while considering gap voltage, peak current, cycle time and tool rotation as input parameters. On the other hand, material removal rate, surface roughness, tool wear rate, overcut and circularity error are treated as responses. Based on single- and multi-objective optimization models, this process is optimized using the teaching-learning-based optimization (TLBO) algorithm, and its performance is contrasted against firefly algorithm, differential evolution algorithm and cuckoo search algorithm. It is revealed that the TLBO algorithm supersedes the others with respect to accuracy and consistency of the derived optimal solutions, and computational efforts

EDM PROCESS PARAMETER OPTIMIZATION FOR EFFICIENT MACHINING OF INCONEL-718

In the present work, multi-response optimization of electro-discharge machining (EDM) process is carried out based on an experimental analysis of machining superalloy Inconel-718. The study aims at optimizing and determining an optimal set of process variables, namely discharge current (), pulse-on duration () and dielectric fluid-pressure () for achieving optimal machining performance in EDM. Nine independent experiments based on L9 orthogonal array are carried out by using tungsten as the electrode. The productivity performance of the EDM process is measured in terms of material removal rate (MRR) and its cost parameter is measured in terms of tool wear rate (TWR) and electrode wear rate (EWR). The TOPSIS is used in conjunction with five different criterion weight allocation strategies— (namely, mean weight (MW), standard deviation (SDV), entropy, analytic hierarchy process (AHP) and Fuzzy). While MW, SDV and entropy are based on the objective evaluation of the decision-maker (DM), the AHP can model the DM’s subjective evaluation. On the other hand, the uncertainty in the DM’s evaluation is analyzed by using the fuzzy weighing approach

Multi-Performance Optimization of Wire Cut EDM Process Parameters on Surface Roughness of AA7075 / B4Cp Metal Matrix Composites

This paper focus on multi performance optimization of process parameters for wire cut electric discharge machining of AA7075/B4C (15%) metal matrix composites processed by stir casting technique using taguchi�s design of experiment and regression analysis. The machining was performed as per design of experiments approach using L9 orthogonal array. Four wire cut electric discharge machining parameters namely pulse-on-time (TON), pulse-off-time (TOFF), spark voltage (SV) and wire tension (WT) were chosen as machining process parameters. Signal-to- noise ratio is used to find the optimal combination of process parameters. The mathematical relationships between wire cut electric discharge machining input process parameters and response parameter are established to determine optimal values of surface roughness by using regression analysis. The Analysis of variance (ANOVA) and F-test are performed to obtain statistically significant process parameters. The generated optimal process conditions have been verified by conducting confirmation experiments and predicted results have been found to be in good agreement with experimental findings