A Critical Review on Optimization of WEDM Process Using Taguchi Array

Wire electrical release machining (WEDM) innovation has developed at special case rate since it was first applied over long term prior.WEDM is a widely recognized unconventional material cutting process used to manufacture components with complex shapes and profiles of hard materials. In this thermal erosion process, there is no physical contact between the wire tool and work materials. Wire Electrical Discharge Machining (WEDM) is getting more tasks in fields like dies, punches, aero and many more. It is the very difficult task to get optimum process parameters for higher cutting efficiency. In WEDM process rough machining gives lesser accuracy and finish machining gives fine surface finish, but it reduces the machining speed. This review involves process, principle, literature and applications of WEDM using Taguchi array. Keywords: WEDM; Materials; Machine; Cutting efficiency; Optimization process.

Multiple-Performance Optimization of Wire EDM Parameters for HDS H13 BY Using Taguchi Method

Wire electrical discharge machining is extensively used in machining of conductive materials. The WEDM process has the ability to machine complex shapes and hard electrically conductive metal components precisely. The main goal of wire electrical discharge machine manufacturers and users are to achieve a better stability and high productivity of the process with desired accuracy and minimum surface damage.The main objectives of the present research are to experimentally study the effect of various process parameters like pulse on time, pulse off time, wire feed, and wire tension on the performance measures like material removal rate, surface roughness and wire wear ratio. WEDM is a widely recognized unconventional material cutting process used to manufacture components with complex shapes and profiles of hard materials. In this paper we are presenting the development of WEDEM process using various pre define parameters using Taguchi method. Index Terms: WEDM, doe, orthogonal array, parameters, Taguchi method, H13, HDS, mean of means, SF, MRR, Ra, etc.

Wire Electrical Discharge Machining (WEDM) of Hybrid Composites (Al-Si12/B4C/Fly Ash)

The present study looks into the effect of WEDM process parameters on the material removal rate (MRR) and surface roughness (SR) responses when machining hybrid composites (Al-Si12/boron carbide/fly ash) using the Taguchi technique. Fly ash and boron carbide (B4C) particles were used for reinforcement (3%, 6%, and 9% by weight), and aluminium alloy (Al-Si12) was used as a matrix material. ANOVA was used to find out the importance of machining factors that affect the quality features of the WEDM process, as well as the relative role of input parameters in determining the WEDM process’ responses. The greatest impact on the response is finalised by the signal-to-noise (S/N) ratio response analysis. However, as a last step, a confirmation experiment with the best combination was carried out to predict and validate the accuracy of the observed values. As the pulse on time and reinforcement increases, MRR also increases. As the gap voltage, wire feed, and pulse off time decrease, it increases. SR is increased by increasing the gap voltage, pulse on time, and pulse off time, wire feed, and reinforcement. The maximum MRR of 38.01 mm3/min and the minimum SR of 3.24 μm were obtained using optimal machining conditions.

Multiresponse Particle Swarm Optimization of Wire-Electro-Discharge Machining Parameters of Nitinol Alloys

The conventional process of machining of nitinol alloy which possesses excess strain hardening and low thermal conductivity makes a complex process that leads to extensive wear on the tool and inadequate surface quality. Wire-electro-discharge machining (WEDM) is widely accepted for machining this alloy involving various input factors, namely, P (pulse-on-duration), Q (pulse-off-duration), C, (maximum-current), and V (voltage). Using the PSO (particle swarm optimization) method, the effect of WEDM process factors on multiresponses such as MRR (metal removal rate) and SR (surface roughness) has been investigated. ANOVA was used to create a relationship model between input factors and response characteristics, which was then optimized using response surface methods (RSM). ANOVA revealed that variables A and C are the most significant. When investigated individually, the influence of WEDM process parameters on SR and MRR is contradictory, as no response provides the best process quality. To find the optimal ideal condition for decreasing SR and maximizing MRR, the MOOPSO approach was used. P = 25.47051 μs, Q = 10.84998 μs, C = 2.026317 A, and V = 49.50757 volts were used to calculate the optimal universal solution for machining characteristics (MRRmax = 3.536791 mm3/min and SRmin = 1.822372 μm). PSO enhanced MRR and SR for every optimal combination of variables, according to the findings. Based on the findings, a wide range of optimal settings for achieving maximum MRR and minimum SR are given, depending on the product requirements.

Data-driven probabilistic performance of Wire EDM: A machine learning based approach

The wire electric discharge machining (WEDM) is a potential alternative over the conventional machining methods, in terms of accuracy and ease in producing intricate shapes. However, the WEDM process parameters are exposed to unavoidable and unknown sources of uncertainties, following their inevitable influence over the process performance features. Thus, in the present work, we quantified the role of parametric uncertainty on the performance of the WEDM process. To this end, we used the practically relevant noisy experimental dataset to construct the four different machine learning (ML) models (linear regression, regression trees, support vector machines, and Gaussian process regression) and compared their goodness of fit based on the corresponding R2 and RMSE values. We further validated the prediction capability of the tested models by performing the error analysis. The model with the highest computational efficiency among the tested models is then used to perform data-driven uncertainty quantification and sensitivity analysis. The findings of the present article suggest that the pulse on time ( Ton) and peak current (IP) are the most sensitive parameters that influence the performance measures of the WEDM process. In this way, the current study achieves two goals: first, it proposes a predictive framework for determining the performance features of WEDM for unknown design points, and second, it reports data-driven uncertainty analysis in the light of parametric perturbations. The observations reported in the present article provide comprehensive computational insights into the performance characteristics of the WEDM process.

Integration of Fuzzy AHP and Fuzzy TOPSIS Methods for Wire Electric Discharge Machining of Titanium (Ti6Al4V) Alloy Using RSM

Titanium and its alloys exhibit numerous uses in aerospace, automobile, biomedical and marine industries because of their enhanced mechanical properties. However, the machinability of titanium alloys can be cumbersome due to their lower density, high hardness, low thermal conductivity, and low elastic modulus. The wire electrical discharge machining (WEDM) process is an effective choice for machining titanium and its alloys due to its unique machining characteristics. The present work proposes multi-objective optimization of WEDM on Ti6Al4V alloy using a fuzzy integrated multi-criteria decision-making (MCDM) approach. The use of MCDM has become an active area of research due to its proven ability to solve complex problems. The novelty of the present work is to use integrated fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal situation (TOPSIS) to optimize the WEDM process. The experiments were systematically conducted adapting the face-centered central composite design approach of response surface methodology. Three independent factors—pulse-on time (Ton), pulse-off time (Toff), and current—were chosen, each having three levels to monitor the process response in terms of cutting speed (VC), material removal rate (MRR), and surface roughness (SR). To assess the relevance and significance of the models, an analysis of variance was carried out. The optimal process parameters after integrating fuzzy AHP coupled with fuzzy TOPSIS approach found were Ton = 40 µs, Toff = 15 µs, and current = 2A.