Application of Taguchi method on Wire Electrical Discharge Machining of Inconel 625

Manikandan, N.; Binoj, J. S.; Thejasree, P.; Sasikala, P.; Anusha, P. T.

doi:10.1016/j.matpr.2020.06.394

Cited by 14 publications

(5 citation statements)

References 13 publications

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“…(2021) [21] L Kumar, U. (2021) [22] L Manikandan (2020) [23] L Ramaswamy (2020) [24] Muniappan (2019) [25] L Babu (2019) [26] L 9 Dayakar (2019) [27] L…”

Section: Materials Removal Rate Energy Othermentioning

confidence: 99%

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Optimization of Cutting Parameters for Energy Efficiency in Wire Electrical Discharge Machining of AISI D2 Steel

González-Rojas,

Miranda-Valenzuela,

Calderón-Najera

2024

Applied Sciences

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Improving energy efficiency in manufacturing processes is a critical global concern for the industry. Manufacturers strive to enhance energy efficiency across all manufacturing operations to remain competitive globally, aiming to reduce production times without compromising product quality. While there has been significant research characterizing energy efficiency and surface roughness in conventional processes like turning or milling, studies on unconventional manufacturing techniques are limited. This study focuses on optimizing a wire electrical discharge machining (WEDM) process to minimize energy consumption while maintaining surface roughness. Various cutting parameters, such as pulse on-time, pulse off-time, servo voltage, wire tension, wire speed, and wire voltage, were evaluated. Experiments were conducted using Taguchi’s methodology with a L27 orthogonal array, employing AISI D2 steel plates of 19 mm and 25 mm thickness as the machining material. The research identified that optimal parameters for reducing energy consumption and improving surface roughness included a pulse on-time of 10 s, pulse off-time of 11 s, servo voltage of 44 V, wire tension of 50 g-force, wire speed of 7 m per minute, and wire voltage of 9 volts. This combination led to an 8% reduction in energy consumption and a 1% enhancement in surface roughness compared to baseline values.

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“…(2021) [21] L Kumar, U. (2021) [22] L Manikandan (2020) [23] L Ramaswamy (2020) [24] Muniappan (2019) [25] L Babu (2019) [26] L 9 Dayakar (2019) [27] L…”

Section: Materials Removal Rate Energy Othermentioning

confidence: 99%

“…In the study by Manikandan et al [23], the wire electrical discharge machining of Inconel 625 was investigated with a focus on optimizing process parameters including pulse on-time, pulse off-time, and peak current. The desired performance measures considered were material removal rate and overcut.…”

Section: Materials Removal Rate Energy Othermentioning

confidence: 99%

Optimization of Cutting Parameters for Energy Efficiency in Wire Electrical Discharge Machining of AISI D2 Steel

González-Rojas,

Miranda-Valenzuela,

Calderón-Najera

2024

Applied Sciences

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“…In addition, it should be pointed out that the surface roughness is the machining parameter that The process of WEDM involves creating intricate components for aerospace and automotive industries. When sparks are discharged from the sources, dielectric medium can enter the work surface and remove materials [7,8]. The wire electrode is moved from the far end of the space among the work material and the tool to the far end.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Machining of Hastelloy Using WEDM by a Multi-Objective Approach

Manikandan¹,

Thejasree²,

Abdullah

et al. 2023

Sustainability

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Superalloys are a much-needed material for abundant engineering fields, such as nuclear-powered reactor components and aeronautics. Owing to their exceptional characteristics, such as higher thermal conductivity, they can be difficult to machine using conventional processes. Modern approaches to machining have evolved to utilize these materials. One of the techniques studied in this project is electrical discharge in a wire machine. This process can help to reduce the energy consumption during machining and negative impact on the environment. In addition, shortening the operation time of the machine can help to minimize its impact on the environment. The duration of the pulse and applied current are independent factors considered in this study. Material removal rate, surface roughness, dimensional deviation, and form/orientation tolerance errors are deemed as performance measures. The goal of this investigation is to reduce the time required to machine and improve the surface finish of components by implementing a Grey-based artificial neural network model. This method is useful in foretelling the conditions of the Wire Electro Discharge Machining (WEDM) process. This paper uses the Taguchi design and Analysis of Variance (ANOVA) framework to analyze the model’s variable inputs. The overall best coefficient of correlation (R = 0.9981) is fetched with an RMSE value of 0.0086. The material removal rate has been increased by decreasing the time taken for removal, which gives the possibility of consuming minimum energy. The finishing of the machined surface also improved. Moreover, this paper shows how to use an Artificial Neural Network (ANN) model with Grey Analysis. The results of the comparative analysis show that the values envisaged are closer with the actual values. The foretelling capacity of the evolved model is confirmed with the performance analysis of the developed model.

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“…A number of research works have been carried out to find the effect of process parameters on the performance of WEDM [7][8][9] and for WEDM process optimization. [10][11][12][13][14] Researchers implemented Taguchi method, Fuzzy based Taguchi technique, FIS coupled TLBO, ANN based GWO, ANN based TLBO, ANN based Jaya algorithm and ANN based PSO to optimize WEDM process during Inconel 625 machining. [10][11][12][13][14] However, a very less work was reported on the use of multi objective optimization algorithm in WEDM operation of Inconel 625.…”

Section: Introductionmentioning

confidence: 99%

“…[10][11][12][13][14] Researchers implemented Taguchi method, Fuzzy based Taguchi technique, FIS coupled TLBO, ANN based GWO, ANN based TLBO, ANN based Jaya algorithm and ANN based PSO to optimize WEDM process during Inconel 625 machining. [10][11][12][13][14] However, a very less work was reported on the use of multi objective optimization algorithm in WEDM operation of Inconel 625. 15 Recently, Multi-objective Grasshopper Optimization Algorithm has emerged as an efficient algorithm used in solving multi objective optimization problems.…”

Section: Introductionmentioning

confidence: 99%

A RSM based MOGOA for process optimization during WEDM of Inconel 625

Sinha

Majumder

Gupta

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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Inconel 625 is a super alloy that has excellent thermal and mechanical properties but is arduous to machine with conventional machining processes due to high melting point and hardness. Wire Electro Discharge machining, that have been an avowed unconventional process of machining that is mainly applied for the machining of tough and hard materials such as Inconel 625. An attempt, in this article, have been made to optimize the WEDM process parameters like servo voltage, peak current, spark-on time, spark-off time while machining on Inconel 625. The study considers Material Removal Rate (MRR) and Surface Roughness (SR) as performance parameters of machining process. L16 orthogonal array is implemented to design the experiments. Mathematical relations between the response parameters and the input variables are computed using of Response Surface Methodology (RSM). The mathematical models are then employed as a fitness function in the Multi-objective Grasshopper Optimization Algorithm (MOGOA) for determining optimal process parameters. Later, a comparison of MOGOA has been done with Non-dominated Sorting Genetic Algorithm II (NSGA- II) and Multiple Objective Particle Swarm Optimization (MOPSO) and found superior to both. Also, for responses like surface roughness (SR) and material removal rate (MRR), the predicted values in the present study are found to be in good proximity to the experimented values.

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Application of Taguchi method on Wire Electrical Discharge Machining of Inconel 625

Cited by 14 publications

References 13 publications

Optimization of Cutting Parameters for Energy Efficiency in Wire Electrical Discharge Machining of AISI D2 Steel

Optimization of Cutting Parameters for Energy Efficiency in Wire Electrical Discharge Machining of AISI D2 Steel

Assessment of Machining of Hastelloy Using WEDM by a Multi-Objective Approach

A RSM based MOGOA for process optimization during WEDM of Inconel 625

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