Multi responses optimization of wire EDM process parameters using Taguchi approach coupled with principal component analysis methodology

Dhakad, Anil Kumar; Vimal, Jyoti

doi:10.4314/ijest.v9i2.6

Cited by 7 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the most promise and at ease route for the manufacturing MMC is P/M, although the fundamental parametric comparisons were done with the relative MMC manufacturing practice on hand as of now. P/M technique ensures MMCs with extra uniform microstructure when compared with other different methods [12]. The composition of Copper composite considered is presented in Table 1.…”

Section: Methodsmentioning

confidence: 99%

WEDM of Cu/WC/SiC composites: development and machining parameters using artificial immune system

Meenakshi¹,

Suresh²

2019

Journal of Experimental Nanoscience

View full text Add to dashboard Cite

A systematic read on evaluating the machining characteristics of wire cut electrical discharge machining (WEDM) using analysis of variance (ANOVA) and multilinear regression model based multi objective optimization is provided during this analysis article. The current work explores, the surface roughness of copper metal matrix composite (MMCs) is minimized by optimizing the process parameters like spark on time, spark off time, peak current and wire feed. Taguchi's L9 orthogonal array has been used to conduct the experiments for two samples having a different composition to measure the surface roughness. The order of significance of parameters on surface roughness (Ra) for sample 1 and 2 MMCs were found using ANOVA and a multilinear regression model (MLRM) was developed to predict the Ra value. The new contribution in the present work is that, the coefficients of MLRM were optimized using artificial immune system algorithm with the objective of minimizing the mean absolute percentage error. Finally, the optimum process parameters were obtained to minimize the surface roughness and reported that the reduced value of Ra for 2.5% and 5% WC Copper MMCs were 0.9 lm and 1.1 lm, respectively later which were established by confirmation experiments.

show abstract

Section: Methodsmentioning

confidence: 99%

WEDM of Cu/WC/SiC composites: development and machining parameters using artificial immune system

Meenakshi¹,

Suresh²

2019

Journal of Experimental Nanoscience

View full text Add to dashboard Cite

show abstract

“…In the presence of deionized fluid with a high insulation capacity, a discharge begins to occur between the work piece and the wire electrode when the voltage is set. As a result of the spark generated between the work piece and the electrode, material ahead of the wire erodes [4]. Because the wire, which works as a cutting tool, has a small diameter and substantial mechanical qualities, fine, clean, and exact cuts on the work piece are achievable [5,6].…”

Section: Introductionmentioning

confidence: 99%

Investigation of surface roughness and material removal rate of WEDM of SS304 using ANOVA and regression models

Srinivasan¹,

Ganesh²,

D³

et al. 2022

Surf. Topogr.: Metrol. Prop.

View full text Add to dashboard Cite

Use of machine learning and artificial intelligence (AI) to analyze the complex interdependencies of production dataset has gained momentum in recent years. Machine learning and predictive algorithms are now used by manufacturers to fine-tune the quality of their products. WEDM of SS304 with process parameters such as pulse-on-time (Ton), pulse-off-time (T off), current (I), and voltage (V) was varied to study the effect of machining parameters such as Material Removal Rate (MRR) and surface roughness. Experiments were planned and executed according to the L’9 orthogonal array. Scanning Electron Microscope (SEM) was utilized to study the machined surface. An analysis of variance (ANOVA) was performed to determine the input and output significance. ANOVA results revealed that V (81.85%) and Toff (77.75 %) for surface roughness. Further to determine the relationship between variables, various regression models based on machine learning was tested. The effectiveness of the regression models were tested. From their output it was concluded that the multilayer perception model had the highest correlation coefficient (0.999) for MRR while for surface roughness it was (0.995).

show abstract

Turning of AISI 9260 using MAC nano coolant: multi‑objective optimization and modeling of cutting parameters using MOGA-RSM

Konnur,

Kotagond,

Kanal

et al. 2024

Int J Interact Des Manuf

View full text Add to dashboard Cite

Multi responses optimization of wire EDM process parameters using Taguchi approach coupled with principal component analysis methodology

Abstract: The wire EDM was known as for its better efficiency to machining hardest material and give precise and accurate result comparing to other machining process.

Cited by 7 publications

References 10 publications

WEDM of Cu/WC/SiC composites: development and machining parameters using artificial immune system

WEDM of Cu/WC/SiC composites: development and machining parameters using artificial immune system

Investigation of surface roughness and material removal rate of WEDM of SS304 using ANOVA and regression models

Turning of AISI 9260 using MAC nano coolant: multi‑objective optimization and modeling of cutting parameters using MOGA-RSM

Contact Info

Product

Resources

About