Enhancing high-speed EDM performance of hybrid aluminium matrix composite by genetic algorithm integrated neural network optimization

Ali, Muhammad Asad; Mufti, Nadeem Ahmad; Sana, Muhammad; Tlija, Mehdi; Farooq, Muhammad Umar; Haber, Rodolfo

doi:10.1016/j.jmrt.2024.07.077

Journal of Materials Research and Technology

2024

DOI: 10.1016/j.jmrt.2024.07.077

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Enhancing high-speed EDM performance of hybrid aluminium matrix composite by genetic algorithm integrated neural network optimization

Muhammad Asad Ali,

Nadeem Ahmad Mufti,

Muhammad Sana

et al.

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2024

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Cited by 5 publications

References 41 publications

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Optimization of process parameters for printed circuit board drilling for Micro needle with Socio inspired optimization algorithms

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Investigation of EDM erosion behavior for Ni-based superalloy using experimental and machine learning approach

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AISI D2 steel machining and manufacturing process optimization for tooling applications in biomedical industry

Tlija,

Rashid,

Sana

et al. 2024

AIP Advances

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Tool steels such as AISI D2 are famous in the manufacturing industry because of their engineering applications. The precise interplay of improved hardness and toughness makes the machining of complex geometries challenging through conventional machining options. Therefore, non-conventional processes such as wire electric discharge machining (WEDM) are preferred because of their simultaneous machining and surface modification actions. To investigate the complex process parameters and their sensitivity, material removal rate (MRR) and cutting surface roughness (SR) are the corresponding performance measure characteristics for WEDM machining on AISI D2 tool steel. The L18 mixed-level Taguchi technique has been used for obtaining combinations of experiments on two levels of thickness and three levels of other remaining factors (21 × 33). Analysis of variance (ANOVA) and signal-to-noise ratio have been applied to measure the magnitude of effects on each control factor, to investigate the optimum levels of input process parameters on machining characteristics, and to identify their significance. ANOVA analysis revealed that, for both responses, all main effect variables are highly significant, with p-values equal to zero. Moreover, the coefficient of determination (R2) value in the ANOVA findings for both responses is above 97%, indicating the high reliability of the model. In addition, the composite desirability (dG) is considered to maximize MRR and minimize the SR during WEDM of D2; the better combination of optimum levels of machining parameters (T = 25.4 mm, Pon = 4 µs, SV = 95 V, and WT = 5 kg-f) has a dG of 0.5614.

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Enhancing high-speed EDM performance of hybrid aluminium matrix composite by genetic algorithm integrated neural network optimization

Cited by 5 publications

References 41 publications

Optimization of process parameters for printed circuit board drilling for Micro needle with Socio inspired optimization algorithms

Optimization of process parameters for printed circuit board drilling for Micro needle with Socio inspired optimization algorithms

Investigation of EDM erosion behavior for Ni-based superalloy using experimental and machine learning approach

AISI D2 steel machining and manufacturing process optimization for tooling applications in biomedical industry

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