Development of statistical and soft computing regression models for WEDM machining of aluminum composites

G, Ramanan; J, Edwin Raja Dhas; K, Anton Savio Lewise; Kulandaiyappan, Naveen Kumar; Stanislaus Arputharaj, Beena; Rajendran, Parvathy; Raja, Vijayanandh; Karuppasamy, Arunkumar

doi:10.1007/s12008-024-02017-4

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2024

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Cited by 2 publications

References 26 publications

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Experimental investigation on optimizing wire-cut EDM process parameters of hybrid MMC(AA7475/ZrO₂/Gr) using artificial neural network

Singuru,

Chekuri

2024

Eng. Res. Express

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Wire cut Electrical discharge machining (WCEDM) is a widely used method for machining complex shapes in advanced materials like metal matrix composites (MMCs) and hybrid metal matrix composites (HMMCs). To address these challenges, this study focusses on the wire-cut EDM (WCEDM) process of a workpiece made from zirconium dioxide and graphite-reinforced aluminium alloy 7475 with a molybdenum electrode. The effects of input process variables such as peak current (IP), pulse-on-time (TON), and flushing pressure (PF) on the output response features are investigated. These output responses include material removal rate (MRR), surface roughness (SR), and wire wear ratio (WWR). To optimise the process parameters, the Taguchi design technique is used. An artificial neural network (ANN) with a feed-forward back propagation architecture is utilised to find the best fit for the optimisation challenges. ANN predicted the results with an accuracy of 97.81% for MRR, 97.95 % for SR and 95.865% for WWR. The results reveal that the WCEDM of AA7475/ZrO2/Gr with a molybdenum electrode achieved minimal WWR and SR, while maximizing the MRR.

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Experimental investigation on optimizing wire-cut EDM process parameters of hybrid MMC(AA7475/ZrO₂/Gr) using artificial neural network

Singuru,

Chekuri

2024

Eng. Res. Express

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Comparison of Machine Learning Algorithms for Dynamic Performance Assessment in Complex Shapes Manufacturing of Hybrid Particle-Reinforced Composites

Ali,

Mufti,

Sana

et al. 2024

Preprint

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Development of statistical and soft computing regression models for WEDM machining of aluminum composites

Cited by 2 publications

References 26 publications

Experimental investigation on optimizing wire-cut EDM process parameters of hybrid MMC(AA7475/ZrO₂/Gr) using artificial neural network

Experimental investigation on optimizing wire-cut EDM process parameters of hybrid MMC(AA7475/ZrO₂/Gr) using artificial neural network

Comparison of Machine Learning Algorithms for Dynamic Performance Assessment in Complex Shapes Manufacturing of Hybrid Particle-Reinforced Composites

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Development of statistical and soft computing regression models for WEDM machining of aluminum composites

Cited by 2 publications

References 26 publications

Experimental investigation on optimizing wire-cut EDM process parameters of hybrid MMC(AA7475/ZrO2/Gr) using artificial neural network

Experimental investigation on optimizing wire-cut EDM process parameters of hybrid MMC(AA7475/ZrO2/Gr) using artificial neural network

Comparison of Machine Learning Algorithms for Dynamic Performance Assessment in Complex Shapes Manufacturing of Hybrid Particle-Reinforced Composites

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Experimental investigation on optimizing wire-cut EDM process parameters of hybrid MMC(AA7475/ZrO₂/Gr) using artificial neural network

Experimental investigation on optimizing wire-cut EDM process parameters of hybrid MMC(AA7475/ZrO₂/Gr) using artificial neural network