Sustainable electric discharge machining using alumina-mixed deionized water as dielectric: Process modelling by artificial neural networks underpinning net-zero from industry

Sana, Muhammad; Asad, Muhammad; Farooq, Muhammad Umar; Anwar, Saqib; Talha, Muhammad

doi:10.1016/j.jclepro.2024.140926

Journal of Cleaner Production

2024

DOI: 10.1016/j.jclepro.2024.140926

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Sustainable electric discharge machining using alumina-mixed deionized water as dielectric: Process modelling by artificial neural networks underpinning net-zero from industry

Muhammad Sana,

Muhammad Asad,

Muhammad Umar Farooq

et al.

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Cited by 11 publications

References 36 publications

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Circular usage of waste cooking oil towards green electrical discharge machining process with lower carbon emissions

Ishfaq,

Sana,

Waseem

et al. 2024

Int J Adv Manuf Technol

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A global manufacturing community is dedicatedly striving to implement the concept of NetZero in precision cutting of difficult-to-machine materials, specifically, Inconel 617 (IN617) with due consideration to environmental protocols. The fast strain hardening issue of the said alloy during conventional processing rationalizes the application of electric discharge machining (EDM). However, EDM has been criticized for its high energy consumption and limited cutting efficiency. Moreover, conventional dielectric (kerosene) employed in EDM has drastic environmental and operator health concerns. To address the abovementioned issues, waste cooking oil (WCO) has been employed in this study which enhances the reusability of resources and minimizes the cost of the dielectric. Making the process sustainable is imperative along with continuously escalating scarcity of engineering resources. Therefore, the potential of shallow and deep cryogenically treated electrodes (SCT and DCT) has been comprehensively examined against nanofilled WCO to achieve the aforementioned objective. Three different concentrations of powder (Cp) and surfactant (Cs) to uplift the machining responses are investigated through a detailed parametric experimental design. Core machining factors such as material removal rate (MRR), surface roughness (SR), and specific energy consumption (SEC) are examined through optical and electron microscopy studies and 3D surface profilometry. Hereafter, machining factors are modelled using the artificial neural network (ANN) technique. An exceptional improvement of 80%, 25.3%, and 75.16% has been achieved in MRR, SR, and SEC respectively using nanopowder-mixed WCO against SCT brass compared to the responses’ values obtained against conventionally used kerosene. Furthermore, compared to kerosene, the maximum CO2 reduction of 79.97 ± 11.2% is achieved with WCO.

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Circular usage of waste cooking oil towards green electrical discharge machining process with lower carbon emissions

Ishfaq,

Sana,

Waseem

et al. 2024

Int J Adv Manuf Technol

View full text Add to dashboard Cite

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Comparative investigation of novel dielectrics against cryogenically refined electrodes for modelling and optimizing EDM cutting proficiency using artificial neural network

Ishfaq,

Sana,

Waseem

et al. 2024

Int J Adv Manuf Technol

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Parametric Analysis and Multi-objective Optimization for Machining Complex Features on D2 and DC53 Steels for Tooling Applications

Hassan,

Asad,

Sana

et al. 2024

J. of Materi Eng and Perform

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Sustainable electric discharge machining using alumina-mixed deionized water as dielectric: Process modelling by artificial neural networks underpinning net-zero from industry

Cited by 11 publications

References 36 publications

Circular usage of waste cooking oil towards green electrical discharge machining process with lower carbon emissions

Circular usage of waste cooking oil towards green electrical discharge machining process with lower carbon emissions

Comparative investigation of novel dielectrics against cryogenically refined electrodes for modelling and optimizing EDM cutting proficiency using artificial neural network

Parametric Analysis and Multi-objective Optimization for Machining Complex Features on D2 and DC53 Steels for Tooling Applications

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