Abstract
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.
Original language | English |
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Pages (from-to) | 5133-5153 |
Number of pages | 21 |
Journal | International Journal of Advanced Manufacturing Technology |
Volume | 131 |
Issue number | 9-10 |
Early online date | 1 Mar 2024 |
DOIs | |
Publication status | Published - Apr 2024 |
Keywords
- Artificial neural networks
- Circular economy
- Electrical discharge machining
- Specific energy consumption
- Surface roughness
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Mechanical Engineering
- Computer Science Applications
- Industrial and Manufacturing Engineering