Sustainability metrics targeted optimization and electric discharge process modelling by neural networks
Muhammad Sana1, Muhammad Asad1, Muhammad Umar Farooq2
1Department of Industrial and Manufacturing Engineering, Faculty of Mechanical Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan.
Scientific Reports
|January 27, 2025
Summary
Cryogenic treatment of brass electrodes significantly improves electric discharge machining (EDM) rates for Al6061. This study optimized parameters using artificial neural networks and genetic algorithms, enhancing material removal and reducing energy consumption.
Area of Science:
- Materials Science and Engineering
- Manufacturing Processes
- Surface Engineering
Background:
- Aluminium alloys, particularly Al6061, are favored for their low density and high strength, but conventional machining of intricate parts is costly.
- Electric Discharge Machining (EDM) offers a non-traditional alternative for machining these alloys, though often limited by low material removal rates.
- Sustainable machining practices are increasingly important, prompting the exploration of alternatives to traditional kerosene oil dielectrics.
Purpose of the Study:
- To investigate the impact of cryogenic treatment (CT) on brass electrodes to enhance EDM machining rates for Al6061.
- To evaluate deionized water as a sustainable dielectric fluid in EDM, replacing kerosene oil.
- To optimize machining parameters and predict responses like material removal rate (MRR), surface roughness (SR), and specific energy consumption (SEC).
Main Methods:
- Cryogenic treatment (CT) was applied to brass electrodes used in EDM of Al6061.
- Deionized water (DI) was employed as the dielectric fluid.
- Machining variables (spark voltage, pulse-on-time, peak current, Al2O3 powder concentration) were systematically varied and analyzed using optical microscopy, SEM, EDX, and 3D surface plots.
- Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II) were utilized for prediction and multi-response optimization.
Main Results:
- Cryogenically treated (CT) electrodes demonstrated significant improvements in MRR, SR, and SEC compared to non-treated (NT) electrodes.
- Multi-response optimization using NSGA-II yielded superior results for CT electrodes, achieving a 64.82% improvement in MRR, 27.45% in SR, and 46.60% in SEC over un-optimized settings.
- Optimal processing parameters were identified as peak current (IP) = 24.85 A, spark voltage (SV) = 2.18 V, pulse-on-time (PON) = 119.11 µs, and Al2O3 concentration (CP) = 1.05 g/100 ml.
Conclusions:
- Cryogenic treatment of brass electrodes is an effective method to enhance EDM performance for Al6061, leading to higher machining efficiency.
- The use of deionized water as a dielectric offers a sustainable alternative in EDM processes.
- ANN and NSGA-II are powerful tools for predicting and optimizing complex machining responses, enabling significant improvements in process efficiency and quality.
