Measurement and optimization of power consumption and aerosol emissions in magnetic field-assisted electrical discharge machining of Inconel 718

Processing of Inconel 718 is still big challenge for manufactures to process with better machining performance and less environmental effect. Therefore, the present study was aimed to develop a sustainable magnetic field-assisted electrical discharge machining (MEDM) for machining of Inconel 718 to...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 27; no. 16; pp. 11451 - 11468
Main Authors: Rao, Kaki Venkata, Kumar, Yekula Prasanna, Shaik, Feroz
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023
Springer Nature B.V
Subjects:
Aerosols
Algorithms
Artificial Intelligence
Computational Intelligence
Control
Electric discharge machining
Electric potential
Energy consumption
Energy efficiency
Engineering
Environmental effects
Environmental impact
Machine learning
Magnetic fields
Manufacturing
Mathematical Logic and Foundations
Mechanical properties
Mechatronics
Microcracks
Neural networks
Nickel base alloys
Optimization
Optimization techniques
Power consumption
Process parameters
Robotics
Superalloys
Surface defects
Surface roughness
Sustainable development
Taguchi methods
Titanium alloys
Voltage
HTLBO
Aerosol emissions
Surface roughness
Power consumption
MRR
MEDM
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Summary:Processing of Inconel 718 is still big challenge for manufactures to process with better machining performance and less environmental effect. Therefore, the present study was aimed to develop a sustainable magnetic field-assisted electrical discharge machining (MEDM) for machining of Inconel 718 to reduce power consumption, aerosol emissions and surface defects. Experiments were conducted at different levels of current, pulse on time, pulse off time and voltage with and without magnetic field assistance and the experimental results for power consumption, aerosol emissions, metal removal rate (MRR) and surface roughness were collected and analyzed. The power consumption, surface roughness, recast layers, microcracks, microvoids and microglobules were found to be reduced with improved MRR in the MEDM. A hybrid teaching learning-based optimization algorithm was proposed and optimized the process parameters in MEDM. With the proposed methodology, the MRR, surface roughness, power consumption and aerosol emissions reached 82%, 80%, 87% and 68% of the specified targets, respectively. The optimum process parameters were 4 A of current, 106 µs of pulse on time, 79 µs of pulse off time, 38 V of voltage.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-08292-9