Artificial neural network modelling and analysis of carbon nanopowder mixed micro wire electro discharge machining of gold coated doped silicon
This research aims to machine gold coated doped Silicon (Si) wafer using micro wire electro discharge machining or uWEDM process in the presence of carbon nanopowder mixed dielectric oil. Effects of uWEDM parameters namely voltage, capacitance, powder concentration and coating thickness on avera...
| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
Inderscience Publishers
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/76241/ http://irep.iium.edu.my/76241/ http://irep.iium.edu.my/76241/ http://irep.iium.edu.my/76241/1/76241_Artificial%20neural%20network%20modelling.pdf http://irep.iium.edu.my/76241/2/76241_Artificial%20neural%20network%20modelling_SCOPUS.pdf |
| Summary: | This research aims to machine gold coated doped Silicon (Si) wafer
using micro wire electro discharge machining or uWEDM process in the
presence of carbon nanopowder mixed dielectric oil. Effects of uWEDM
parameters namely voltage, capacitance, powder concentration and coating
thickness on average surface roughness (ASR), material removal rate (MRR)
and spark gap (SG) have been analysed. All the three output parameters, i.e.
material removal rate (MRR), spark gap (SG) and average surface roughness
(ASR) were found to follow the parabolic trend with the variation of the
nanopowder concentration. SG and ASR both were observed to be increased with the coating thickness. However, MRR was decreased with the same. An
Artificial neural network based technique was used to model various responses. The overall model prediction was found to be in good agreement (average error
less than 10%) with the experimental results for the corresponding input process parameters. |
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