A hybrid Grey-TOPSIS based quantum behaved particle swarm optimization for selection of electrode material to machine Ti6Al4V by electro-discharge machining
Journal article
Sahu, A.K., Mahapatra, S.S., Leite, M. and Goel, S. (2022). A hybrid Grey-TOPSIS based quantum behaved particle swarm optimization for selection of electrode material to machine Ti6Al4V by electro-discharge machining. Journal of the Brazilian Society of Mechanical Sciences and Engineering . 44 (188). https://doi.org/10.1007/s40430-022-03494-y
Authors | Sahu, A.K., Mahapatra, S.S., Leite, M. and Goel, S. |
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Abstract | Electro-discharge machining is an extensively used manufacturing process. The process requires a tool electrode but the selection of the right material for preparing the tool continues to remain an engineering puzzle. This work makes use of a hybrid intelligent algorithm for selecting the right electrode out of three tool electrodes such as AlSi10Mg, copper and graphite for efficient electro-discharge machining of Ti6Al4V. The work began by constructing a Taguchi’s L27 experimental design and then collecting the output data such as the material removal rate, tool wear rate, surface roughness, surface crack density, white layer thickness and micro-hardness. A simultaneous multi-objective optimization was performed to maximise the workpiece material removal rate while minimizing the remaining variables. For this purpose, a hybrid grey-TOPSIS based quantum-behaved particle swarm optimization was chosen and additional data gathered from scanning electron microscopy and energy dispersive spectroscopy techniques revealed new insights into the post-machining material behaviour such as the use of graphite electrode makes the machined surface far harder due to the dissociated carbon. |
Year | 2022 |
Journal | Journal of the Brazilian Society of Mechanical Sciences and Engineering |
Journal citation | 44 (188) |
Publisher | Springer |
ISSN | 1678-5878 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s40430-022-03494-y |
Publication dates | |
17 Apr 2022 | |
Publication process dates | |
Accepted | 24 Mar 2022 |
Deposited | 29 Mar 2022 |
Accepted author manuscript | License File Access Level Open |
https://openresearch.lsbu.ac.uk/item/8z962
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