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Title
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Optimizing the performance of high-speed machining on 15CDV6 HSLA steel in terms of green manufacturing using response surface methodology and artificial neural network
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Sub-Title |
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Subject |
"15CDV6 HSLA steel ANN, Green machining, High-speed machining, Near-dry machining,Optimization"
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Sub-Subject |
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Author |
Khawaja, Amar ul Hassan; Jahanzaib, Mirza; Munawar
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Publish Year |
2021 |
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Diss#. |
https://doi.org/10.1007/s12541-021-00520-2 |
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Pages |
1125–1145 |
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Text Language |
English |
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Accession |
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Library Section |
Research Article |
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Abstract |
The execution of sustainable manufacturing methods to make machining processes more eco-friendly is a difficult task that
has attracted significant attention from the industrial area for a long time. As one of the leading manufacturing processes,
machining can have a profound impact on the environment, society, and financial aspects. In a specific scenario, recognizing
reasonable machining conditions to supply cutting fluids utilizing eco-friendly methods is at present a significant focal point
of academic and industrial sector research. This study is to investigate the optimal operational parameters such as speed, feed
rate, and cutting depth during high-speed machining of 15CDV6 HSLA steel under near-dry (green machining) and flood
lubrication using response surface methodology and an artificial neural network that leads to better performance measures
like tool-chip interface temperature, specific energy, yield strength, and percentage elongation. Initially, tensile samples
were prep
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