Laser-Assisted High Speed Machining of 316 Stainless Steel: The Effect of Water-Soluble Sago Starch Based Cutting Fluid on Surface Roughness and Tool Wear
Laser-assisted high speed milling is a subtractive machining method that employs a laser to thermally soften a difficult-to-cut material’s surface in order to enhance machinability at a high material removal rate with improved surface finish and tool life. However, this machining with high speed...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI
2021
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/36406/1/machining1.pdf http://ir.unimas.my/id/eprint/36406/ https://www.mdpi.com/1996-1944/14/5/1311 https://doi.org/10.3390/ma14051311 |
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| Summary: | Laser-assisted high speed milling is a subtractive machining method that employs a laser
to thermally soften a difficult-to-cut material’s surface in order to enhance machinability at a high
material removal rate with improved surface finish and tool life. However, this machining with
high speed leads to high friction between workpiece and tool, and can result in high temperatures,
impairing the surface quality. Use of conventional cutting fluid may not effectively control the heat
generation. Besides, vegetable-based cutting fluids are invariably a major source of food insecurity of
edible oils which is traditionally used as a staple food in many countries. Thus, the primary objective
of this study is to experimentally investigate the effects of water-soluble sago starch-based cutting
fluid on surface roughness and tool’s flank wear using response surface methodology (RSM) while
machining of 316 stainless steel. In order to observe the comparison, the experiments with same
machining parameters are conducted with conventional cutting fluid. The prepared water-soluble
sago starch based cutting fluid showed excellent cooling and lubricating performance. Therefore,
in comparison to the machining using conventional cutting fluid, a decrease of 48.23% in surface
roughness and 38.41% in flank wear were noted using presented approach. Furthermore, using the
extreme learning machine (ELM), the obtained data is modeled to predict surface roughness and
flank wear and showed good agreement between observations and predictions. |
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