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

• Main Authors: Farhana, Yasmin, Khairul Fikri, Tamrin, Nadeem Ahmed, Sheikh, Pierre, Barroy, Abdullah, Yassin, Amir Azam, Khan, Shahrol, Mohamaddan
• Format: Article
• Language: English
• Published: MDPI 2021
• Subjects: TA Engineering (General). Civil engineering (General); TJ Mechanical engineering and machinery
• 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
• Institution: Universiti Malaysia Sarawak

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.