Short Title: Int. J. Mech. Eng. Robot. Res.
Frequency: Bimonthly
Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
2024-10-25
2024-09-24
Abstract—This paper is concerned with optimization of surface roughness when drilling of stainless steel SS304 with HSS drill. This study included drilling of SS304 with supply of Sic abrasive having grain size 650 and 1250 mesh size through abrasive slurry system. Abrasives not only used for cooling purpose but also increases the surface finish, MRR and reduce tool wear. Experiments were conducted on a universal milling machine. Response Surface Methodology (RSM) is applied for executing the planning of experiments. Analysis of variance is employed to find the significant control factors and percentage contributions of each control factor. The drilling parameters namely spindle speed, feed rate; slurry concentration and mesh size were optimized using multiple performance characteristics for surface roughness. The result shows that the feed rate, and spindle speed are the most significant factors which affect the surface roughness and performance in the drilling can be effectively improved by using this approach. Index Terms—Abrasives ANOVA, drilling, HSS drill bit, RSM, Stainless steel SS304, Surface roughness
Cite: Sudha Kumari, Kapil Kumar Goyal, and Vivek Jain, "Optimization of Cutting Parameters for Surface Roughness of Stainless Steel SS304 in Abrasive Assisted Drilling ," International Journal of Mechanical Engineering and Robotics Research, Vol. 2, No. 4, pp. 209-215, October 2013.