Parameter Optimization and Machining Performance of Inconel 625 with Nanoparticles Dispersed in Biolubricant. (24th August 2022)

• Record Type: Journal Article
• Authors: Mohanraj, T.; Radhika, N.; Aswin Nanda, S.; Vignesh, V.; Jayaraman, B.; Ratana Selvan, K. R.; Admassu, Yesgat
• Other Names: Gopal Pudhupalayam Muthukutti Academic Editor.
• Is Part Of: Advances in materials science and engineering. Volume 2022(2022)
• Publication Date: 2022-08-24
• Subjects: Materials science -- Periodicals; Materials science; Periodicals; 620.11
• DOI: 10.1155/2022/7210265
• Languages: English
• ISSNs: 1687-8434

Abstract

Abstract : Productivity and cost-effectiveness are essential components of any long-term manufacturing system. While quantity and quality are linked to productivity, the economy focuses on energy-efficient processes that produce a high output-to-input ratio. Hard-to-cut materials have always been difficult to machine because of more significant tool wear and power losses. Inconel 625 is a hard material used in aerospace and underwater applications and is milled using biolubricants with nanoparticles. Palm oil is considered a biolubricant, and titanium dioxide (TiO2 ) and copper oxide (CuO) are selected as nanoparticles. When the combination of biolubricants and nanoparticles is added to the workpiece's surface, it enhanced some properties while machining. Experiments involving four factors with four levels were carried out using the Taguchi design of experiments (DoE). The feed, depth of cut, speed, and coolant with nanoparticle additives were all factors. The responses were surface roughness, spindle vibration along X, Y, and Z axes, and material removal rate. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to alter the multiresponse optimization problem to a single-response optimization problem. The S/N of TOPSIS closeness coefficients was calculated, and the optimal machining conditions were determined. Surface roughness, material removal rate, and spindle vibration were reduced by 3.10%, 6.14%, 7.54% ( Vx ), and 6.78% ( Vz ), respectively, due to the TOPSIS optimization.