Nowadays, a rotary friction welding method is accepted in many industries, particularly for joining dissimilar materials as a mass production process. It is due to advantages like less material waste, low production time and low energy expenditure. The effect of the change in carbon contents in steel is studied experimentally in the rotary friction welding process, and a statistical model is developed. The Grey Taguchi method gives the single parameters optimization for all output responses. The paper aims to discuss these issues.Design/methodology/approachAn experimental setup was designed and produced to achieve the multi-response in single optimum parameters through Grey relational analysis. A continuous/direct drive rotary friction welding process is chosen in which transition from friction to the forging stage can be achieved automatically by applying a break. In this experimentation, high carbon and low carbon work-pieces with different carbon percentage were welded with rotary friction welding. Response tensile strength and micro-hardness of the design of the experiment are used to analyze the results.FindingsThe optimization of parameters has been performed with Grey relational analysis, and optimum parameters are friction pressure 40 kg/cm2, forging pressure 100 kg/cm2 and speed 1,120 rpm. GRA optimum parameters give 56.04 and 82.16 percent improvement in Tensile strength and micro-hardness, respectively.Practical implicationsHigh carbon steel (En-31) and low carbon steel (SAE-1020) are used in so many industrial applications. These materials are mostly used in the process like manufacturing, metallurgy, machinery, agricultural, etc. These practical applications have brought forward definite and notable economic benefits.Originality/valueIt provides a new framework to investigate the problems where multiple input machining variables and various output responses are obtained in single optimized parameters.
Grey Systems: Theory and Application – Emerald Publishing
Published: Sep 30, 2019
Keywords: Optimization; Multiobjective; GRG; HCS; LCS; RFSW