Experimental investigation of sawing approach of low power fiber laser cutting of titanium alloy using particle swarm optimization technique

Abstract Manufacturing industries are very much interested in laser beam machining for its efficiency in material removal. As a result, this non-conventional machining process bears a great determination for its modelling and optimization. Difficulty lies in the development of an accurate model between the input and output variables and the non-linear behaviour of the process makes it complex under various situations. A new process variable of sawing angle makes it more complicated for quality characteristics. The present study deals with a research effort for examination of the influences of the process parameter of fiber laser beam machining regarding cutting of Titanium superalloy (Ti6Al4V). Sawing angle, power, duty cycle, pulse frequency and scanning speed are the input variables in laser beam machining process and quality characteristics of laser machined workpiece along with kerf taper and heat affected zone are the output variables. Empirical findings and existing knowledge in regard to modelling, optimizing, monitoring and controlling of the LBM process have been used by the artificial intelligence (AI) technique. This study bears an application of AI technique regarding metaheuristic-based particle swarm optimization (PSO) in regard to the quality features of LBM in modelling and optimization.