Modeling and prediction of optimal process parameters in wear behaviour of selective inhibition sintered high density polyethylene parts

Abstract

Owing to the dependency of multiple process parameters in additive manufacturing (AM) techniques, it is tedious to determine optimal processing conditions for improving quality characteristics of fabricated functional pats. The present study focuses on optimization of four key contributing parameters such as layer thickness, heater energy, heater feedrate, and printer feedrate on dry sliding wear behaviour of high-density polyethylene (HDPE) parts fabricated through a novel selective inhibition sintering (SIS) process. The experiments are conducted on the basis of response surface methodology (RSM) and four factor-three level box-behnken design. The significance of the developed models and contribution of each process parameters on wear rate are estimated through analysis of variance (ANOVA). The results suggested that wear rate is influenced principally by the layer thickness and heater energy. The quadratic regression model of RSM associated with the desirability approach is employed to determine optimum levels of process parameters. The morphologies of worn surfaces are observed using scanning electron microscope. Sensitivity analysis has been performed to measure the relative impact of SIS process parameters on wear rate.