ABSTRACT
The present work focuses on integrating response surface methodology (RSM) and krill herd algorithm (KHA) to determine optimal variables for selective inhibition sintering (SIS) process for enhancing the surface roughness characteristics. Firstly, SIS experiments are designed and high-density polyethylene parts are fabricated using RSM-based Box–Behnken design approach considering layer thickness, heater energy, heater feed rate and printer feed rate as the independent variables and surface roughness characteristics such as Ra , Rz and Rq as the responses. Further, the interrelationship between the selected variables and responses is established using multiple linear regression models. Then, the effects of process variables on the surface roughness characteristics are assessed through three-dimensional response surface plots. Finally, the multi-response optimization is performed and optimal SIS variables are assessed through KHA approach. The optimization results reveal that a layer thickness of 0.12 mm, a heater energy of 28.47 J/mm2, a heater feed rate of 3.30 mm/sec and a printer feed rate of 100.90 mm/min help in achieving the desired surface quality.
