Parametric Optimization on Impact Strength of Selective Inhibition Sintering Fabricated PA-12 Parts Based on Evolutionary Optimization Algorithms

Abstract

Selective inhibition sintering (SIS) is one of the novel additive manufacturing techniques to fabricate complex three-dimensional (3D) functional parts in short span. The quality of SIS processed parts can be achieved through appropriate selection of various SIS process variables such as infrared (IR) sintering temperature, part bed temperature, IR heater scanning distance, IR heater feed rate and layer thickness. The higher impact resistance is always preferred so that the materials absorb more energy during the collision. However, additively manufactured parts have experienced low impact strength. The effect of SIS process variables on the impact strength characteristics of SIS-processed parts has not yet studied. Hence, the present investigation emphasizes on the optimization of these variables on enhancing the impact strength of polyamide parts through using metaheuristic techniques, namely genetic and simulated annealing algorithms. The SIS experiments were planned and performed based on three-level five-factor Box-Behnken design (BBD). An empirical model was developed to correlate the impact strength and input process variables. The cogency of the developed model was validated using analysis of variance (ANOVA) at 95% confidence levels. The optimization results show that the genetic algorithm provides optimal SIS parameters with minimized computational efforts compared to that of simulated annealing algorithm. The maximum impact strength of 44.26 J/m was found with optimal IR heater temperature of 240 °C, sintering bed temperature of 176 °C, IR heater feed rate of 3.46 mm/s, IR heater scanning distance of 10 mm and layer thickness of 0.6 mm. Further, the validation experiments were conducted based on the attained optimal SIS process variables for evaluating the effectiveness of both the optimization methods.