Improving 3D-printed high-impact polystyrene using fused filament fabrication via multi-objective optimisation

Purpose This study aims to improve mechanical strength and build time of Fused Filament Fabrication (FFF)-printed high-impact polystyrene (HIPS), considering five key controllable FFF process parameters including layer thickness, printing speed, number of contours, raster angle and infill density and their effects on mechanical performance of the HIPS. Design/methodology/approach This study develops a novel multistage material optimisation framework with a mixing experimental and theoretical analysis procedures for FFF of thermoplastic polymers. Artificial neuron network (ANN) is adopted for pattern recognition before the genetic algorithm (GA) and multi-criteria decision-making algorithm are applied for optimisation. Findings Optimised FFF-printing HIPS with rational balance between mechanical properties (tensile strength, flexural strength and impact strength) and build time were achieved. The infill density as the main contributor to the tensile strength and flexural strength, the raster angle as the main contributor to the impact strength while the layer thickness has the highest impact on the build time. ANN-GA method succeeds at achieving a reasonable balance of mechanical strength and build time. Originality/value User-friendly and innovative methodology are devised for developing highly accurate ANN-GA-TOPSIS models for multi-objective optimisation. Optimum settings for three-dimensional-printing HIPS with rational balance between mechanical properties (tensile strength, flexural strength and impact strength) and build time are achieved. The outcome of this research can be useful to achieve high-performance FFF-printed HIPS parts for automotive industries and medical fields with significantly reduced build time.