An investigation of the effects of ironing parameters on the surface and compression properties of material extrusion components utilizing a hybrid-modeling experimental approach

Abstract

AbstractThermoplastic filament Material Extrusion (ME) is one of the most expansive 3D printing processes. Owed to the ME process’s simplicity, low cost of materials, popularity, and flexibility, considerable attention has been devoted to manufacturing specific parts in industries manipulating the polylactic acid (PLA) polymer, such as automotive and biomedical. This work aims to experimentally investigate the material flow and temperature for different layer heights on the surface texture parameters and compression strength of a tailored PLA hexagonal prismatic part. An experimental approach based on L9 Taguchi’s array and residual analysis (ANOVA) was employed to clarify the parameters’ effects and trends regarding the response variables. The analysis of means showed that the material flow and layer height are critical variables in defining ME parts’ roughness and compression. Based on ANOVA and mean absolute percentage errors (MAPE) results, additive models (ADMO) were used to predict all combinatorial response values. Then, the experimental and the ADMO values feed as trained data for developing a feed-forward back-propagation neural network (FFBP-NN). Three independent experiments confirmed the validity of the proposed methodology resulting in reasonable accuracy of all the performance metrics, making the proposed hybrid-modeling approach adequate for process multi-parameter multi-objective optimization 3D printing cases.