Experimental study and modeling the tensile strength of 3D‐printed aluminium polylactic acid (PLA) parts using artificial neural networks

Abstract

Background: High quality 3D printed products are in high demand, resulting in an increase in the production of 3D printed parts with precise tolerances, improved surface roughness, and overall durability. The processing parameters of 3D printers have a significant impact on the quality of 3D printed parts. Three-dimensionally printed parts must be durable, especially in terms of tensile strength, and its impact on the printer's process parameters must be investigated. Methods: Tensile test specimens were printed in the Makerbot 3D printer with aluminium polylactic acid (PLA) material. The three controllable input parameters taken into consideration were layer thickness, infill density and number of shells. The three levels for each of the respective parameters were 0.1mm, 0.2mm and 0.3mm for layer thickness; 2,3 and 4 for number of shells; 20% 40% and 60% for Infill density. Tensile testing was carried out on the specimens and data was tabulated. Using these data, an artificial neural network model was created using Matlab R2021b software’s neural network toolbox (alternatively Scilab can be used). Results: A high layer thickness (0.3mm) and a 40% infill density were found to be the most effective among all other parameters. The specimen with the lowest layer thickness of 0.1mm, four shells, and a 20% infill density had the highest tensile strength. With the tensile test data, a Matlab ANN model was developed. Validation was done by comparing the values obtained from the model with the experimental data by using random layer thickness, infill density, and number of shells. Conclusions: In conclusion, higher layer thickness has lower tensile strengths. However, as the number of shells and infill density increases, the tensile strength increases. In summary an ANN model was successfully developed and validated to predict 3D printed aluminium parts.