Modelling and estimation of tensile behaviour of polylactic acid parts manufactured by fused deposition modelling using finite element analysis and knowledge-based library

• Published in: Virtual and physical prototyping Vol. 13; no. 3; pp. 177 - 190
• Main Authors: Zhou, Xunfei, Hsieh, Sheng-Jen, Ting, Chen-Ching
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 03.07.2018; Taylor & Francis Group
• Subjects: artificial neural network; finite element analysis; Fused deposition modelling; mechanical behaviour
• ISSN: 1745-2759; 1745-2767
• DOI: 10.1080/17452759.2018.1442681

With the rise of the Fused Deposition Modelling (FDM) industry, a better understanding of the relationship between FDM process parameters and mechanical behaviour -especially tensile behaviour -of designed parts is needed to enable development of industry specifications. To optimise and control the deposition process, modelling and predicting the mechanical behaviour of a manufactured part under various process parameters is required. Existing numerical modelling approaches either require input of extensive experimental data or lack cross-validation. In this paper, the mechanical behaviour of polylactic acid manufactured parts under tensile conditions was studied both experimentally and numerically, and the effects of printing pattern and infill density on ultimate tensile strength (UTS)-weight ratio and the modulus of elasticity were evaluated. The experimental results revealed that minimising air gaps and using a triangular infill pattern are beneficial for obtaining a good UTS/weight ratio. Of all the specimens considered, the 20% triangular pattern had the highest UTS/weight ratio. The numerical investigation revealed that the meso-structure approach described in this paper can be used to predict the modulus of elasticity and the breaking point, and does not require input from the unidirectional specimen stress-strain curves. Finally, the meso-structure numerical model and artificial neural network were used to construct a knowledge-based library that can predict the modulus of elasticity of FDM manufactured polylactic acid with three infill patterns and any infill density with an average prediction error of 14.80%.