Long-term ageing effect on mechanical properties of polyamide 12 printed by multi-jet-fusion

• Main Authors: Koh, Zhi Hui, Chen, Kaijuan, Du, Hejun, Zeng, Jun, Zhou, Kun
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Mechanical engineering; Multi-Jet-Fusion; Polyamide 12
• Online Access: https://hdl.handle.net/10356/170871
• Institution: Nanyang Technological University

Multi-Jet-Fusion (MJF) is a relatively new powder-based face-sintering additive manufacturing technique that exhibits great potential in high-volume manufacturing due to its rapid printing speed. Extensive research has been conducted on the mechanical properties of MJF-printed polyamide 12 (MJF PA12) by investigating the powder characteristics and build orientations. However, no work has been conducted to study the effect of humidity and physical ageing on the mechanical performance of MJF PA12 specimens. Here, the physical and mechanical properties of MJF PA12 specimens that were printed in different build orientations and stored under ambient and dryer conditions for 474 days were investigated. It was found that specimens stored under ambient conditions exhibited a drop in glass transition temperature due to moisture absorption followed by an increase, which could indicate that ageing of the specimens occurred. The moisture absorption was also found to have an insignificant effect on the crystallinity and crystallite size of the specimens. The change in mechanical properties, such as the tensile modulus and ultimate tensile strength, was more significant for specimens stored in ambient conditions than those in dryer conditions. It was also found that the build orientation does not have a significant impact on the moisture absorption rate, glass transition temperature, and the change in the trend of mechanical properties. In addition, an artificial neural network was adopted to predict the mechanical properties of the MJF PA12 specimens under the influence of physical ageing, humidity, and build orientations. The neural network adopted in this work can predict the change in the mechanical properties of differently-orientated MJF PA12 with different moisture content over time. The neural network prediction could be useful in providing guidance on the structure and risk assessment of the parts before print.