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The material and damage behaviour of additively manufactured polyamide 12 under cyclic loading was characterized by cyclic tests and microstructure analysis by using microscopy, X-ray refraction, and computed tomography. The results were used to determine parameters for the viscoplastic material model by Chaboche and a damage model by Gurson-Tvergaard-Needleman. The temperature was monitored during the experiments and the self-heating effect was observed. By including this effect, a higher accuracy could be achieved with the results of mechanical experiments.
In order to characterise the material and damage behaviour of additively manufactured polyamide 12 (PA12) under quasi-static load and to implement it in a numerical model, experiments under quasi-static load as well as microstructural investigations were carried out. Selective laser sintering (SLS) was used as the manufacturing process. For the classification of the material behaviour, quasi-static cyclic tests with holding times as well as tensile tests were performed. X-ray refraction and computed tomography (CT) were used to investigate the damage behaviour. The Chaboche model, which has already been applied for metallic materials under thermomechanical loading, served as the basis for the selection of the numerical material model. The same procedure was used for the selection of the damage model, where the Gurson–Tvergaard–Needleman (GTN) model was chosen, which was already used for porous metallic materials. The Chaboche model shows very good agreement with experimental results. Furthermore, the coupling with the GTN model allows a very good modelling of the damage behaviour. Finally, it could be shown that the selected models are suitable to simulate the material and damage behaviour of 3D printed PA12.
The material and damage behaviour of additively manufactured polyamide 12 (PA12) under dynamic loading was characterized by cyclic tests and microstructure analysis. The results were used to develop a numerical material and damage model. In a recent study, it was shown that the material and damage behaviour of 3D printed PA12 under quasistatic loading is simulated in a realistic way by coupling the
material model by Chaboche and the damage model by Gurson-Tvergaard-Needleman (GTN).
Using microscopy, X-ray refraction, and computed tomography, a porosity of about 5% was evaluated. These results served as a starting point for the present work. For the dynamic load, both the previously used Chaboche model and the GTN model were extended. Furthermore, the temperature was measured during the experiment and the self-heating effect was observed. Therefore, a temperaturedependent material parameters for the simulation were introduced. Considering the results of mechanical experiments, microstructural investigations, and self-heating effects, a good agreement between Experiment and numerical simulation could be achieved.
The purpose of this research is to predict failure of a largely deformed pressure vessel. The employed Gurson-Tvergaard-Needleman (GTN) model enables a failure prediction through its in-built damage evolution law. A critical damage threshold defines the limit state of the evolution. Here, crack initiation is seen as the limit. Unfortunately, the GTN model does not capture void volume growth under shear stress and shows thermodynamic inconsistencies under pressure. To overcome these drawbacks and to make the model applicable to a wide range of complex stress states, a user-defined subroutine of the GTN model with an extension of its damage evolution law has been developed. The routine also accounts for large deformation which is advantageous for ductile vessel materials. The wall of a pressurised vessel is subjected to multi-axial stress states. Features, such as anges and valves, lead to even more complex stress states. The subroutine is used to determine the burst pressure and the location of failure. This research compares the results of the conventional and the modified GTN model with results of experimentally conducted burst tests. The conclusion of this research is that there is a clear improvement in failure prediction by using the modified GTN model.