Methodology for evaluation of long-term mechanical properties of viscoelastic materials

Abstract: Additive manufacturing (AM) enables the fabrication of complex geometries using layer-by-layerdeposition of materials like polymers and metals. However, predicting the long-term viscoelasticcharacteristics of AM polymer parts is challenging due to limited available data. This thesisinvestigates the applicability of time-temperature superposition (TTS) to forecast mechanicalbehaviour over extended timescales beyond experimentally feasible testing durations.Comprehensive stress relaxation testing was systematically conducted on 3D-printed polylacticacid (PLA) samples across a temperature range of 26°C to 34°C using a customised climatechamber. The results demonstrated an increasing rate and extent of stress relaxation at highertemperatures, confirming the viscoelasticity of the printed PLA. Leveraging Python programmingand the Williams-Landel-Ferry (WLF) equation, the individual stress relaxation curves at differenttemperatures were shifted to create master curves. By quantitatively determining the shift factors,a continuous prediction of viscoelastic properties spanning much longer time durations wasachieved. Experimental validation through prolonged relaxation testing showed reasonableagreement between the actual mechanical data and the model predictions, thereby validating thecapability of TTS in expanding the behavioural forecast. This research provides a robustframework and methodology for the accelerated determination of time-dependent viscoelasticcharacteristics of polymers fabricated through additive manufacturing. The computational toolsand experimental techniques established here can be extended to other AM materials likecomposites and metal alloys. Overall, this thesis demonstrates a methodology for timetemperaturesuperposition as a viable and powerful tool for developing comprehensive predictivemodels of the long-term mechanical performance of viscoelastic material.