Modeling of polymeric materials with applications in active materials and additive manufacturing

Active materials;3D printing;Machine learning;Additive manufacturing;Photopolymerization;Polymer dissolution

Hamel, Craig Michael ; Qi, H. J. Mechanical Engineering Zhou, Min Hu, Yuhang Jacob, Karl I. Long, Kevin N. ; Qi, H. J.

University:Georgia Institute of Technology

Department:Mechanical Engineering

关键词: Active materials;    3D printing;    Machine learning;    Additive manufacturing;    Photopolymerization;    Polymer dissolution;   

Others  :  https://smartech.gatech.edu/bitstream/1853/63592/1/HAMEL-DISSERTATION-2020.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Polymeric materials used in the settings of active materials and additive manufacturing offer unique challenges for which high fidelity multiphysics modeling would greatly assist in improving processing and component function during service. In recent years there have been many advances in polymeric materials that offer exciting applications such as 3D printing, actuation, and recyclability. With these advances, however, there has been a scarcity of physics driven models to accompany the three applications listed above. Actuation is now possible with many different physical mechanisms due to advances in material synthesis but the coupling of large deformation of these solid materials with physical actuation mechanisms is a vast area of research. This modeling approach is further complicated if one wishes to use these models in a design for 4D printing for example. Modeling of the 3D printing process is also needed to gain further understanding into how processing parameters such as 3D printing ink formulations, printing speed, and others play a role in the geometrical tolerances of printed parts along with the effects these parameters have upon the material properties after manufacturing. Recycling of polymeric materials could also greatly benefit from model driven optimization of processes. With a robust enough model, “virtual” experiments of new recycling methods could be conducted which would greatly reduce both the consumption of time and experiments needed for streamlining a new recycling process. The objective of this dissertation is to evaluate the use of high-fidelity material models of polymeric materials for applications in active materials and additive manufacturing settings. The general goals are to build models for various stages a polymer sees during its lifecycle in the application of 3D printing. Specifically, the problems that will be considered are the dissolution of polymeric materials, free radical diffusion in photopolymers during curing in digital light processing (DLP) 3D printing, and design of active structures for 4D printing. For the case of dissolution in polymers, a reaction-diffusion model will be presented and its effectiveness at predicting the dissolution process and will be applied to specialty grade polymers known as vitrimers. The model will enable the streamlining of new recycling processes without the need for a large suite of experimental methods through the use of process simulation. In thecase of free radical diffusion in photopolymers in the DLP process a reaction-diffusion model will be presented that can assess the evolution of functional groups at the pixel level and can directly predict the geometric distortion with reasonable agreement to experiments. This work enables further process modeling but in this case the process is manufacturing rather than recycling. Again, type of model can be utilized for optimizing printing parameters such as the light intensity and resin composition to improve geometric tolerance in the future. Design of active structures for 4D printing utilizes non-linear constitutive models coupled with machine learning techniques to determine an ideal material distribution to achieve a pre-defined target shape upon activation from an environmental stimulus. This work will enable the use of high-fidelity physics models in design for 4D printing.