Date of Award


Degree Name

Doctor of Philosophy




Tzu-Liang Tseng

Second Advisor

Yirong Lin


Shape memory polymers (SMPs) are classified as smart materials due to their inherent stimulus-induced response. SMP is capable of recovering its original shape from a high degree of deformation by applying an external stimulus such as thermal energy. Other stimuli are electricity, light, chemical, and magnetic field. Shape memory polymers (SMPs) and its fabrication process has recently attracted much attention as a result of their potential application as soft active materials. Demonstration of SMP systems fabricated via 3D printing technologies has been one of the most popular attempts. This Dissertation presents an integration of two commercial SMP materials (DiAPLEX and Tecoflex) and a material extrusion (ME) printer to fabricate SMP parts and specimens. In addition, a simple approach to 3D printing of carbon black (CB) based shape memory polymer nanocomposites (SMP/CB) with toughness improving capabilities during programing stage using electrical stimulus is reported. Conductive SMP/CB nanocomposites, consisting of commercial SMP filled with conductive CB nanoparticles, were fabricated using solvent casting and single screw extrusion processes. In similar research, the fabrication of shape memory polymer/ graphene oxide composites (SMP/GO) using digital light processing (DLP) 3D printing was performed and is presented. In order to achieve SMP/GO systems, it was required to evaluate the proper SMP material mixing (monomer, cross-linker, photo-initiator). It has been demonstrated that GO has high photo-thermal properties, which eventually could be used for shape memory triggering using a laser beam with specific wavelength. In addition, since dimensional accuracy has been an inherent challenge in 3D printing, an artificial neural network (ANN) was developed on Phyton, which models the dimensional error of parts produced by DLP process. The ANN, which is trained by using historical DLP process data, could be used to predict the dimensional error based on input parameters. Finally, an approach that focuses on performing dimensional evaluation of printed SMP parts using a 3D scanner is proposed. For instance, two algorithms for dimensional evaluation of 3D printed parts and recovered parts are presented. A dimensional accuracy error distribution was obtained by comparing the 3D scan file of the actual part with the CAD model. Another dimensional error distribution was acquired through evaluation of a SMP part through digital comparison of 3D scan files of a produced part and the recovered part.

The ME fabrication settings for the SMP specimens were defined by implementing a design of experiments (DOE) with temperature, velocity, and layer height as process variables. ME raster orientation factor was also evaluated separately. After fabrication, specimens were submitted to a thermo-mechanical cycle that encompasses tensile test, compact tension test, and a thermo-recovery process. On SMP/CB, material extrusion (ME) technique was used to 3D print dog bones type IV specimens for tensile test and electrical stimulus. On SMP/GO, it was required experimental trials to prove the compatibility of GO dispersed in the SMP photo-resin to be used in DLP process.

On pure SMPs fabrication, the material properties such as Young's modulus of the specimens was examined as a process output. Furthermore, stress-strain curve, strain recovery, instant shape-fixity ratio, long-term shape-fixity ratio, and recovery ratio of SMP specimens during a thermo-mechanical cycle were evaluated. Moreover, toughness property, maximum load, and load-displacement curves were investigated by using standard specimens to compact tension testing. Comparison studies of load-displacement, toughness and recovery efficiency of the specimens were carried out to determine the optimized fabrication parameters. On SMP/CB, temperature profiles at various electrical current levels, Young's modulus, and toughness results of the 3D printed specimens subjected and not subjected to electrical current were reported. On SMP/GO, mechanical properties of SMP parts, such as tensile-strength, and programing capacity of specimens using 3-point bending testing were characterized and reported.

It was found, according to main effect and iteration plots that fabrication parameters have an impact on SMP Young's modulus and exist minimum iteration among variables. In addition, Young's modulus variation of DiAPLEX and Tecoflex specimens was mostly caused by velocity and layer height parameters respectively. Moreover, results showed that SMP specimens were able to recover high levels of deformation. On SMP/CB composites, it was found that SMP/CB electrical conductivity can be tuned by the CB filler fraction, and that an electrical current passing through SMP/CB nanocomposites causes temperature increments and changes on material strength condition. Moreover, it was observed that conductive SMP/CB specimens responded to electrical current stimulus by increasing their toughness four times higher than with no current applied during tensile test. On SMP/ GO composites, during experimental trials, it was found that specimens support high degree of deformation and full shape recovery, and that factors such as layer thickness and exposure significantly vary dimensional characteristics on final parts. On ANN dimensional prediction, it was found that inner layer architecture with 2(number of inputs) + 1 neurons achieved the maximum Pearson correlation prediction with 77.7 %. Finally, respecting the dimensional accuracy quality evaluation, a case study is presented through comparison of a commercial SMP substance and a traditional acrylonitrile butadiene styrene (ABS) material. Computational results obtained conclude that SMP and ABS parts have a similar level of dimensional accuracy while the SMP parts have an overall high degree of recovery.

This study contributes to process control as well as for rheological, toughness, and recovery properties of SMP parts produced by ME fabrication process. In addition, it is a reference for conductive properties of SMP/CB nanocomposites fabricated by 3D printing process, and programing properties of photo-responsive SMP systems fabricated via DLP 3D printing technique. Finally, this research paves a way to investigate quality assessment in dimensional accuracy for 3D printed parts.




Received from ProQuest

File Size

108 pages

File Format


Rights Holder

Carlos Alejandro Garcia Rosales