Development and processing of shape memory polymer composites (SMPCS) for application in structural robotics and robotic sensors.

Author

Kavish Sudan, University of LouisvilleFollow

Date on Master's Thesis/Doctoral Dissertation

8-2024

Document Type

Doctoral Dissertation

Degree Name

Ph. D.

Department

Mechanical Engineering

Degree Program

Mechanical Engineering, PhD

Committee Chair

Kate, Kunal

Committee Member

Popa, Dan

Committee Member

Bhatia, Bikram

Committee Member

Chitalia, Yash

Author's Keywords

3D printing; SMPCs; shape memory; carbon fiber; graphene; conductive; mechanical properties

Abstract

Shape Memory Polymers (SMPs) have attracted significant attention since their introduction in the 1980s due to their remarkable ability to regain their original shape from a temporarily deformed state when exposed to an external stimulus, typically heat. This shape memory effect, driven by thermal transitions such as the glass transition temperature (T_g) or melting temperature (T_m), has made SMPs highly attractive for applications in soft robotics, aerospace, and biomedical devices. However, SMPs face challenges such as limited mechanical strength, thermal stability, and electrical conductivity, which hinder their broader adoption in advanced applications. To address these challenges, traditional fabrication techniques like casting, molding, and extrusion have been used, but these methods often result in anisotropic mechanical properties and lack the precision required for complex geometries. 3D printing techniques such as Fused Deposition Modeling (FDM) has emerged as a superior alternative for fabricating SMPs due to its ability to produce complex shapes with high precision, layer-by-layer construction, and customization potential. FDM also allows for the integration of various additives, ensuring uniform distribution and enhancing the properties of SMPs. This dissertation explores the development, characterization, and application of advanced SMP composites, leveraging the capabilities of FDM to overcome existing limitations. This second chapter of the work focuses on the formulation and characterization of a novel three-part polymer system comprising Thermoplastic Polyurethane (TPU), Polycaprolactone (PCL), and Olefin Block Copolymer (OBC). This unique SMP feedstock is compounded at elevated temperatures, pelletized, and extruded into filament suitable for 3D printing. Comprehensive feedstock characterization includes viscosity, density, thermal properties, homogeneity, shape-changing glass transition temperature, and degradation temperature. Mechanical testing reveals the SMP’s impressive capability to endure strains of approximately 600%. Dynamic Mechanical Analysis (DMA) assesses the shape fixity and recovery ratios of 3D-printed parts, showing an ~87% shape recovery after 10 cycles and indicating potential for room temperature applications. The study also explores the force generated by the SMP at different strain levels, demonstrating its potential for use in actuators or deployable structures. The third chapter investigates the enhancement of SMP mechanical properties through the integration of carbon fibers to improve the material's robustness and expand its application range. Using a multifaceted approach, including Scanning Electron Microscopy (SEM) for filament morphology, tensile testing for mechanical strength, DMA for viscoelastic behavior, and Differential Scanning Calorimetry (DSC) for thermal properties, the study reveals substantial improvements in tensile strength and thermal stability. The incorporation of carbon fibers not only strengthens the SMP matrix but also enhances the composite’s mechanical robustness, making it suitable for advanced engineering applications requiring higher strength and thermal resistance. The fourth chapter extends the functionality of SMPs by incorporating graphene powder at varying concentrations (2, 4, 6, and 8 wt.%) to develop conductive SMP composites compatible with 3D printing. The preparation involves homogenizing the SMP and graphene blend in a torque rheometer, transforming the blend into pellets, and extruding it into filaments. These filaments retain structural integrity and dimensional stability post-graphene addition. Thermal characteristics are assessed using DSC and Thermogravimetric Analysis (TGA), confirming the preservation of the SMP's thermal properties. DMA evaluates the viscoelastic behavior, while electrical conductivity measurements show increased conductivity with rising graphene content. The percolation threshold is observed, highlighting the material's sensitivity and responsiveness. This research demonstrates the potential for developing multifunctional SMP composites that combine mechanical adaptability with adjustable electrical conductivity, paving the way for new applications in smart materials. In conclusion, this work advances the field of SMP applications by addressing key challenges through the development of multi-component SMP feedstocks, carbon fiber-reinforced composites, and graphene-enhanced conductive composites. The comprehensive characterization and innovative approaches presented in these studies provide valuable insights and pave the way for future advancements in smart material engineering, offering new solutions for a wide range of applications. This research significantly contributes to the understanding and enhancement of SMPs, positioning them as versatile materials with the potential to transform the landscape of material engineering.

Recommended Citation

Sudan, Kavish, "Development and processing of shape memory polymer composites (SMPCS) for application in structural robotics and robotic sensors." (2024). Electronic Theses and Dissertations. Paper 4462.
Retrieved from https://ir.library.louisville.edu/etd/4462