Date of Award

Winter 3-1-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Nanosystems Engineering

First Advisor

Arden Moore

Abstract

While additive manufacturing (AM) is experiencing rapid growth, its development is uneven across different branches. Some areas are still emerging, while even the more established branches are still facing ongoing challenges that require further development. Regardless of their development stage, both emerging and mature AM require process monitoring and part characterization. Process monitoring helps to achieve more control over the process and build a self-adaptive system, while characterization of printed parts speeds up process optimization and ensures required quality. Together, process monitoring and build characterization will transform AM into a more dependable and commercially viable technique. Build surface temperature is a critical parameter for the majority of AM processes. For solid-state AM processes, build surface temperature helps with the evaluation of process-structure-property relations. For fused deposition AM processes, build melt pool temperature can facilitate the real-time detection of various printing defects. This study developed an in situ, multi-sensor approach for monitoring build temperature in which noncontact infrared temperature sensors with customized field of view move along with the moving print head and sense build temperature regardless of the print head’s X-Y translational movements. Different in situ infrared sensing implementations are facilitated in this work for two different categories of AM processes: a solid-state friction stir deposition AM, and a fused deposition modeling type AM. For metallic feedstock materials, a high-temperature calibration method was developed, while for polymer materials, a different and comparatively low-temperature calibration method has been developed. A statistical method for defect detection is also developed and utilized to identify temperature deviations caused by intentionally implemented defects. Effective detection of FDM print defects is demonstrated using both a simple L-shaped test geometry and a more complex industry standard test article. The effect of spindle speed and spindle torque on AFSD build temperature has been illustrated. Strengths and limitations of this approach are presented, and the potential for expansion via more advanced data analysis techniques such as machine learning are discussed. Thermal conductivity is a physical property that changes with material, microstructure, physical state, and scaling of material from bulk to micro/nanoscale. As microstructure evaluation is a common phenomenon in solid-state AM processes, it is essential to understand how microstructure affects the thermal transport properties in printed parts. This research develops a nanosecond thermoreflectance (NSTR) technique for thermal transport property characterization of bulk and thin films, which is then utilized to analyze transport property change in additive friction stir deposited layers. This work designs and constructs the NSTR setup and develops standard experimental procedures for thermoreflectance signal acquisition. A heat transfer model is also developed using a finite difference model to simulate the experimental conditions. The model finds the best fit of experimental data in order to extract thermal transport properties. Prediction of thermal penetration depth is also facilitated using the model. Finally, a combined analysis of thermal characterization and microstructure is presented to better understand the observed thermal conductivity change from substrate to print layers.

Download

Share

COinS