Abstract

Part design and process parameters directly influence the spatiotemporal distribution of temperature and associated heat transfer in parts made using additive manufacturing (AM) processes. The temporal evolution of temperature in AM parts is termed herein as thermal profile or thermal history. The thermal profile of the part, in turn, governs the formation of defects, such as porosity and shape distortion. Accordingly, the goal of this work is to understand the effect of the process parameters and the geometry on the thermal profile in AM parts. As a step towards this goal, the objectives of this work are two-fold: (1) to develop and apply a finite element-based framework that captures the transient thermal phenomena in the fused filament fabrication (FFF) additive manufacturing of acrylonitrile butadiene styrene (ABS) parts, and (2) validate the model-derived thermal profiles with experimental in-process measurements of the temperature trends obtained under different feed rate settings (viz., the translation velocity, also called scan speed or deposition speed, of the extruder on the FFF machine). In the specific context of FFF, this foray is the critical first-step towards understanding how and why the thermal profile directly affects the degree of bonding between adjacent roads (linear track of deposited material), which in turn determines the strength of the part, as well as, propensity to form defects, such as delamination. From the experimental validation perspective, we instrumented a Hyrel Hydra FFF machine with three non-contact infrared temperature sensors (thermocouples) located near the nozzle (extruder) of the machine. These sensors measure the surface temperature of a road as it is deposited. Test parts are printed under three different settings of feed rate, and subsequently, the temperature profiles acquired from the infrared thermocouples are juxtaposed against the model-derived temperature profiles. Comparison of the experimental and model-derived thermal profiles confirms a high-degree of correlation therein, with maximum absolute error less than 10%. This work thus presents one of the first efforts in validation of thermal profiles in FFF via in-process sensing. In our future work, we will focus on predicting defects, such as delamination and inter-road porosity based on the thermal profile.

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