Prediction and Experimental Validation of Part Thermal History in the Fused Filament Fabrication Additive Manufacturing Process

Abstract

Part design and process parameters directly influence the instantaneous spatiotemporal distribution of temperature in parts made using additive manufacturing (AM) processes. The temporal evolution of temperature in AM parts is termed herein as the 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 toward this goal, the objectives of this work are two-fold. First, 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. Second, validate the model-derived thermal profiles with experimental in-process measurements of the temperature trends obtained under different material deposition speeds. In the specific context of FFF, this foray is the critical first-step toward 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 a mean absolute percentage error less than 6% (root mean squared error <6 °C). This work thus presents one of the first efforts in validating thermal profiles in FFF via direct in situ measurement of the temperature. In our future work, we will focus on predicting defects, such as delamination and inter-road porosity based on the thermal profile.

Six-Sigma Quality Management of Additive Manufacturing

“…Therefore, sensors, such as an infrared (IR) thermal camera, are intractable to be mounted near the nozzle to obtain the surface distribution. This is because a large surface of the part will be blocked by the nozzle as it translates over the part [39]. A similar argument is made for the material jetting process.…”

Feature Engineering for Surrogate Models of Consolidation Degree in Additive Manufacturing

“…More details on the model are included in our other work [ 18 ]. The model was experimentally validated in our previous study [ 6 ]. The data are used for the training and testing of the surrogate model.…”

“…However, they also come with an inherent burden of high computational cost that impedes any practical optimization of the process or in-situ control. The finite element models [ 1 , 2 , 3 , 4 , 5 , 6 ] of the thermally driven processes in additive manufacturing belong to this category. Current state-of-the-art physics-based models require more time to simulate the underlying processes than to physically print and test the specimen ( Figure 1 ).…”

Transient thermal finite-element analysis of fused filament fabrication process

“…Because the temperature variation is large, it is high time to simulate this phenomenon using temperature-dependent (nonlinear) thermal properties rather than constant properties. The numerical model would help in setting up the efficient process with optimized process variables, which in turn would also help in designing and selecting the material (Vanaei et al , 2020; Roy et al , 2019; Yin et al , 2018; Yang et al , 2017; Costa et al , 2014; Sun et al , 2003). It is also evident from the comparison and percentage difference results in the present study that this wide range of temperature in the FFF process demands the use of temperature-dependent (nonlinear) thermal properties to simulate the exact cooling process at the interfaces.…”