Thermal Modeling in Metal Additive Manufacturing Using Graph Theory: Experimental Validation with Laser Powder Bed Fusion Using in Situ Infrared Thermography Data

The objective of this work is to provide experimental validation of the graph theory approach for predicting the thermal history of additively manufactured parts. The graph theory approach for thermal modeling in additive manufacturing (AM) was recently published in these transactions. In the present paper, the graph theory approach is validated with in situ infrared thermography data in the context of the laser powder bed fusion (LPBF) additive manufacturing process. We realize the foregoing objective through the following four tasks. First, two kinds of test shapes, namely, a cylinder and cone, are made in two separate builds on a production-type LPBF machine (Renishaw AM250); the material used for these tests is stainless steel (SAE 316L). The intent of both builds is to influence the thermal history of the part by controlling the cooling time between the melting of successive layers, called the interlayer cooling time (ILCT). Second, layer-wise thermal images of the top surface of the part are acquired using an in situ a priori calibrated infrared camera. Third, the thermal imaging data obtained during the two builds is used to validate the graph theory-predicted surface temperature trends. Fourth, the surface temperature trends predicted using graph theory are compared with results from finite element (FE) analysis. The results substantiate the computational advantages of the graph theory approach over finite element analysis. As an example, for the cylinder-shaped test part, the graph theory approach predicts the surface temperature trends to within 10% mean absolute percentage error (MAPE) and approximately 16 K root mean squared error (RMSE) relative to the surface temperature trends measured by the thermal camera. Furthermore, the graph theory-based temperature predictions are made in less than 65 min, which is substantially faster than the actual build time of 171 min. In comparison, for an identical level of resolution and prediction error, the finite element approach requires 175 min.