Modeling of Layer-wise Additive Manufacturing for Part Quality Prediction

Widespread adoption of Additive Manufacturing (AM) has remained a challenge due to part quality inconsistency. Using Fused Deposition Modeling as a representative AM process, this paper presents a deep learning technique termed Long-Short Term Memory for quantification of the nonlinear relationship between the printing process and part tensile strength. The presented modeling method takes into account the layer thermal history associated with the layer-wise printing process as well as the IR sensing data acquired online. Evaluation using Polylactide as the printing material has shown a 46% reduction in prediction error of part tensile strength achieved with the developed modeling method.