Machine Learning-Assisted Mitigation of Interlayer Heat Accumulation and Optimization of Forming Quality for Laser Powder Bed Fusions

• The sequence features of interlayer infrared radiation intensity (IIRI) in LPBF were defined. • A gated recurrent unit neural network model was developed to predict IIRI during LPBF process. • This prediction model exhibited a good applicability across different materials. • The DZ125 truncated-pyramid components were fabricated to validate the effectiveness of the model. Interlayer heat accumulation (IHA) is major challenge in the laser powder bed fusion (LPBF) process, as it exacerbates the instability of melt pools, and compromises the quality of the as-built samples. Infrared radiation monitoring is an effective method for exploring IHA. Based on the defined sequence features of interlayer infrared radiation intensity (IIRI), this study established a gated recurrent unit (GRU) neural network model for predicting IIRI in formed samples using machine learning to mitigate the IHA. The model trained on 316 L alloys achieved precise prediction results when transferred to the DZ125 superalloy, effectively managing various emergencies in the LPBF process. The truncated pyramid components were fabricated through parameter optimization based on IIRI prediction results. Compared with the non-optimized components, the CT results demonstrated a significant reduction in internal voids, with the relative density increasing from 91.6 % to 98.5 %. Additionally, surface roughness ( Ra ) decreased from 32.58 μm to 19.91 μm, while residual stress on the top surface was reduced from 169.21 MPa to 102.37 MPa.