Predicting Defects in Laser Powder Bed Fusion using in-situ Thermal Imaging Data and Machine Learning
This work addresses defect prediction in additive manufacturing to improve quality control, but it is incremental as it builds on existing sensing and ML approaches for a specific domain.
The authors tackled the problem of predicting microporosity defects in laser powder bed fusion additive manufacturing by developing machine learning models that use in-situ thermal imaging data, achieving F1 scores above 0.96 with random forests. They identified key thermal features and incorporated neighbor voxel data to improve accuracy based on thermal transport physics.
Variation in the local thermal history during the laser powder bed fusion (LPBF) process in additive manufacturing (AM) can cause microporosity defects. in-situ sensing has been proposed to monitor the AM process to minimize defects, but the success requires establishing a quantitative relationship between the sensing data and the porosity, which is especially challenging for a large number of variables and computationally costly. In this work, we develop machine learning (ML) models that can use in-situ thermographic data to predict the microporosity of LPBF stainless steel materials. This work considers two identified key features from the thermal histories: the time above the apparent melting threshold (/tau) and the maximum radiance (T_{max}). These features are computed, stored for each voxel in the built material, are used as inputs. The binary state of each voxel, either defective or normal, is the output. Different ML models are trained and tested for the binary classification task. In addition to using the thermal features of each voxel to predict its own state, the thermal features of neighboring voxels are also included as inputs. This is shown to improve the prediction accuracy, which is consistent with thermal transport physics around each voxel contributing to its final state. Among the models trained, the F1 scores on test sets reach above 0.96 for random forests. Feature importance analysis based on the ML models shows that T_{max}is more important to the voxel state than /tau. The analysis also finds that the thermal history of the voxels above the present voxel is more influential than those beneath it.