Publication
The Laser Power Bed Fusion (LPBF) process is of high interest to many industries, such as motors and vehicles, robotics, biomedical applications, aerospace, and others. LPBF workpieces can indeed achieve near full density and high resistance. However, a large amount of pore formation, in conjunction with the probabilistic nature of defect formation, results in a lack of process repeatability and reproducibility. This limits the range of industrial applications requiring high quality and defect free workpieces. To overcome this issue, we developed an acoustic monitoring system able to classify with high confidence three processing regimes (lack of fusion pores, conduction mode, keyhole pores) using a Convolution Neural Network (CNN). For the first time, we infer the processing regime based on AE waves produced during the LPBF process for conditions that are new and not part of the training database (>96%). The choice of processing conditions used in the database (training sets) is discussed in details, looking at the influence of their number, relative normalized distance, and position in the processing map on the classification accuracy. We found that the higher the number of processing conditions in the database, the higher the classification accuracies. Moreover, the higher the relative normalized "distance" between training and testing sets (measured in terms of laser speed and power), the lower the classification accuracies. Finally, the threshold defining the minimum number of training processing conditions is identified as eight to obtain a robust model able to identify the processing regimes for new laser parameters within the processing map. This number can be lowered to six if the training sets are in the surrounding region of the testing set. When one process parameter (speed, power, or normalized enthalpy) is constant between all the training and the testing sets, only four parameter sets allow a high classification accuracy (>88%). These results demonstrate the potential of in situ acoustic emission for monitoring the additive manufacturing process, in particular when the process conditions may deviate from the conduction mode. Finally, for a well-chosen set of training conditions, the model is able to construct a full processing map without additional experiments.