Abstract
Laser wire directed energy deposition (DED-LB/w) offers notable efficiency in metal additive manufacturing (AM), allowing rapid component fabrication with high material utilization. Despite its advantages, challenges such as anisotropy and uneven mechanical properties arise from unregulated heat application, highlighting the need for precise temperature control during deposition. This study evaluates the use of visible light images to predict melt pool temperature, leveraging Convolutional Neural Networks (CNN), Gaussian Process Regression (GPR), and Artificial Neural Networks (ANN). While CNN is trained directly on images, GPR and ANN utilize extracted features such as melt pool dimensions. The CNN model notably excels, achieving an R-squared value of 0.981, root mean square error of 44.46, and mean absolute percentage error of 2.74%, demonstrating the superior capability of visible light imaging in accurately predicting the melt pool temperature. This success illustrates the considerable potential of integrating predictive models with visible light imaging as a cost-effective alternative to traditional sensory systems. Such integration not only offers a pragmatic solution to the high costs and complexities associated with thermal imaging but also opens new avenues for combining other measurement techniques such as pyrometers to further enhance the prediction accuracy for AM process control. Future efforts will concentrate on implementing these models in real-time metal printing, aiming to enhance microstructure control and advance process automation.
