摘要： Laser cladding is a complex manufacturing process. As the laser beam melts the feedstock powder, small changes in laser power or traverse speed reflect on deviations of the deposition’s geometry. Thus, fine-tuning these process parameters is crucial to achieve desirable results. In order to monitor and further understand the laser cladding process, an automated method for clad bead final geometry estimation is proposed. To do so, six different convolutional neural network architectures were developed to analyze the process’ molten pool image acquired by a 50-fps coaxial camera. Those networks receive both the camera image and the process parameters as inputs, yielding width and height of the clad beads as outputs. The results of the network’s performances show testing error mean values as little as 8 μm for clad beads around a millimeter in height. For the width dimension, in 95% of the cases, the error remained under 15% of the bead’s width. Plots of the target versus the estimated values show coefficients of determination over 0.95 on the testing set. The architectures are then compared, and their performances are discussed. Deeper convolutional layers far exceeded the performance of shallower ones; nonetheless, deeper densely connected layers decreased the performances of the networks when compared with shallower ones. Those results represent yet another alternative on intelligent process monitoring with potential for real-time usage, taking the researches one step further into developing a closed-loop control for this process.