This review explores the evolution and current state of machine learning (ML) and artificial intelligence (AI) applications in direct energy deposition (DED) and wire arc additive manufacturing (WAAM) processes. A Python-based automated search script was developed to systematically retrieve relevant literature using the Crossref API, yielding around 370 papers published between 2010 and July 2025. The study identifies significant growth in ML-related DED research starting in 2020, with increasing adoption of advanced techniques such as deep learning, fuzzy logic, and hybrid physics-informed models. A year-by-year trend analysis is presented, and a comprehensive categorization of the literature is provided to highlight dominant application areas, including process optimization, real-time monitoring, defect detection, and melt pool prediction. Key challenges, such as limited closed-loop control, lack of generalization across systems, and insufficient modeling of deposition-location effects, are discussed. Finally, future research directions are outlined, emphasizing the need for integrated thermo-mechanical models, uncertainty quantification, and adaptive control strategies. This review serves as a resource for researchers aiming to advance intelligent control and predictive modeling in DED-based additive manufacturing.

Despite advances in computational power, the cost of time-accurate flows in axial compressor and fan stages with spatially non-uniform inflow is still too high for design-stage use in industry. Body force modeling reduces the computation time to practical levels, mainly by reducing the problem to a steady one. These computations are important to determine efficiency penalties associated with non-uniform inflows. Previous studies of body force methods have, in most cases, relied on computations with the presence of the blades to calibrate loss models. In some recent studies, uncalibrated models have been used, but such models can drop off in accuracy at conditions where separation would occur on the blade surfaces. In this paper, a neural-network-based loss model introduced in a recent paper by the authors is implemented for NASA rotor 67 for both uniform and non-uniform inflow conditions. For uniform inflow, the spanwise trend of entropy variation is generally captured with the new body force model. Although there are discrepancies at some span fractions, the present model generally predicts the compressor’s isentropic efficiency to within 3 $%$ compared to bladed Reynolds-averaged Navier–Stokes simulations. For non-uniform inflow, we consider a stagnation pressure profile representative of boundary layer ingestion. The results show that the region of maximum entropy generation is captured by the present model and the prediction of isentropic efficiency penalty due to the non-uniform inflow is only 0.2 points less than that determined from bladed computations.

Body force models of fans and compressors are widely employed for predicting performance due to the reduction in computational cost associated with their use, particularly in nonuniform inflows. Such models are generally divided into a portion responsible for flow turning and another for loss generation. Recently, accurate, uncalibrated turning force models have been developed, but accurate loss generation models have typically required calibration against higher fidelity computations (especially when flow separation occurs). In this paper, a blade profile loss model is introduced which requires the trailing edge boundary layer momentum thicknesses. To estimate the momentum thickness for a given blade section, an artificial neural network is trained using over 400,000 combinations of blade section shape and flow conditions. A blade-to-blade flow field solver is used to generate the training data. The model obtained depends only on blade geometry information and the local flow conditions, making its implementation in a typical computational fluid dynamics framework straightforward. We show good agreement in the prediction of profile loss for 2D cascades both on and off design in the defined ranges for the neural network training.