Directed Energy Deposition using a laser beam and metallic wire (directed energy deposition using a laser beam with wire (DED-LB/w)) is a promising additive manufacturing technique known for its high deposition rates, low material waste, and suitability for applications such as the repair of high-value components, rapid prototyping, and low-volume production. As a layered manufacturing process, it builds metal structures by melting and depositing wire in successive beads and layers. While DED-LB/w offers significant advantages, including potential cost and lead time reductions, especially when automated, its complex thermal and geometric dynamics present major challenges for process stability and part quality. Achieving consistent outcomes requires real-time, non-intrusive monitoring solutions capable of capturing critical process parameters, such as the temperature of the deposit, which significantly influences bead geometry, microstructure, and overall mechanical properties.

The setup used in this study consisted of a vision camera and a pyrometer, with the vision camera potentially replaceable by a pyrometer. The proposed measurement approach enables reliable estimation of the immediate solidification temperature and serves as a compact and flexible alternative to conventional pyrometers, which often suffer from a limited field of view and alignment challenges.

This leads to a solution that is industrially practical, low-cost, and robust, and that can be easily installed to existing DED-LB/w systems.   The results revealed the effectiveness of the proposed temperature estimation method, showing strong correlations between the melt pool’s top-surface length and the immediate solidification temperature. Temperature variations (increasing and decreasing, and how fast it reacts to the changes in laser power) during multi-layer deposition can be tracked using in-situ melt pool length measurements. Comparative analysis of the melt pool’s top-surface length and area revealed that melt pool length is a better representative of temperature variations, and it can be used for controlling energy input in multi-layer depositions in DED-LB/w.

In another set of experiments, the influence of preheat temperature on melt pool behavior, melt pool-wire interaction, and microstructure of as-deposited beads was presented, using vision and high-speed cameras and resistive preheating of the feedstock wire. The results of these experiments showed that higher preheat temperatures reduce the thermal gradient between the melt pool and substrate, resulting in larger melt pool volume. Beyond a threshold around 400°C preheat temperature melt pool width showed a gradual increase, larger melt pool volumes resulted in bigger cross-section of liquid bridge and better flow of free electrons from wire to melt pool therefore a jump in electrical conductance values. Higher preheat temperatures shortened and thickened the liquid bridge. A hotter substrate reduces the temperature difference between melt pool and incoming wire, enabling faster and more uniform melting upon contact. The wire reaches melting conditions over a shorter distance while the sustained molten state maintains a thicker transfer region with greater metal flow. The bead cross-section measurements showed a sharp increase in depth and total area of the beads. Microstructural analysis revealed coarser solidification features at higher preheat temperatures caused by reduced cooling rates.

Overall, this work contributes to the advancement of in-situ monitoring in DED-LB/w by demonstrating the feasibility and benefits of vision-based temperature estimation and the suitability of electrical sensing as a feedback variable for closed-loop control of heat input. By quantifying the effects of thermal history and validating predictive models, the study lays the foundation for future development of closed-loop control strategies in wire-based additive manufacturing.

Laser energy absorption plays an important role in determining process stability and energy efficiency in wire Directed Energy Deposition using a laser beam. However, the combined influence of wire geometry, beam positioning, and temperature-dependent optical properties on absorbed laser power remains insufficiently understood. This work presents an analytical framework for predicting the absorptance and absorbed power during the processing of Inconel 718. The model combines Monte Carlo ray tracing, Fresnel reflection theory, and a temperature-dependent Drude formulation in which the optical properties are related to electrical resistivity. A parametric investigation was conducted to evaluate the effects of wire-feeding angle, beam-wire offset, melt pool temperature, and wire temperature. The results showed that increasing the wire feeding angle altered the incidence-angle distribution of laser rays on the wire surface, resulting in non-monotonic variations in wire absorptance and absorbed power despite increasing beam interception. Furthermore, the interaction between wire and melt pool temperatures revealed three operating regimes governing the response of absorbed power to beam-wire offset. When the wire absorptance was lower than that of the melt pool, absorbed power increased with increasing offset, whereas the opposite trend occurred when the wire absorptance exceeded that of the melt pool. Between these regimes, a compensation condition was identified in which absorbed power remained nearly independent of beam-wire offset. This behavior occurred when the wire absorptance approached that of the melt pool, minimizing the influence of laser-energy redistribution between both surfaces. For the investigated geometry and optical assumptions, the compensation condition was associated with an approximately constant electrical-resistivity ratio and a constant relationship between the compensation wire temperature and melt pool temperature.

Directed energy deposition using laser beam and wire feed is a critical technology in high-productivity metal additive manufacturing, enabling precise deposition and fabrication of components with high quality and deposition rates. The interaction between the laser beam and metal is highly temperature-sensitive, affecting metal transfer, melting, solidification, and phase transformations. Effective control of heat input is crucial for influencing the geometry, microstructure, and overall integrity of the fabricated parts.

This study investigates the correlation between the melt pool top surface boundary contour and the immediate solidification temperature, utilizing machine vision and radiation pyrometer measurements. The research highlights the significance of melt pool length as an indicator of temperature variations during deposition. System identification techniques were employed to develop a predictive model of the dynamic relationship between melt pool length and temperature, aimed at enhancing process control strategies. Our approach substitutes off-axis pyrometry with a cost-effective coaxial vision camera integrated into the laser head. By incorporating a vision camera without active illumination, the study offers a compact, omnidirectional solution. Comparative analysis of melt pool length and area measurements demonstrates the superiority of length-based correlations with temperature data.

This work addresses the challenges of heat-input control in directed energy deposition laser beam wire processes and suggests future research directions in various metal alloys, complex geometries, and advanced sensing and automatic control methodologies.

Effective thermal management is essential in metal additive manufacturing to ensure process stability and desirable material properties. Directed energy deposition using a laserbeam and wire (DED-LB/w) enables the production of large, high-performance components but remains sensitive to adverse thermal effects during multilayer deposition due to heat accumulation. While prior studies have investigated interlayer temperature control and substrate preheating in DED modalities, including laser-powder and arc-based systems, the influence of substrate preheating in DED-LB/w has not been thoroughly examined.

This study employs substrate preheating to simulate heat accumulation and assess its effects on melt pool geometry, wire–melt pool interaction, and the microstructural evolution of Alloy 718. Experimental results demonstrate that increased substrate temperatures lead to a gradual expansion of the melt pool, with a notable transition occurring beyond 400 ◦C. Microstructural analysis reveals that elevated preheat temperatures promote coarser secondary dendrite arm spacing and the development of wider columnar grains. Moreover, Nb-rich secondary phases, including the Laves phase, exhibit increased size but relatively unchanged area fractions. Observations from electrical conductance measurements and coaxial visual imaging show that preheat temperature significantly affects the process dynamics and microstructural evolution, providing a basis for advanced process control strategies.

Accurate temperature estimation is crucial in metal additive manufacturing ensuring component quality and process efficiency. This study introduces the use of Convolutional Neural Networks (CNNs), namely MobileNet and ResNet, to predict temperatures directly from melt pool images without the need for extensive preprocessing. Through comparative analysis, MobileNet demonstrated superior performance over ResNet, achieving a mean absolute error of 0.0562 and a correlation coefficient of 0.9900. These findings underscore the effectiveness of CNNs in real-time temperature prediction tasks within Laser-Directed Energy Deposition with wire (DED-LB/w), highlighting significant advancements and setting the stage for further technological enhancements.

Hot-wire directed energy deposition using a laser beam (DED-LB/w) is a method of metal additive manufacturing (AM) that has benefits of high material utilization and deposition rate, but parts manufactured by DED-LB/w suffer from a substantial heat input and undesired surface finish. Hence, regulating the process parameters and monitoring the process signatures to control the final quality during the deposition is crucial to ensure the quality of the final part. This paper explores the dynamic modeling of the DED-LB/w process and introduces a parameter-signature-quality modeling and control approach to enhance the quality of modeling and control of part qualities that cannot be measured in situ. The study investigates different process parameters that influence the melt pool width (signature) and bead width (quality) in single and multi-layer beads. The proposed modeling approach utilizes a parameter-signature model as F1 and a signature-quality model as F2. Linear and nonlinear modeling approaches are compared to describe a dynamic relationship between process parameters and a process signature, the melt pool width (F1). A fully connected artificial neural network is employed to model and predict the final part quality, i.e., bead width, based on melt pool signatures (F2). Finally, the effectiveness and usefulness of the proposed parameter-signature-quality modeling is tested and verified by integrating the parameter-signature (F1) and signature-quality (F2) models in the closed-loop control of the width of the part. Compared with the control loop with only F1, the proposed method shows clear advantages and bears potential to be applied to control other part qualities that cannot be directly measured or monitored in situ.