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Directed energy deposition (DED) additive manufacturing (AM) enables the production of components at a high deposition rate. For certain alloys, interpass temperature requirements are imposed to control heat accumulation and microstructure transformation, as well as to minimize distortion under varying thermal conditions. A typical strategy to comply with interpass temperature constraints is to increase the interpass dwell time, which can lead to an increase in the total deposition time. This study aims to develop an optimized tool path that ensures interpass temperature compliance and reduces overall deposition time relative to the conventional sequential deposition path during the DED process. To evaluate this, a compact analytic thermal model is used to predict the thermal history during laser-based directed energy deposition (DED-LB/M) hot wire (lateral feeding) of ER100S-G, a welding wire equivalent to high yield steel. A greedy algorithm, integrated with the thermal model, identifies a tool path order that ensures compliance with the interpass requirement of the material while minimizing interpass dwell time and, thus, the total deposition time. The proposed path planning algorithm is validated experimentally with in situ temperature measurements comparing parts fabricated with the baseline (sequential) deposition path to the modified path (resulting from the greedy algorithm). The experimental results of this study demonstrate that the proposed path planning algorithm can reduce the deposition time by 9.2% for parts of dimensions 66 mm × 73 mm × 16.5 mm, comprising 15 layers and a total of 300 beads. Predictions based on the proposed path planning algorithm indicate that additional reductions in deposition time can be achieved for larger parts. Specifically, increasing the (experimentally validated) part dimension perpendicular to the deposition direction by five-times is expected to result in a 40% reduction in deposition time. 

Laser Hot Wire (LHW) Directed Energy Deposition (DED) Additive Manufacturing (AM) processes are capable of manufacturing parts with a high deposition rate. There is a growing research interest in replacing large cast Nickel Aluminum Bronze (NAB) components using LHW DED processes for maritime applications. Understanding thermomechanical behavior during LHW DED of NAB is a critical step towards the production of high-quality NAB parts with desired performance and properties. In this paper, finite element simulations are first used to predict the thermomechanical time histories during LHW DED of NAB test coupons with an increasing geometric complexity, including single-layer and multilayer depositions. Simulation results are experimentally validated through in situ measurements of temperatures at multiple locations in the substrate as well as displacement at the free end of the substrate during and immediately following the deposition process. The results in this paper demonstrate that the finite element predictions have good agreement with the experimental measurements of both temperature and distortion history. The maximum prediction error for temperature is 5% for single-layer samples and 6% for multilayer samples, while the distortion prediction error is about 12% for single-layer samples and less than 4% for multilayer samples. In addition, this study shows the effectiveness of including a stress relaxation temperature at 500 °C during FE modeling to allow for better prediction of the low cross-layer accumulation of distortion in multilayer deposition of NAB. 

The goal of this work is to quantify the link between the design features (geometry), in-situ process sensor signatures, and build quality of parts made using laser powder bed fusion (LPBF) additive manufacturing (AM) process. This knowledge is critical for establishing design rules for AM parts, and to detecting impending build failures using in-process sensor data. As a step towards this goal, the objectives of this work are two-fold: Quantify the effect of the geometry and orientation on the build quality of thin-wall features. To explain further, the geometry-related factor is the ratio of the length of a thin-wall (l) to its thickness (t) defined as the aspect ratio (length-to-thickness ratio, l/t), and the angular orientation (θ) of the part, which is defined as the angle of the part in the X-Y plane relative to the re-coater blade of the LPBF machine. Assess the thin-wall build quality by analyzing images of the part obtained at each layer from an in-situ optical camera using a convolutional neural network. To realize these objectives, we designed a test part with a set of thin-wall features (fins) with varying aspect ratio from Titanium alloy (Ti-6Al-4V) material — the aspect ratio l/t of the thin-walls ranges from 36 to 183 (11 mm long (constant), and 0.06 mm to 0.3 mm in thickness). These thin-wall test parts were built under three angular orientations of 0°, 60°, and 90°. Further, the parts were examined offline using X-ray computed tomography (XCT). Through the offline XCT data, the build quality of the thin-wall features in terms of their geometric integrity is quantified as a function of the aspect ratio and orientation angle, which suggests a set of design guidelines for building thin-wall structures with LPBF. To monitor the quality of the thin-wall, in-process images of the top surface of the powder bed were acquired at each layer during the build process. The optical images are correlated with the post build quantitative measurements of the thin-wall through a deep learning convolutional neural network (CNN). The statistical correlation (Pearson coefficient, ρ) between the offline XCT measured thin-wall quality, and CNN predicted measurement ranges from 80% to 98%. Consequently, the impending poor quality of a thin-wall is captured from in-situ process data.