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Probing smartphone-based photogrammetry for part profiling in wire arc directed energy deposition
(2025)
Fast dynamic part profiling during wire arc directed energy deposition (DED-Arc) is required to maintain the dimensional consistency of the fabricated part as it is printed over several overlapped tracks and successive layers. The metrology methods such as laser scanning, though accurate, are costly and less flexible for monitoring of layer-wise deposition. Photogrammetry is an optical measurement technique to reconstruct a 3D geometry using a series of 2D images in different orientations to ensure complete coverage. We propose here a novel attempt for rapid dimensioning of the deposit geometry using smartphone-based photogrammetry for wire arc directed energy deposition (DED-Arc). The recorded images from multiple viewpoints are used for feature extraction and matching and triangulation-based 3D reconstruction of the deposit geometry using open-source software. The reconstructed deposit surface is compared with the original CAD geometry to compare the dimensional consistency of parts during the DED-Arc process. Experimental validation is performed on both simple (cuboid) and complex (hollow cylindrical) aluminum geometries, as well as a steel deposit against laser scanning data. The results demonstrate that the smartphone-based photogrammetry can capture the layer-wise geometry variations with the maximum height deviations well within 15% of the laser scan measurements. Although reconstruction and post-processing times are slightly longer in photogrammetry, the approach provides a flexible, accessible, and cost-effective alternative for part profile monitoring.
A prior estimation of the temperature field and bead profile can help fabricate dimensionally consistent and structurally sound parts using wire arc directed energy deposition (DED-Arc). We present here a three-dimensional analytical heat transfer model with a volumetric heat source to compute the transient temperature field and melt pool dimensions for DED-Arc. The analytical model considers the thermal conductivity and volumetric heat capacity as a linear function of temperature. In contrast to assuming a pre-defined deposited track profile, the same is scaled from the analytically computed melt pool dimensions into the substrate. The computed deposit profiles of single and multiple tracks and layers are validated extensively with the corresponding experimentally measured results for a range of DED-Arc process conditions.
Wire arc directed energy deposition (DED-Arc) using a gas metal arc (GMA) welding power source is cited as DED-GMA that fabricates a part by layer-by-layer deposition of molten wire droplets along horizontal and out-of-position inclined trajectories. For the out-of-position trajectories, a smooth deposition of material is impaired by the gravitational force on the molten wire droplets, resulting in uneven and inconsistent deposit profiles. We present here a detailed experimental investigation to realize the effect of the out-of-position inclinations on the quality of the deposited structure for DED-GMA with a steel and an aluminium filler wire. The evolution of the droplet transfer and melt pool during the out-of-position inclined deposition is probed through high-speed videography at different baseplate angles and commonly used scanning strategies. An analytical model is proposed further based on force equilibrium analysis for a prior estimation of the out-of-position inclined deposit profile, which can help design dimensionally consistent and structurally sound parts using DED-GMA.
Gas metal arc assisted directed energy deposition (DED-GMA) is a metal additive manufacturing process for fabricating large-scale parts with a higher printing rate. An accurate monitoring and control of the melt pool geometric features is critical for printing zero-defect parts. In this study, the melt pool thermography is used for the real-time detection of the melt pool boundary, centreline, and transient cooling time using an efficient deep learning technique. The presented real-time process monitoring and control methodology using deep learning allows adaptive control of the DED-GMA process.
Automated in-situ synchronous monitoring and analysis of key process signatures during arc-based directed energy deposition (DED) process are the key challenges for layer-by-layer printing of large-scale parts. An attempt is presented here for real-time monitoring of process transients, deposit profile, and quantitative assessment of arc power, energy input and its influence on deposit dimensions. The workflow including setup, job generation and data analysis is fully automated in Python to allow large scale experiments with fast analysis results.
An easy-to-use methodology for a prior estimation of the overlapping track profile during wire arc directed energy deposition is in ever-demand to assess the dimensional consistency of the fabricated part. A novel analytical framework is proposed here to compute the cross-section of a deposited track, considering the spread of molten filler wire volume on a substrate until solidification. The final track cross-section is obtained as a function of the surface tension force, viscous force, and the contact angle between the liquid filler wire droplet and the substrate. The profile of multiple overlapping tracks is estimated further, considering the remelting of the adjacent tracks. The analytically computed build profiles are compared with the experimentally measured results for different process conditions. The model predicts the width and height of the single-track deposits with average errors of approximately 5% and 14%, respectively, while the multi-track widths are estimated with an error ranging between 2% to 9%. The proposed computational framework serves as a reliable mechanistic model for an effective design of the wire arc directed energy deposition process with improved part quality.
Wire arc directed energy deposition (DED-Arc) is an emerging metal additive manufacturing process to build near-net shaped metallic parts in a layer-by-layer with minimal material wastage. Automated in situ monitoring and fast-responsive analyses of process signatures and deposit profiles during DED-Arc are in ever demand to print dimensionally consistent parts and reduce post-deposition machining. A comprehensive experimental investigation is presented here involving real-time synchronous measurement of arc current, voltage, and the deposit profile using a novel multi-sensor monitoring framework integrated with the DED-Arc set-up. The recorded current–voltage transients are used to estimate the time-averaged arc power, and energy input in real time for an insight of the influence of wire feed rate and printing travel speed on the deposit characteristics. A unique attempt is made to represent the geometric profiles of the single-track deposits in a generalized mathematical form corresponding to a segmented ellipse, which has exhibited the minimum root-mean-square error of 0.03 mm. The dimensional inconsistency of multi-track deposits is evaluated quantitatively in terms of waviness using build profile monitoring and automated estimation, which is found to increase with an increase in step-over ratio and energy input. For the multi-track mild steel deposits, the suitable range of step-over ratio for the minimum surface waviness is observed to lie between 0.6 and 0.65. Collectively, the proposed framework of synchronized process monitoring and real-time analysis provides a pathway to achieve dimensionally consistent and defect-free parts, and highlights the potential for closed-loop control systems for a wider industrial application of DED-Arc.
Wire arc directed energy deposition (DED-Arc) is an emerging metal additive manufacturing process to build near-net shaped metallic parts in a layer-by-layer with minimal material wastage. Automated in situ monitoring and fast-responsive analyses of process signatures and deposit profiles during DED-Arc are in ever demand to print dimensionally consistent parts and reduce post-deposition machining. A comprehensive experimental investigation is presented here involving real-time synchronous measurement of arc current, voltage, and the deposit profile using a novel multi-sensor monitoring framework integrated with the DED-Arc set-up. The recorded current–voltage transients are used to estimate the time-averaged arc power, and energy input in real time for an insight of the influence of wire feed rate and printing travel speed on the deposit characteristics. A unique attempt is made to represent the geometric profiles of the single-track deposits in a generalized mathematical form corresponding to a segmented ellipse, which has exhibited the minimum root-mean-square error of 0.03 mm. The dimensional inconsistency of multi-track deposits is evaluated quantitatively in terms of waviness using build profile monitoring and automated estimation, which is found to increase with an increase in step-over ratio and energy input. For the multi-track mild steel deposits, the suitable range of step-over ratio for the minimum surface waviness is observed to lie between 0.6 and 0.65. Collectively, the proposed framework of synchronized process monitoring and real-time analysis provides a pathway to achieve dimensionally consistent and defect-free parts, and highlights the potential for closed-loop control systems for a wider industrial application of DED-Arc.
