Cold wire gas metal arc (CWGMA) additive manufacturing (AM) is more productive and beneficial than the common electric arc processes currently used in wire arc additive manufacturing (WAAM). Adding a non-energised wire to the gas metal arc (GMA) system makes it possible to overcome a process limitation and decouple the energy input from the material feed rate. Two novel process control methods were proposed, namely, arc power and travel speed control, which can keep the required geometry accuracy in WAAM through a broad range of thermal conditions. The reinforcement area of the bead is kept constant with accurate control over the height and width while still reducing the energy input to the substrate; decreasing penetration depth, remelting, and the heat-affected zone (HAZ); and reaching a dilution lower than 10%. This work also presents improved productivity compared to all the other single-arc energy-based processes with a demonstrator part built using 9.57 kg h⁻¹ with CWGMA AM.

Wire-based hybrid arc-laser additive manufacturing is suitable for producing large metallic parts (metres in scale) with high deposition rates and near-net-shape. In this process, the surface quality and dimensional accuracy of the deposited parts are determined by the melt pool geometry. However, how to control the melt pool in the hybrid process is complex due to the multiple parameters that can be used. In this study, control of melt pool geometry by investigating different process parameters, including laser power, travel direction, arc-laser separation distance, laser beam size, and arc current in the hybrid plasma transferred arc (PTA)-laser process, was studied systematically. It was found that a larger melt pool was achieved with the PTA-leading configuration compared to that with the laser-leading configuration due to a higher laser absorption occurred with the former configuration. The melt pool was enlarged by either increasing the laser power or arc current due to the increased energy input. However, if the laser power density is high enough to determine the melt pool depth, the increasing arc current has very little effect on melt pool depth but only increases the melt pool width. In addition, the melt pool became shallower and wider when using a larger laser beam. The arc-laser separation distance had a minor effect on the melt pool geometry due to the fixed energy input used in the studied cases. The results of this study provide a reference for melt pool control in wire-based hybrid arc-laser additive manufacturing.

Wire arc additive manufacturing (WAAM) is suitable for building large-scale engineering structures with high deposition rates and relatively low costs. However, in a typical plasma transferred arc (PTA)–based WAAM process using an inclined wire and vertical torch, keyhole defects can occur due to the high arc pressure, and the process is sensitive to the wire-feeding position with respect to the workpiece. Therefore, in this study, a PTA-based WAAM process with a new configuration employing a vertical wire and an inclined plasma torch was investigated for the potential of mitigation of keyhole formation and improvement of process tolerance. In particular, detailed investigations were carried out on the metal transfer mechanisms and bead formation characteristics under various processing conditions. The results show that the new configuration significantly reduces the likelihood of keyhole formation compared with the conventional approach due to the changes in arc pressure and heat distribution. Systematic analysis reveals that process parameters, including wire feed speed, arc current, and plasma gas flow rate, strongly influence droplet transfer stability, melt pool dynamics, and final bead morphology, which offer guidance for future process optimisation.

Process control and quality assurance of wire + arc additive manufacturing (WAAM) and automated welding rely heavily on in-process monitoring videos to quantify variables such as melt pool geometry, location and size of droplet transfer, arc characteristics, etc. To enable feedback control based upon this information, an automatic and robust segmentation method for monitoring of videos and images is required. However, video segmentation in WAAM and welding is challenging due to constantly fluctuating arc brightness, which varies with deposition and welding configurations. Additionally, conventional computer vision algorithms based on greyscale value and gradient lack flexibility and robustness in this scenario. Deep learning offers a promising approach to WAAM video segmentation; however, the prohibitive time and cost associated with creating a well-labelled, suitably sized dataset have hindered its widespread adoption. The emergence of large computer vision models, however, has provided new solutions. In this study a semi-automatic annotation tool for WAAM videos was developed based upon the computer vision foundation model SAM and the video object tracking model XMem. The tool can enable annotation of the video frames hundreds of times faster than traditional manual annotation methods, thus making it possible to achieve rapid quantitative analysis of WAAM and welding videos with minimal user intervention. To demonstrate the effectiveness of the tool, three cases are demonstrated: online wire position closed-loop control, droplet transfer behaviour analysis, and assembling a dataset for dedicated deep learning segmentation models. This work provides a broader perspective on how to exploit large models in WAAM and weld deposits.

Part quality monitoring and control in wire-based directed energy deposition additive manufacturing (w-DEDAM) processes has been garnering continuous interest from both the academic and industrial sectors. However, maintaining a consistent layer height and ensuring that the wall height aligns closely with the design, as depicted in computer-aided design (CAD) models, pose significant challenges. These challenges arise due to the uncertainties associated with the manufacturing process and the working environment, particularly with extended processing times. To achieve these goals in an industrial scenario, the deposition geometry must be measured with precision and efficiency throughout the part-building process. Moreover, it is essential to comprehend the changes in the interlayer deposition height based on various process parameters. This paper first examines the behaviour of interlayer deposition height when process parameters change within different wall regions, with a particular focus on the transition areas. In addition, this paper explores the potential of geometry monitoring information in implementing interlayer wall height compensation during w-DEDAM part-building. The in-process layer height was monitored using a coherent range-resolved interferometry (RRI) sensor, and the accuracy and efficiency of this measurement were carefully studied. Leveraging this information and understanding of deposition geometry, the control points of the process parameters were identified. Subsequently, appropriate and varied process parameters were applied to each wall region to gradually compensate for wall height. The wall height discrepancies were generally compensated for in two to three layers.