A review of WAAM for steel construction – Manufacturing, material and geometric properties, design, and future directions

Symmetric molten pool morphology is essential and fundamental in manufacturing three-dimensional (3D) components by wire-arc directed energy deposition (DED) method. However, when the molten metal droplets are deposited on non-level surfaces, various inclined substrates lead to undesirable molten pool behaviors, asymmetrical weld bead morphologies, or even formation of defects, which are mainly triggered by the gravity. Therefore, the evolution mechanism and suppression strategy of the asymmetric molten pool morphology on the inclined surface are necessary for the fabrication of 3D components. Herein, experimental and simulation analysis was carried out to reveal the evolutionary mechanism of molten pool and forming characteristics of wire-arc DED on various inclined surfaces, and a printing strategy was proposed to suppress the asymmetric molten pool morphology. First, a series of deposition experiments based on variable tilted substrates was conducted to evaluate the influence mechanism of weld bead morphology, and a characteristic parameter was introduced to quantitatively analyze the influence of inclination angle on the forming morphologies of weld beads. Second, in order to better comprehend the evolution of the asymmetric molten pool morphology, 3D numerical simulation and high-speed photography test were conducted to investigate the evolution of temperature field, velocity field, and fluid dynamics during the deposition on variable substrates. Finally, based on the theory of equilibrium of forces, a strategy was proposed to suppress the undesirable fluid flow pattern and eliminate the asymmetric morphology of single bead. Besides, for the multi-bead deposition process, this paper proposed an alternate printing strategy to alleviate asymmetric molten morphology and improve the forming quality. The feasibility of the proposed methods were verified via comparative quantitative evaluation. • Evolution mechanism of molten pool in wire-arc DED is systematically investigated. • Adverse effect of gravity on the mass transfer behavior of molten pool is revealed. • The influencing mechanisms of asymmetric morphology of molten pool are explored. • Printing strategies are proposed to eliminate the asymmetric weld bead morphology. 

Asymmetric molten pool morphology in wire-arc directed energy deposition: Evolution mechanism and suppression strategy

Additive manufacturing (AM) has gained extensive attention and tremendous research due to its advantages of fabricating complex-shaped parts without the need of casting mold. However, distortion is a known issue for many AM technologies, which decreases the precision of as-built parts. Like fusion welding, the local high-energy input generates residual stresses, which can adversely affect the fatigue performance of AM parts. To the best of the authors’ knowledge, a comprehensive review does not exist regarding the distortion and residual stresses dedicated for AM, despite some work has explored the interrelationship between the two. The present review is aimed to fill in the identified knowledge gap, by first describing the evolution of distortion and residual stresses for a range of AM processes, and second assessing their influencing factors. This allows us to elucidate their formation mechanisms from both the micro- and macro-scales. Moreover, approaches which have been successfully adopted to mitigate both the distortion and residual stresses are reviewed. It is anticipated that this review paper opens many opportunities to increase the success rate of AM parts by improving the dimension precision and fatigue life. 

A Review on Distortion and Residual Stress in Additive Manufacturing

Research on high efficiency deposition method of titanium alloy based on double-hot-wire arc additive manufacturing and heat treatment

Recent development in the Wire arc additive manufacturing (WAAM) provides a promising alternative for fabricating high value-added medium to large metal components for many industries such as aerospace and maritime industry. However, challenges stemming from the demand for increasingly complex and high-quality products, hinder the widespread adoption of the conventional WAAM method for manufacturing industries. The development of artificial intelligence (AI) techniques may provide new opportunities to upgrade WAAM to the next generation. Hence, this paper provides a comprehensive review of the state-of-the-art research on AI techniques in WAAM. Firstly, we proposed a novel concept of intelligent wire arc additive manufacturing (IWAAM) and revealed the challenges of developing IWAAM. Secondly, an overview of the research progress of applying AI techniques to several aspects of the WAAM process chain, including fabrication process pre-design, online deposition control and offline parameter optimization is provided. Thirdly, the relevant machine learning algorithms, and the knowledge of corresponding AI techniques, are also reviewed in detail. Through reviewing the current research articles, issues of applying AI techniques to the WAAM process are presented and analysed. Finally, future research perspectives in terms of novel AI technique applications and AI technique enhancement are discussed. Through this systematic review, it is expected that WAAM may gradually develop into a smart/intelligent manufacturing technology in the context of Industry 4.0 through the adoption of AI techniques. 

Research and application of artificial intelligence techniques for wire arc additive manufacturing: a state-of-the-art review

Coordinated monitoring and control method of deposited layer width and reinforcement in WAAM process

The invention relates to an arc additive manufacturing layer width active disturbance rejection control method based on deep learning. The method comprises the following steps: 1, collecting a moltenpool image and carrying out ROI selection, 2, performing molten pool image segmentation by adopting a segmentation network structure, 3, extracting the width of a weld joint, and 4, adjusting the welding current and controlling the width of a welding seam. According to the designed segmentation network EPNet, the width of the weld joint is determined, an active disturbance rejection control algorithm is designed, real-time control over the width of a welding pool is achieved, EPNet is based on an ERFNet network, a pyramid pooling module is added, multi-scale deep image features are fused, dataaugmentation is carried out on an original image in a data set and a corresponding label in morphology during training, and by combining an active disturbance rejection control algorithm, real-time control can be carried out according to the extracted width of the molten pool in the welding process, the control precision of the width of the molten pool is guaranteed, and a necessary strategy is provided for online monitoring and control over the width of a welding seam in the arc additive manufacturing welding process.

Xingwang Xu

Papers

Coordinated monitoring and control method of deposited layer width and reinforcement in WAAM process

Arc additive manufacturing layer width active disturbance rejection control method based on deep learning