Additive manufacturing has revolutionized the manufacturing paradigm in recent years due to the possibility of creating complex shaped three-dimensional parts which can be difficult or impossible to obtain by conventional manufacturing processes. Among the different additive manufacturing techniques, wire and arc additive manufacturing (WAAM) is suitable to produce large metallic parts owing to the high deposition rates achieved, which are significantly larger than powder-bed techniques, for example. The interest in WAAM is steadily increasing, and consequently, significant research efforts are underway. This review paper aims to provide an overview of the most significant achievements in WAAM, highlighting process developments and variants to control the microstructure, mechanical properties, and defect generation in the as-built parts; the most relevant engineering materials used; the main deposition strategies adopted to minimize residual stresses and the effect of post-processing heat treatments to improve the mechanical properties of the parts. An important aspect that still hinders this technology is certification and nondestructive testing of the parts, and this is discussed. Finally, a general perspective of future advancements is presented.

Current Status and Perspectives on Wire and Arc Additive Manufacturing (WAAM)

Additive manufacturing enables the printing of metallic parts, such as customized implants for patients, durable single-crystal parts for use in harsh environments, and the printing of parts with site-specific chemical compositions and properties from 3D designs. However, the selection of alloys, printing processes and process variables results in an exceptional diversity of microstructures, properties and defects that affect the serviceability of the printed parts. Control of these attributes using the rich knowledge base of metallurgy remains a challenge because of the complexity of the printing process. Transforming 3D designs created in the virtual world into high-quality products in the physical world needs a new methodology not commonly used in traditional manufacturing. Rapidly developing powerful digital tools such as mechanistic models and machine learning, when combined with the knowledge base of metallurgy, have the potential to shape the future of metal printing. Starting from product design to process planning and process monitoring and control, these tools can help improve microstructure and properties, mitigate defects, automate part inspection and accelerate part qualification. Here, we examine advances in metal printing focusing on metallurgy, as well as the use of mechanistic models and machine learning and the role they play in the expansion of the additive manufacturing of metals. Several key industries routinely use metal printing to make complex parts that are difficult to produce by conventional manufacturing. Here, we show that a synergistic combination of metallurgy, mechanistic models and machine learning is driving the continued growth of metal printing.

Metallurgy, mechanistic models and machine learning in metal printing

A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system

Directed energy deposition (DED) is an important additive manufacturing method for producing or repairing high-end and high-value equipment Meanwhile, the lack of reliable and uniform qualities is a key problem in DED applications With the development of sensing devices and control systems, in situ monitoring (IM) and adaptive control (IMAC) technology is an effective method to enhance the reliability and repeatability of DED In this paper, we review current IM technologies in IMAC for metal DED First, this paper describes the important sensing signals and equipment to exhibit the research status in detail Meanwhile, common problems that arise when gathering these signals and resolvent methods are presented Second, process signatures obtained from sensing signals and transfer approaches from sensing signals for processing signatures are shown Third, this work reviews the developments of the IM of product qualities and illustrates ways to realize quality monitoring Lastly, this paper specifies the main existing problems and future research of IM in metal DED

A review on in situ monitoring technology for directed energy deposition of metals

Additive manufacturing (AM) technology, in other words “layered manufacturing” or “3D printer technology” has been developing rapidly in recent years. Unlike the traditional manufacturing method (TM), the working principle of AM technology is to create layer-based production by deposition the layers on top of each other. Owing to its advantages such as material saving, lower cost, the ability to produce parts without the need for molds and the design flexibility in complex shaped parts, it has brought a breath of fresh air to the areas where it is used primarily medical, aerospace and automotive. However, the parts produced by AM method have dimensional limitations. According to recent studies, in order to eliminate this problem, metal materials produced with AM can be combined with commonly used by different welding methods so that large parts can be obtained. In this study, these welding methods are explained and recent researches are examined. AM technology and methods are introduced. The usage areas of the method are described. In addition, the welding parameters and the effects of this new method on the mechanical properties and microstructures are investigated.

Additive manufacturing method and different welding applications

Quality monitoring in wire-arc additive manufacturing based on cooperative awareness of spectrum and vision

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

Active disturbance rejection control of layer width in wire arc additive manufacturing based on deep learning

Wire Arc Additive Manufacturing (WAAM) has developed rapidly in recent years and has been widely used in industry. Cold Metal Transfer (CMT), a kind of Gas Metal Arc Welding (GMAW), is widely used in the modeling of thin parts. In the monitoring of CMT process, it is necessary to monitor the weld reinforcement of the deposited layer. It can provide effective alerting for welding and help to improve welding quality. In this paper, the weld reinforcement and the position of molten pool image on the deposited layer are determined by the molten pool visual acquisition system. According to the data of the same direction additive manufacturing experiment, the future molten pool image is predicted by the improved prediction network (PredNet). Meanwhile through a regression network (SERes), the predicted results are regressed to the accurate weld reinforcement information of the deposited layer in advance. The experimental results show that the monitoring system designed in this paper can predict the change of molten pool shape 140ms in advance and the average precision of the weld reinforcement regression is better than 0.3mm. This paper provides the necessary basis for the shape online monitoring and controlling in WAAM process.

Weld Reinforcement Analysis Based on Long-Term Prediction of Molten Pool Image in Additive Manufacturing

Based on the spectrum of self-emitted radiation of weld pool and the quantum efficiency of the camera, we deduce the relative response intensity of the camera to the self-emitted radiation of the weld pool and verify it with the spectrometer Combined with arc spectrum properties, we propose the theoretical criteria for optimal imaging band selection, which is suitable to multiple welding processes and materials Through analyzing the relationship between welding current and light intensity, we design a trigger method for weld pool image acquisition Based on the theory, we select 850 nm high-pass filter and 660 nm band-pass filter to capture weld pool images of stainless steel GMAW plate welding The captured images have clear texture, high signal-to-noise ratio, and abundant details, which verifies the feasibility of our theory This paper provides theoretical guidance for the imaging bands selection in weld pool vision

Zhuang Zhao

Papers

Quality monitoring in wire-arc additive manufacturing based on cooperative awareness of spectrum and vision

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

Active disturbance rejection control of layer width in wire arc additive manufacturing based on deep learning

Weld Reinforcement Analysis Based on Long-Term Prediction of Molten Pool Image in Additive Manufacturing

Optimal imaging band selection mechanism of weld pool vision based on spectrum analysis