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This work aimed at introducing and exploring the potential of a thermal management technique, named as near-immersion active cooling (NIAC), to mitigate heat accumulation in Wire + Arc Additive Manufacturing (WAAM). According to this technique concept, the preform is deposited inside a work tank that is filled with water, whose level rises while the metal layers are deposited. For validation of the NIAC technique, Al5Mg single-pass multi-layer linear walls were deposited by the CMT® process under different thermal management approaches. During depositions, the temperature history of the preforms was measured. Porosity was assessed as a means of analyzing the potential negative effect of the water cooling in the NIAC technique. The preform geometry and mechanical properties were also assessed. The results showed that the NIAC technique was efficient to mitigate heat accumulation in WAAM of aluminum. The temperature of the preforms was kept low independently of its height. There was no measurable increase in porosity with the water cooling. In addition, the wall width was virtually constant, and the anisotropy of mechanical properties tends to be reduced, characterizing a preform quality improvement. Thus, the NIAC technique offers an efficient and low-cost thermal management approach to mitigate heat accumulation in WAAM and, consequently, also to cope with the deleterious issues related to such emerging alternative of additive manufacturing.

Wire-laser directed energy deposition has emerged as a transformative technology in metal additive manufacturing, offering high material deposition efficiency and promoting a cleaner process environment compared to powder processes. This technique has gained attention across diverse industries due to its ability to expedite production and facilitate the repair or replication of valuable components. This work reviews the state-of-the-art in wire-laser directed energy deposition to gain a clear understanding of key process variables and identify challenges affecting process stability. Furthermore, this paper explores modeling and monitoring methods utilized in the literature to enhance the final quality of fabricated parts, thereby minimizing the need for repeated experiments, and reducing material waste. By reviewing existing literature, this paper contributes to advancing the current understanding of wire-laser directed energy deposition technology. It highlights the gaps in the literature while underscoring research needs in wire-laser directed energy deposition.

Defects are a leading issue for the rejection of parts manufactured through the Directed Energy Deposition (DED) Additive Manufacturing (AM) process. In an attempt to illuminate and advance in situ quality monitoring and control of workpieces, we present an innovative data-driven method that synchronously collects sensing data and AM process parameters with a low sampling rate during the DED process. The proposed data-driven technique determines the important influences that individual printing parameters and sensing features have on prediction at the inter-layer qualification to perform feature selection. Three Machine Learning (ML) algorithms including Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) are used. During post-production, a threshold is applied to detect low-density occurrences such as porosity sizes and quantities from CT scans that render individual layers acceptable or unacceptable. This information is fed to the ML models for training. Training/testing are completed offline on samples deemed “high-quality” and “low-quality”, utilizing only features recorded from the build process. CNN results show that the classification of acceptable/unacceptable layers can reach between 90% accuracy while training/testing on a “high-quality” sample and dip to 65% accuracy when trained/tested on “low-quality”/“high-quality” (respectively), indicating over-fitting but showing CNN as a promising inter-layer classifier.

This work focuses on the development of the 3D Extreme High-Speed DED process (EHLA3D), a variant of the laser-based Directed Energy Deposition (DED-LB), for the processing of the material HSS M2. Characteristics for the EHLA3D process are feed rates of >20 m/min, high cooling rates, and layer thicknesses in the range of 100 µm. This work covers the three subsequent stages: (1) a process parameter study on single-track deposition, (2) development of coating parameters, and (3) development of parameters for AM. In scope of stage 2, a coating parameter with a powder mass flow of ṁ = 1.9 kg/h was achieved. A variation in the deposition angles indicates that the coating process is feasible within a tilted deviation of up to 20°. In stage 3, a process parameter with a deposition rate of ṁ = 0.4 kg/h was developed. The hardness results of the as-built specimen with 67 HRC exceeds the hardness of conventionally manufactured and heat-treated M2 steel. The results of this work indicate that the EHLA3D process can be potentially utilized for the additive manufacturing with the material M2 as well as for the productive deposition of anti-wear coatings on free-form surfaces.

Additive Manufacturing (AM) technologies are recognized as the future of the manufacturing industry thanks to their possibilities in terms of shape design, part functionality, and material efficiency. The use of AM technologies in many industrial sectors is growing, also due to the increasing knowledge regarding the AM processes and the characteristics of the final part. One of the most promising AM techniques is the Directed Energy Deposition (DED) that uses a thermal source to generate a melt pool on a substrate into which metal powder is injected. The potentialities of DED technology are the ability to process large build volumes (> 1000 mm in size), the ability to deliver the material directly into the melt pool, the possibility to repair existing parts, and the opportunity to change the material during the building process, thus creating functionally graded material. In this paper, a review of the industrial applications of Laser Powder Directed Energy Deposition (LP-DED) is presented. Three main applications are identified in repairing, designed material, and production. Despite the enormous advantages of LP-DED, from the literature, it emerges that the most relevant application refers to the repairing process of high-value components.

Metal additive manufacturing technologies, such as powder bed fusion process, directed energy deposition (DED) process, sheet lamination process, etc., are one of promising flexible manufacturing technologies due to direct fabrication characteristics of a metallic freeform with a three-dimensional shape from computer aided design data. DED processes can create an arbitrary shape on even and uneven substrates through line-by-line deposition of a metallic material. Theses DED processes can easily fabricate a heterogeneous material with desired properties and characteristics via successive and simultaneous depositions of different materials. In addition, a hybrid process combining DED with different manufacturing processes can be conveniently developed. Hence, researches on the DED processes have been steadily increased in recent years. This paper reviewed recent research trends of DED processes and their applications. Principles, key technologies and the state-of-the art related to the development of process and system, the optimization of deposition conditions and the application of DED process were discussed. Finally, future research issues and opportunities of the DED process were identified.

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.

In the circular economy, products, components, and materials are aimed to be kept at the utility and value all the lifetime. For this purpose, repair and remanufacturing are highly considered as proper techniques to return the value of the product during its life. Directed Energy Deposition (DED) is a very flexible type of additive manufacturing (AM), and among the AM techniques, it is most suitable for repairing and remanufacturing automotive and aerospace components. Its application allows damaged component to be repaired, and material lost in service to be replaced to restore the part to its original shape. In the past, tungsten inert gas welding was used as the main repair method. However, its heat affected zone is larger, and the quality is inferior. In comparison with the conventional welding processes, repair via DED has more advantages, including lower heat input, warpage and distortion, higher cooling rate, lower dilution rate, excellent metallurgical bonding between the deposited layers, high precision, and suitability for full automation. Hence, the proposed repairing method based on DED appears to be a capable method of repairing. Therefore, the focus of this study was to present an overview of the DED process and its role in the repairing of metallic components. The outcomes of this study confirm the significant capability of DED process as a repair and remanufacturing technology.

Additive manufacturing (AM) is a new paradigm for the design and production of high-performance components for aerospace, medical, energy, and automotive applications. This review will exclusively cover directed energy deposition (DED)-AM, with a focus on the deposition of powder-feed based metal and alloy systems. This paper provides a comprehensive review on the classification of DED systems, process variables, process physics, modelling efforts, common defects, mechanical properties of DED parts, and quality control methods. To provide a practical framework to print different materials using DED, a process map using the linear heat input and powder feed rate as variables is constructed. Based on the process map, three different areas that are not optimized for DED are identified. These areas correspond to the formation of a lack of fusion, keyholing, and mixed mode porosity in the printed parts. In the final part of the paper, emerging applications of DED from repairing damaged parts to bulk combinatorial alloys design are discussed. This paper concludes with recommendations for future research in order to transform the technology from “form” to “function,” which can provide significant potential benefits to different industries.

Over the past several decades, metal Additive Manufacturing (AM) has transitioned from a rapid prototyping method to a viable manufacturing tool. AM technologies can produce parts on-demand, repair damaged components, and provide an increased freedom of design not previously attainable by traditional manufacturing techniques. The increasing maturation of metal AM is attracting high-value industries to directly produce components for use in aerospace, automotive, biomedical, and energy fields. Two leading processes for metal part production are Powder Bed Fusion with laser beam (PBF-LB/M) and Directed Energy Deposition with laser beam (DED-LB/M). Despite the many advances made with these technologies, the highly dynamic nature of the process frequently results in the formation of defects. These technologies are also notoriously difficult to control, and the existing machines do not offer closed loop control. In the present work, the application of various Machine Learning (ML) approaches and in-situ monitoring technologies for the purpose of defect detection are reviewed. The potential of these methods for enabling process control implementation is discussed. We provide a critical review of trends in the usage of data structures and ML algorithms and compare the capabilities of different sensing technologies and their application to monitoring tasks in laser metal AM. The future direction of this field is then discussed, and recommendations for further research are provided.