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As high-strength aluminum alloys present several processability issues with additive manufacturing (AM), Scalmalloy®, an Al-Mg-Sc-Zr-based alloy, has been developed. This alloy is age-hardenable, allowing it to precipitate out a strengthening precipitate phase, Al3(Sc,Zr). The manufacturer recommends a single-stage aging treatment at 325 °C for 4 h; however, the majority of the literature studies utilize a powder bed processing known as selective laser melting (SLM) over powder-fed processing directed energy deposition (DED). This study addresses the lack of information on heat treatments for DED fabrication by exploring the application of artificial aging temperatures of 300–400 °C for 2, 4, and 6 h to: 1. determine the impact on the microstructural evolution and mechanical performance and 2. determine whether the recommended treatment for Scalmalloy® is appropriate for DED fabrication. Tensile testing determined that low-temperature treatments exhibited no visible dependence on time (2–6 h); however, time becomes influential at higher temperatures starting at 350 °C. The temperature plays a considerable role in the mechanical and microstructural behaviors of DED Scalmalloy®. The highest tensile strength was noted at 300 °C (384 MPa, 21.6% increase), but all heat-treated cases resulted in an improvement over the as-built case. This investigation established that increasing the treatment temperature resulted in a decreasing trend for the tensile strength that held over time. Elongation at 2 h displayed a near parabolic trend that peaks at 350 °C (20%) and falls with higher temperatures. At the 4 h treatment, a slight decreasing trend was noticed for elongation. No visible change was observed for elongation at 6 h, with elongation values remaining fairly consistent. The microstructural evolution, including micron-sized and nano-sized Al3(Sc,Zr) and grain size, was examined, and coarsening effects were noted with the increase in the temperature. It is recommended that treatment be conducted at 300 °C to achieve the precipitation of the strengthening Al3(Sc,Zr) phase while minimizing coarsening.

 

In directed energy deposition (DED), accurately controlling and predicting melt pool characteristics is essential for ensuring desired material qualities and geometric accuracies. This paper introduces a robust surrogate model based on recurrent neural network (RNN) architectures—Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). Leveraging a time series dataset from multi-physics simulations and a three-factor, three-level experimental design, the model accurately predicts melt pool peak temperatures, lengths, widths, and depths under varying conditions. RNN algorithms, particularly Bi-LSTM, demonstrate high predictive accuracy, with an R-square of 0.983 for melt pool peak temperatures. For melt pool geometry, the GRU-based model excels, achieving R-square values above 0.88 and reducing computation time by at least 29%, showcasing its accuracy and efficiency. The RNN-based surrogate model built in this research enhances understanding of melt pool dynamics and supports precise DED system setups.

 

Laser-directed energy deposition (DED), a metal additive manufacturing method, is renowned for its role in repairing parts, particularly when replacement costs are prohibitive. Ensuring that repaired parts avoid residual stresses and deformation is crucial for maintaining functional integrity. This study conducts experimental and numerical analyses on trapezoidal shape repairs, validating both the thermal and mechanical models with experimental results. Additionally, the study presents a methodology for creating a toolpath applicable to both the DED process and Abaqus CAE software. The findings indicate that employing a pre-heating strategy can reduce residual stresses by over 70% compared to no pre-heating. However, pre-heating may not substantially reduce final distortion. Notably, final distortion can be significantly mitigated by pre-heating and subsequently cooling to higher temperatures, thereby reducing the cooling rate. These insights contribute to optimizing DED repair processes for enhanced part functionality and longevity.

 

In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions in predicting residual stresses in laser additive manufacturing.

 

Scalmalloy® is an Al-Mg-Sc-Zr-based alloy specifically developed for additive manufacturing (AM). This alloy is designed for use with a direct aging treatment, as recommended by the manufacturer, rather than with a multistep treatment, as often seen in conventional manufacturing. Most work with Scalmalloy® is conducted using powder bed rather than powder-fed processes. This investigation seeks to fill this knowledge gap and expand beyond single-step aging to promote an overall balanced AM-fabricated component. For this study, directed energy deposition (DED)-fabricated Scalmalloy® components were subjected to low-temperature treatments to minimize residual stresses inherent in the material due to the layer-by-layer build process. X-ray diffraction (XRD) indicated the possibility of stress minimization while reducing the detriment to mechanical strength through lower temperature treatments. Microstructural analyses consisting of energy dispersion spectroscopy (EDS) and electron backscatter diffraction (EBSD) revealed the presence of grain growth detrimentally affecting the strength and elongation made possible by very small grains inherent to AM and rapid solidification. Tensile testing determined that treatment at 175 °C for 1 h provides the best relief from the existing residual stresses; however, this is accompanied by a diminishment in the yield and tensile strength of 19 and 9.5%, respectively. It is noted that treatment at 175 °C for 2 h did not provide as great of a decrease in residual stresses, theorized to be the result of grain growth and other strengthening mechanisms further stressing the structure; however, the residual stresses are still significantly diminished compared with the as-built condition. Furthermore, a minimal reduction of the tensile strengths indicates the possibility of finding a balance between property diminishment and stress state through the work proposed here.

 

Refractory multi-principal element alloys (RMPEAs), HfNbTaTiZr, (HfNbTaTiZr)9Cr and (HfNbTaTiZr)9Al, were manufactured using vacuum arc melting followed by laser-remelting to mimic additive manufacturing. The microhardness of as-cast HfNbTaTiZr, (HfNbTaTiZr)9Cr and (HfNbTaTiZr)9Al samples after arc melting was measured as 6.20, 7.63 and 6.89 GPa, respectively. After laser-remelting and re-solidification, the hardness increased by ~30% for each composition; the hardest was (HfNbTaTiZr)9Cr measured at 9.60 GPa, and the softest was HfNbTaTiZr with a hardness of 8.42 GPa, which was still harder compared to all the as-cast samples. The addition of Al and Cr led to enhanced oxidation resistance for the respective RMPEA systems. The Al-containing composition showed the best oxidation resistance for the as-cast samples; however, after laser remelting, the Cr-containing RMPEA had the best overall oxidation resistance, and the increase in weight after oxidation dropped by 42% when compared to that for the as-cast alloy. Laser remelting the RMPEAs led to an improvement in mechanical properties; it also resulted in enhanced oxidation resistance for (HfNbTaTiZr)9Cr. However, laser remelting barely changed the oxidation resistance for (HfNbTaTiZr)9Al, and it decreased the oxidation resistance for HfNbTaTiZr. These phenomena are related to microstructure changes induced by the laser remelting/additive manufacturing as compared to conventional casting-based manufacturing. 

Additive manufacturing (AM) simulations are effective for materials that are well characterized and published; however, for newer or proprietary materials, they cannot provide accurate results due to the lack of knowledge of the material properties. This work demonstrates the process of the application of mathematical search algorithms to develop an optimized material dataset which results in accurate simulations for the laser directed energy deposition (DED) process. This was performed by first using a well-characterized material, Ti-64, to show the error in the predicted melt pool was accurate, and the error was found to be less than two resolution steps. Then, for 7000-series aluminum using a generic material property dataset from sister alloys, the error was found to be over 600%. The Nelder–Mead search algorithm was then applied to the problem and was able to develop an optimized dataset that had a combined width and depth error of just 9.1%, demonstrating that it is possible to develop an optimized material property dataset that facilitates more accurate simulation of an under-characterized material.

 

Nanostructured steels are expected to have enhanced irradiation tolerance and improved strength. However, they suffer from poor microstructural stability at elevated temperatures. In this study, Fe–21Cr–5Al–0.026C (wt%) Kanthal D (KD) alloy belonging to a class of (FeCrAl) alloys considered for accident‐tolerant fuel cladding in light‐water reactors is nanostructured using two severe plastic deformation techniques of equal‐channel angular pressing (ECAP) and high‐pressure torsion (HPT), and their thermal stability between 500–700 °C is studied and compared. ECAP KD is found to be thermally stable up to 500 °C, whereas HPT KD is unstable at 500 °C. Microstructural characterization reveals that ECAP KD undergoes recovery at 550 °C and recrystallization above 600 °C, while HPT KD shows continuous grain growth after annealing above 500 °C. Enhanced thermal stability of ECAP KD is from significant fraction (>50%) of low‐angle grain boundaries (GBs) (misorientation angle 2–15°) stabilizing the microstructure due to their low mobility. Small grain sizes, a high fraction (>80%) of high‐angle GBs (misorientation angle >15°) and accordingly a large amount of stored GB energy, serve as the driving force for HPT KD to undergo grain growth instead of recrystallization driven by excess stored strain energy.

 

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.