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Wire arc additive manufacturing (WAAM) direct energy deposition is used to process two different duplex stainless steels (DSS) wire chemistries. Macro- and micromechanical response variables relevant to industrialization are studied using a design of the experiment (DoE) approach. The tested operation window shows that the variation of layer height and over-thickness are highly correlated with travel speed and wire feed speed and positively correlated with heat input. The maximum achieved average instantaneous deposition rate is 3.54 kg/h. The use of wire G2205, which contains 5 wt% nickel content, results in a ferrite-to-austenite ratio that is equally balanced, while wire G2209, with 9 wt% nickel, provides a lower ferrite content. The spatial distribution of Fe% is influenced by part geometry and path planning, and higher heat inputs result in coarser microstructures. The manufacturing weaving strategy generates a heterogeneous microstructure characterized by fluctuations in Fe%. Thus, understanding the effect of complex thermal history, higher-dimensional design spaces, and uncertainty quantification is required to drive metal WAAM toward full industrialization.

The sequential digital design and manufacturing of components play a crucial role in realizing the industrial potential of directed energy deposition (DED), particularly when employing an electric arc as the energy source to melt a filler wire (DED-ARC). This study explores the application of DED-ARC for manufacturing large-scale, load-bearing structures, using a railway bogie as a case study. Originally a cast Bettendorf-type design, the bogie was redesigned using a multi-material approach. High-strength low-alloy (HSLA) steel was utilized in high-stress areas, while low-carbon steel was used elsewhere to reduce mass, enhance manufacturability, and improve repairability. The workflow included computer-aided design (CAD), topological optimization, finite element analysis (FEA), material selection, and iterative CAD modifications to address process constraints. The redesigned bogie underwent pre-manufacturing, fabrication, and a final scan of the as-built part. Representative multi-material wall samples were characterized, revealing typical microstructures and elastic limits of 468 MPa and 737 MPa for ER70S-6 and ER100S-G, respectively. These tensile properties were incorporated into FEA verification simulations, demonstrating a higher safety factor compared to the original design. A CAD-to-part analysis, including scan comparisons, highlighted manufacturing-induced deformation, material-dependent over-thickness, and localized geometric variations. This study offers a comprehensive overview of the DED-ARC process, from design through characterization, and demonstrates its capability to produce high-quality industrial components. The findings underscore the manufacturability and potential of DED-ARC for fabricating robust, multi-material structures for demanding applications.

Laser Wire Directed Energy Deposition (LW-DED) is a high-precision additive manufacturing method known for material efficiency and high deposition rates; however, real-time monitoring remains challenging due to complex interactions between the laser, melt pool, and wire, especially during multilayer deposition. This study presents a real-time wire monitoring approach for multilayer LW-DED of Inconel 625 to predict surface waviness. Using a constant linear energy density, 10-layer wall structures were fabricated. A convolutional neural network model achieved 81.11% mAP50–95 and over 59 frame per second for wire detection, while an artificial neural network, using wire features and process parameters, predicted Wp10 waviness with a 33.54 µm RMSE and R 2 greater than 80%. The results confirm the system’s effectiveness in monitoring and surface quality prediction, offering a promising solution for quality control in multilayer LW-DED.

Duplex stainless steels (DSS) in wire and arc additive manufacturing (WAAM) have attracted significant research attention due to their mechanical properties and corrosion resistance. This study uses conventional and nanomechanical testing methods to compare the mechanical and microstructural behaviors at macroscopic and microscopic length scales. Macro hardness (HV10) testing yielded 259 and 249 in low and high heat input (HI) samples, respectively, while ferrite content averaged 52.7 and 48.5%. However, these results fail to provide conclusive insight into the potential influence of microstructural variations at the macroscopic level, likely due to the composite response of the material. To overcome this limitation, the mechanical response of the DSS samples is assessed at the grain level via high throughput nanoindentation mapping with image processing to track the location of each indent. This approach enabled differentiating the indents landing on ferrite and austenite phases as well as those landing on the interfaces. The results showed that the austenite phase had higher hardness (4.30 and 4.35 GPa) than the ferrite phase (3.89 GPa and 4.03 GPa) for high and low HI samples, respectively. The observed differences in hardness between the phases can be attributed to higher nitrogen content in the austenitic phase.

Industry 4.0, also known as the Fourth Industrial Revolution, is a term used to describe the current trend of automation and data exchange in manufacturing and other industries. The Internet of Things (IoT) plays a crucial role in Industry 4.0 by connecting devices, machines, and products to the Internet and enabling real-time data exchange. Moreover, additive manufacturing is a key developing manufacturing technology in Industry 4.0. New technologies such as data analysis with Artificial Intelligence and machine vision are widely used in optimization. However, in a lab environment, these technologies depend on the data collected from the process. For such work, the researchers should be able to focus on their core research rather than on the development of infrastructure to collect and analyse the data. This research presents an open software and hardware IoT solution to monitor a laser wire direct energy deposition system installed in a cartesian type 3-axis machine tool. The IoT solution adopts three open-source tools for core issues, such as 1) interoperability, flexibility, and availability; 2) data storage; and 3) data visualization of sensor data and manufacturing process signals. The system architecture is based on one or more edge devices connected to sensors and forwarding their data toward a local API endpoint. The endpoint is created with Node-RED, an open-source visual flow-based development tool for IoT data. Node-RED forwards the data to an open-source InfluxDB database. Finally, the data is visualized with an open-source Grafana application. The system is prototyped, designed, implemented, and tested in a lab environment to monitor a laser-wire direct energy deposition process. The significance of such a flexible IoT data collection system for research and development projects can be integral. Thus, providing savings in time and money can substantially speed up the development of new technologies where the value arises from the sensor data and its analysis.

Wire-feed laser additive manufacturing processes have gained researchers’ attention because of their potential to reduce material waste, guarantee accuracy, increase material quality and density, and produce a wide dimensional range of final products. Nevertheless, printing materials with desired geometrical properties of the beads is still challenging in such processes. This might be attributed to the need for more sufficient experimental data and precise modeling approaches. In this study, an architecture based on Artificial Neural Networks (ANNs) is developed to model the bead geometries (width, height, and area), considering the wire feed rate, laser power, and travel speed as process parameters. A design-of-experiment based on full factorial design is considered for processing single beads with a Fraunhofer coaxial wire-feed laser system. Inconel 625 wire with a diameter of 1.14 mm and stainless steel substrate are utilized as the experimental materials. Geometrical data is obtained using a laser scanner model RA-7525 SE with 0.026mm volumetric accuracy. The beads’ geometrical details are provided as the feeding data for the proposed ANN. For each bead, a length of 10 mm is considered to calculate the average geometrical parameters, which increases the accuracy of the dataset in comparison to the values acquired via a macroscopic picture of the cross-section of each weld bead. A variety of hyperparameters are chosen and compared regarding precision criteria, including Mean Square Error (MSE), to increase the model‘s accuracy. A train-test separation strategy is considered to evaluate the model‘s accuracy on independent data points. The outcome of this research is an ANN-based geometry prediction model that can be utilized to enhance the development of offline path planners and optimize process parameter selection for a precise geometry toward process control.