Search Results (360)

Copper-based alloys are widely known for their high thermal and electrical conductivity. Although the use of these alloys in powder-based additive manufacturing (AM) shows significant promise, applying this method in wire arc additive manufacturing (WAAM) processes poses various considerable challenges, including porosity, delamination, surface oxidation, etc. The limited research on WAAM of copper alloys, especially Cu1897, highlights the need for a more in-depth investigation. This study addresses the effects of process parameters in pulse cold metal transfer (CMT)-based WAAM of Cu1897, i.e., pulse correction (PC) and arc length correction (ALC), on bead penetration and porosity. The results showed that as PC was increased from −5 to +5, weld bead penetration increased from 2.38 mm to 3.87 mm. To further enhance penetration and reduce the porosity, the ALC was varied from +30% to −30% with a step size of 15%. The results showed that weld bead penetration increased to 4.47 mm by altering the ALC from +30% to −30%. Additionally, as the ALC varied within this range, porosity decreased significantly from 3.98% to 0.28%. Overall, it is concluded that a lower value of ALC is recommended to improve bead penetration and reduce porosity in WAAM of Cu1897. Full article

An evaluation was conducted on the fabrication of an axial flow impeller by a hybrid system of wire arc additive manufacturing and machining. First, a four-bladed stainless steel axial flow impeller was fabricated to measure the number of chips and fabrication time. Next, axial flow impellers with different numbers of blades were designed and compared with those fabricated only by machining from a round bar. In both cases, the number of chips was reduced by approximately 80% by using this system. On the other hand, the increase in the number of blades reduced the difference in fabrication time, which was almost the same with six blades. In conclusion, the use of this system is an option from the viewpoint of reducing environmental impact; however, it is not necessarily advantageous in terms of fabrication time. Full article

Thermal simulation is essential in wire-arc-directed energy deposition (W-DED) to accurately estimate temperature distributions, impacting residual stress and distortion in components. Proper calibration of simulation models minimizes inaccuracies caused by varying material properties, machine settings, and environmental conditions. The lack of standardized calibration methods further complicates thermal predictions. This paper introduces a novel calibration method integrating both machine learning, as the high-fidelity (HF) model, and response surface modeling, as the low-fidelity (LF) model, within a multi-fidelity (MF) framework. The approach utilizes Bayesian optimization to effectively explore the search space for optimal solutions. A two-tiered model employs the LF model to identify feasible regions, followed by the HF model to refine calibration parameters, such as thermal efficiency (η), convection coefficient (h), and emissivity (ε), which are difficult to determine experimentally. A three-factor Box–Behnken design (BBD) is applied to explore the design space, requiring only thirteen parameter configurations, conserving resources and enabling robust model training. The efficacy of this MF model is demonstrated in multi-layer W-DED calibration, showing strong alignment between experimental and simulated temperatures, with a mean absolute error (MAE) of 7.47 °C. This method offers a replicable framework for broader additive manufacturing processes. Full article

This paper presents a study on residual stress measurement in wire-arc additively manufactured (WAAM) titanium samples using the non-destructive method of phased array ultrasonics. The contour method (CM) was used for the verification of the phased array ultrasonic results. This allowed for a comparison of measurement methods to understand the effects on the distribution of residual stress (RS) within Ti-6Al-4V samples and the effectiveness of measurement of residual stress using phased array ultrasonics. From the results of the experiments, the phased array ultrasonic data were found to be in good agreement with the CM results and displayed similar residual stress distributions in the samples. The results of the individual elements of the phased array were also compared and an improvement in accuracy was found. From per-element results, anomalies were found and could be mitigated with the ability to average the results by using phased array ultrasonics. Therefore, based on these results, there is a strong case for the benefits of using phased array ultrasonics as a method of residual stress measurement for WAAM Ti-6Al-4V components over other existing residual stress measurement techniques. Full article

This paper takes the single-wall wall manufactured by wire arc additive manufacturing (WAAM) as the research object and compares it with the as-cast aluminum alloy with the same series. By using feed rate, cutting depth, spindle speed, etc., as single or compound parameters, the machinability of the sample is analyzed. The results indicate that the influence of varying parameters on the as-deposited aluminum alloy follows the order of feed rate > cutting depth > spindle speed. As the feed rate increases, the surface roughness initially decreases and then increases, with the optimal surface quality achieved at 12 mm/s (with a surface roughness of 2.013 $\mathsf{\mu }\mathrm{m}$). Different from the as-deposited alloy, the influence of the parameters on the as-cast alloys follows the order of spindle speed > cutting depth > feed rate. The experiments reveal that, for both as-deposited and as-cast states, the trends of the impact of cutting depth and spindle speed on surface quality are consistent. However, at low feed rates (2–12 mm/s), for as-deposited states, the surface quality of as-deposited samples becomes smoother as the feed rate increases (contrary to common knowledge). This result can be attributed to the elevated milling temperature, which softens the material, making it easier to remove and reducing the surface roughness. Full article

The effects of solid-solution and ageing treatments on the microstructure and mechanical properties of a GH4169 alloy made by wire arc additive manufacturing, micro-casting, and forging were researched. The microstructure, along with the size and type of precipitated phase, were analysed using an optical microscope, scanning electron microscope, and transmission electron microscope. The strength and toughness were tested using a tensile testing machine. The results show that a polygonal austenite microstructure was obtained for the GH4169 alloy prepared through wire arc additive manufacturing, micro-casting, and forging, followed by solid-solution and double-ageing treatments at different times. There were a few twins in the austenite matrix. A large number of nano-sized γ″- and γ′-precipitated phases and a small number of Laves phases and MX phases were found in the matrix. The tensile strength and yield strength of the GH4169 alloy increased first and then decreased with the ageing time. After ageing for 16 h, the maximum yield strength was 1287 ± 22 MPa, the maximum tensile strength was 1447 ± 19 MPa, and the elongation was about 19.5%. The main strength mechanism is precipitated phase strength and solid-solution strength. The fracture exhibited obvious ductile fracture characteristics. Full article

This study investigates the impact of incorporating TiC and TiB₂ inoculants on the microstructure and corrosion performance of an arc-directed energy-deposited PH 13-8Mo martensitic stainless steel. The microstructural characterizations revealed partial dissolution of the incorporated ceramic-based nanoparticles, resulting in the formation of in situ TiC phase in the TiC-inoculated sample, while TiC and chromium-enriched M₃B₂ phases were formed in the TiB₂-inoculated sample. Further investigations into the electrochemical response of the fabricated samples confirmed that the applied inoculation strategy slightly enhanced the corrosion resistance of the alloy, offering a valuable advantage for in-service performance for applications in harsher environments. The slight improvement in the corrosion resistance of the inoculated samples was found to be attributed to the formation of a higher fraction of low-angle grain boundaries and enhanced retained austenite content in the microstructure. However, it is essential to note that the formation of chromium-enriched M₃B₂ phases in the TiB₂-inoculated sample led to a slight deterioration in its corrosion resistance compared to the TiC-inoculated counterpart. Full article

Additive Manufacturing (AM) is a pivotal technology for transforming complex geometries with minimal tooling requirements. Among the several AM techniques, Wire Arc Additive Manufacturing (WAAM) is notable for its ability to produce large metal components, which makes it particularly appealing in the aerospace sector. However, precise control of the bead geometry, specifically bead width and height, is essential for maintaining the structural integrity of WAAM-manufactured parts. This paper introduces a methodology using a Deep Neural Network (DNN) model for forecasting the bead geometry in the WAAM process, focusing on gas metal arc welding cold metal transfer (GMAW-CMT) WAAM. This study addresses the challenges of bead geometry prediction by developing a robust predictive framework. Key process parameters, such as the wire travel speed, wire feed rate, and bead dimensions of the previous layer, were monitored using a Coordinate Measuring Machine (CMM) to ensure precision. The collected data were used to train and validate various regression models, including linear regression, ridge regression, regression, polynomial regression (Quadratic and Cubic), Random Forest, and a custom-designed DNN. Among these, the Random Forest and DNN models were particularly effective, with the DNN showing significant accuracy owing to its ability to learn complex nonlinear relationships inherent in the WAAM process. The DNN model architecture consists of multiple hidden layers with varying neuron counts, trained using backpropagation, and optimized using the Adam optimizer. The model achieved mean absolute percentage error (MAPE) values of 0.014% for the width and 0.012% for the height, and root mean squared error (RMSE) values of 0.122 for the width and 0.153 for the height. These results highlight the superior capability of the DNN model in predicting bead geometry compared to other regression models, including the Random Forest and traditional regression techniques. These findings emphasize the potential of deep learning techniques to enhance the accuracy and efficiency of WAAM processes. Full article

In this study, to further clarify the advantages of industrial applications of wire arc additive manufacturing (WAAM), the focus is on the repair of blades damaged by cavitation erosion using WAAM. A fan-type inducer was installed in a centrifugal pump experimental apparatus, and then paint erosion tests were conducted. Based on the tests, the fabrication time for repairing blades with a hybrid system of WAAM and machining was calculated and compared with that for fabricating a new part. It is concluded that applying WAAM to the fabrication process of an industrial turbopump has advantages not only in the manufacture of parts but also in the repair of such parts. Full article

Functionally Graded Materials (FGMs) can outperform their homogeneous counterparts. Advances in digitalization technologies, mainly additive manufacturing, have enabled the synthesis of materials with tailored properties and functionalities. Joining dissimilar metals to attain compositional grading is a relatively unexplored research area and holds great promise for engineering applications. Metallurgical challenges may arise; thus, a theoretical critical analysis is presented in this paper. A multidisciplinary methodology is proposed here to unify optimal design, multi-feed Wire-Arc Additive Manufacturing (WAAM), and image-based characterization methods to create structure-specific oriented FGM parts. Topology optimization is used to design FGMs. A beam under pure bending is used to explore the layer-wise FGM concept, which is also analytically validated. The challenges, limitations, and role of WAAM in creating FGM parts are discussed, along with the importance of numerical validation using full-field deformation data. As a result, a conceptual FGM engineering workflow is proposed at this stage, enabling digital data conversion regarding geometry and compositional grading. This is a step forward in processing in silico data, with a view to experimentally producing parts in future. An optimized FGM beam, revealing an optimal layout and a property gradient from iron to copper along the build direction (bottom–up) that significantly reduces the normal pure bending stresses (by 26%), is used as a case study to validate the proposed digital workflow. Full article

Currently, the focus of production is shifting towards the use of innovative manufacturing techniques and away from traditional methods. Additive manufacturing technologies hold great promise for creating industrial products. The industry aims to enhance the reliability of individual components and structural elements, as well as the ability to accurately anticipate component failure, particularly due to fatigue. This paper explores the possibility of predicting component failure in parts produced using the WAAM (wire arc additive manufacturing) method by employing fractal dimension analysis. Additionally, the impact of manufacturing imperfections and various heat treatment processes on the fatigue resistance of 30CrMnSi steel has been investigated. Fatigue testing of samples and actual components fabricated via the WAAM process was conducted in this study. The destruction of the examined specimens and products was predicted by evaluating the fractal dimensions of micrographs acquired at different stages of fatigue testing. It has been established that technological defects are more dangerous in terms of fatigue failure than microstructural ones. The correctly selected mode of heat treatment for metal after electric arc welding allows for a more homogeneous microstructure with a near-complete absence of microstructural defects. A comparison of the fractal dimension method with other damage assessment methods shows that it has high accuracy in predicting part failure and is less labor-intensive than other methods. Full article

It is difficult for traditional aluminum alloy manufacturing technology to meet the requirements of large-scale and high-precision complex shape structural parts. Wire Arc additive manufacturing technology (WAAM) is an innovative production method that presents the unique advantages of high material utilization, a large degree of design freedom, fast prototyping speed, and low cast. As a result, WAAM is suitable for near-net forming of large-scale complex industrial production and has a wide range of applications in aerospace, automobile manufacturing, and marine engineering fields. In order to serve as a reference for the further development of WAAM technology, this paper provides an overview of the current developments in WAAM both from the digital control system and processing parameters in summary of the recent research progress. This work firstly summarized the principle of simulation layering and path planning and discussed the influence of relative technological parameters, such as current, wire feeding speed, welding speed, shielding gas, and so on. It can be seen that both the welding current and wire feeding speed are directly proportional to the heat input while the travel speed is inversely proportional to the heat input. This process regulation is an important means to improve the quality of deposited parts. This paper then summarized various methods including heat input, alloy composition, and heat treatment. The results showed that in the process of WAAM, it is necessary to control the appropriate heat input to achieve minimum heat accumulation and improve the performance of the deposited parts. To obtain higher mechanical properties (tensile strength has been increased by 28%–45%), aluminum matrix composites by WAAM have proved to be an effective method. The corresponding proper heat treatment can also increase the tensile strength of WAAM Al alloy by 104.3%. In addition, mechanical properties are always assessed to evaluate the quality of deposited parts. The mechanical properties including the tensile strength, yield strength, and hardness of the deposited parts under different processing conditions have been summarized to provide a reference for the quality evaluation of the deposition. Examples of industrial products fabricated by WAAM are also introduced. Finally, the application status of WAAM aluminum alloy is summarized and the corresponding future research direction is prospected. Full article

The following study examines the impact of temperature on the deposition of components using Cold Metal Transfer–Wire Arc Additive Manufacturing technology. In the experiment, two overlay weld wall structures were created by applying an interpass temperature of 100 °C and without additional cooling. Subsequently, the microstructural and mechanical properties were observed. No changes in the microstructure due to the application of the interpass temperature were confirmed, and the microstructure of the manufactured components, in both cases, consisted of columnar dendrites. It was found that applying an interpass temperature reduced the average ultimate tensile strength by nearly 65 MPa and the average offset yield strength by 82 MPa. The influence of the cooling strategy on the resulting microstructure was not confirmed. Transmission electron microscopy analysis confirmed the presence of strengthening phases γ′/γ″ in both components; however, a larger amount of the strengthening phase γ″ was found in the component manufactured without the application of an interpass temperature. Full article

Wire Arc Additive Manufacturing (WAAM) represents a disruptive technology in the field of metal additive manufacturing. Understanding the relationship between input factors and layer geometry is crucial for studying the process comprehensively and developing various industrial applications such as slicing software and feedforward controllers. Statistical tools such as clustering and multivariate polynomial regression provide methods for exploring the influence of input factors on the final product. These tools facilitate application development by helping to establish interpretable models that engineers can use to grasp the underlying physical phenomena without resorting to complex physical models. In this study, an experimental campaign was conducted to print steel components using WAAM technology. Advanced statistical methods were employed for mathematical modeling of the process. The results obtained using linear regression, polynomial regression, and a neural network optimized using the Tree-structured Parzen Estimator (TPE) were compared. To enhance performance while maintaining the interpretability of regression models, clusterwise regression was introduced as an alternative modeling technique along with multivariate polynomial regression. The results showed that the proposed approach achieved results comparable to neural network modeling, with a Mean Absolute Error (MAE) of 0.25 mm for layer height and 0.68 mm for layer width compared to 0.23 mm and 0.69 mm with the neural network. Notably, this approach preserves the interpretability of the models; a further discussion on this topic is presented as well. Full article

This paper delves into the formability of material deposited by wire arc additive manufacturing. It presents a novel dieless Nakajima testing procedure that offers a practical solution for obtaining strain loading paths up to failure directly from the deposited material without the need for extracting sheet blanks. The procedure involved machining a region of the deposited material to the desired shape and thickness and using a press to drive and control the movement of a hemispherical punch. The test was designed using finite element modeling, and its effectiveness in obtaining the required strain loading paths directly from the deposited material was verified through experimentation with digital image correlation. Importantly, this novel test eliminates the need for the special-purpose tool setup required in conventional Nakajima sheet formability tests, thereby simplifying the overall testing process. Full article