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Additive manufacturing (AM) technology is increasingly being used in the aerospace industry due to its advantages for aerospace components such as reduction of weight. A deep understanding of the behavior and properties of additively manufactured materials or parts is required to effectively carry out the certification process which is inevitable f...
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... roughly eight categories and compared and analyzed. According to Zhang et al. [3], they confirmed that the exponentially decaying equation model gives more accurate simulation results than other heat source models. Therefore, the exponentially decaying equation model is used in this research. The distribution shape of the heat source is shown in Fig. 3 and can be expressed by Eq. (1). P is the power of the stationary laser source, r is the radius of the laser beam, (x, y, z) are the coordinates of the center of the heat source, β is the laser-beam absorptivity and H is regarded as the powder layer thickness. The moving heat source model is implemented using ABAQUS DFLUX user ...
Citations
... In the LPBF process, the powder becomes a liquid state by high energy of the laser and becomes a solid state as it cools. As done by Jeong et al. [24], a global array is created using the UEXTERNALDB user subroutine for ABAQUS, and the temperatures of all integration points are monitored at every increment to check the material phase. Then, phase and temperature-dependent material thermal properties are applied using the UMATHT user subroutine for ABAQUS. ...
A numerical analysis model that can accurately predict the physical characteristics of the actually additive manufactured products can significantly reduce time and costs for experimental builds and tests. Thermal analysis for the metal AM process simulation requires a lot of analysis parameters and conditions. However, their accuracy and reliability are not clear, and the current understanding of their influence on the analysis results is very insufficient. Therefore, in this study, the influence of uncertain analysis parameters on the thermal analysis results is estimated, and a procedure to calibrate these analysis parameters is proposed. By using the thermal analysis results for parameter cases determined by a design of experiments, a regression analysis model is constructed to estimate the sensitivity of the analysis parameters to the thermal analysis results. Additionally, it is used to determine the optimal values of analysis parameters that can produce the thermal analysis results closest to the given reference data from actual builds. By using the melt pool size computed from a numerical model as reference data, the proposed procedure is validated. From this result, it is confirmed that a high-fidelity thermal analysis model that can predict the characteristics of actual builds from minimal experimental builds can be constructed efficiently.
This research delves into an extensive exploration of the natural frequencies of the shear-deformable functionally graded material (FGM) aircraft wings, employing an innovative FE-based multilayered FGM model. Natural frequency analysis is pivotal in aircraft design as it influences structural stability and vibration characteristics. The model employs a power-law distribution, following the rule of mixture, to ascertain effective material properties. Realistic gradient variations are obtained for the FGM wings, which is composed of several layers with unique material compositions. The study focuses on three prevalent FGM airfoil wing profiles (NACA0009, NACA2424, and NACA4415) and examines the impact of volume fraction indices and aspect ratios on natural frequency analysis. Rigorous analyses encompass various FGM structures, such as plates, shells, and airfoils, affirming the efficiency and accuracy of the FE-based multilayered FGM model. The results indicate that the present model is efficient for analyzing complex-shaped FGM structures.
Additive manufacturing (AM or 3D printing) technology creates the same shape to 3D CAD data through an additive process rather than using a conventional manufacturing method. This has made it possible to surpass the limitations of existing processing methods. Because of the many advantages of AM technology, many studies have been conducted to apply AM technology for the manufacture of aircraft structures. In this work, in particular, the influence of build parameters, mechanical properties, and post-processing were reviewed with the aim of determining the effect of support among the build parameters. The tip-cap of an aircraft wing was fabricated using AlSi10Mg powder, and the deformation during post-processing was reviewed according to three types of support. Mechanical properties were verified through specimen testing, and parts were fabricated and post-processing applied, under the same conditions as for the specimens. Based on the existing CAD model, it was discovered that deformation occurred during each type of post-processing and the influence of support on the deformation was reviewed.
The smart manufacturing revolution is continuously enabling the manufacturers to achieve their prime goal of producing more and more products with higher quality at a minimum cost. The crucial technologies driving this new era of innovation are machine learning and artificial intelligence. Paving to the advancements in the digitalization of the production and manufacturing industry and with a lot of available data, various machine learning techniques are employed in manufacturing processes. The main aim of implementing the ML techniques being to save time, cost, resources and avoid possible waste generation. This paper presents a systematic review focusing on the application of various machine learning techniques to different manufacturing processes, mainly welding (arc welding, laser welding, gas welding, ultrasonic welding, and friction stir welding), molding (injection molding, liquid composite, and blow molding) machining (turning, milling, drilling, grinding, and finishing), and forming (rolling, extrusion, drawing, incremental forming, and powder forming). Moreover, the paper also reviews the aim, purpose, objectives, and results of various researchers who have applied AI/ML techniques to a wide range of manufacturing processes and applications.
Additive manufacturing (AM) has emerged as a promising technology to cater to the increasing demand for the fabrication of multi-functional, multi-material, and complex parts. AM is revolutionizing production and product development in the aerospace, automotive, and medical fields. However, mismatch in material properties, pervasive imperfections in the printed part, and lack of build consistency are crucial concerns. Higher accuracy in AM processes primarily depends on controlling various aspects of the process. In the last few years, machine learning, data analytics, and design for additive manufacturing have been the most extensively used techniques to address the vital concerns of additive manufacturing. Despite well-known techniques, very few studies reported applications of these techniques for aerospace. Specifically, this study comprehensively reviews recent advancements in the design for additive manufacturing (DfAM) and applications of machine learning and big data analytics to address the prime concerns of AM. The DfAM emphasizes issues and opportunities for topology optimization and methods for generative design for weight reduction and manufacturing of products with high resolution. Simulation and modeling techniques that are being used to improve geometric quality and process analysis are discussed to enable its potential for different applications. Further, automation of AM process using the Internet of things and knowledge-based systematic process planning is discussed to address key issues in process planning of multiple parts. Finally, the current challenges and scope for algorithmically driven AM processes are summarized with the trends of automation in AM to ensure greater efficiency and a better lifecycle of AM products in the era of industry 4.0.
In an effort to simulate the involved thermal physical effects that occur in Directed Energy Deposition (DED) a thermodynamically consistent phase-field method is developed. Two state parameters, characterizing phase change and consolidation, are used to allocate the proper material properties to each phase. The numerical transient solution is obtained via a finite element analysis. A set of experiments for single-track scanning were carried out to provide dimensional data of the deposited cladding lines. By relying on a regression analytical formulation to establish the link between process parameters and geometries of deposited layers from experiments, an activation of passive elements in the finite element discretization is considered. The single-track cladding of Inconel 625 powder on tempered steel 42CrMo4 was printed with different power, scanning speed, and feed rate to assess their effect on the morphology of the melt pool and the solidification cooling rate. The forecast capability of the developed model is assessed by comparison of the predicted dimensions of melt pools with experiments reported in the literature. In addition, this research correlated the used process parameter in the modeling of localized transient thermal with solidification parameters, namely, the thermal gradient (\(G\)) and the solidification rate (\(R\)). The numerical results report an inverse relationship between \(R\) with \(G\), and microstructure transition from the planar to dendrite by moving from the boundary to the interior of melt pool, which agree well with experimental measurements.
