Numer czasopisma
Computer Methods in Materials Science
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ISSN: 2720-4081
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Data wydania
2024
Rocznik
Vol. 24
Numer
No. 2
Prawa dostępu
Dostęp: otwarty dostęp
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Prawa: CC BY 4.0 
Uznanie autorstwa 4.0 Międzynarodowe (CC BY 4.0)
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Rocznik czasopisma (rel.)
Rocznik czasopisma
Computer Methods in Materials Science
Vol. 24 (2024)
Artykuły numeru (rel.)
Artykuł
Otwarty dostęp
Modeling the thermo-mechanical response and phase changes in metallic additive manufacturing (MAM) processes using a dissipative phase-field model
(Wydawnictwa AGH, 2024) Darabi, Roya; Azinpour, Erfan; Reis, Ana; de Sa, Jose Cesar
Additive manufacturing (AM) has emerged as a highly promising manufacturing technique, offering unprecedented possibilities for creating complex geometries and functional structures. However, harnessing the full potential of AM requires the development of a robust computational framework capable of capturing the intricate multi-scale and multi-physics nature of the process. The constitutive and structural responses encountered in AM are particularly challenging to reproduce due to the complex behavior of the material involved. This research aims to address these challenges by presenting a comprehensive computational approach that incorporates a material model capable of accurately representing the behavior of different phases occurring during AM. To achieve this, the finite element method, using the Lagrangian framework in the implicit time scheme, is employed through the widely adopted ABAQUS software. Computational implementation is facilitated using the FORTRAN programming language. By employing weakly coupled thermal and mechanical constitutive equations, the framework enables the analysis of thermal stresses, strains, and displacements during realistic solidification processes, which inherently involve highly nonlinear constitutive relations. Through a series of numerical examples, the capabilities of the proposed model are demonstrated across various computational scales, particularly during the rapid melting and solidification phases. These simulations reveal the formation of residual stresses, which can lead to part distortion and have detrimental effects on the mechanical properties of the manufactured components. This research contributes to the advancement of additive manufacturing by providing a reliable computational tool that integrates the complex interplay between thermal and mechanical phenomena. The developed framework enhances our understanding of the AM process, offering valuable insights into the factors influencing the structural integrity and performance of additively manufactured parts.
Artykuł
Otwarty dostęp
A universal convolutional neural network for the pixel-level detection and monitoring of weld beads
(Wydawnictwa AGH, 2024) Wang, Zhuo; Kayitmazbatir, Metin; Banu, Mihaela
In weld-based manufacturing processes such as welding and metal deposition additive manufacturing (AM), the weld bead is a direct indicator of manufacturing quality. For example, the geometry of the weld bead was optimized to a net shape which outperformed conventional geometries. Automatic monitoring of weld bead is thus of prime importance for welding process control and quality assurance. This paper develops a general-purpose convolutional neural network (CNN) for pixel-level detection and monitoring of beads, regardless of welding materials, machine, manufacturing conditions, etc. To achieve the generality, we collected a great variety of welding images containing 2677 single-line beads from 231 research articles, followed by pixel-wise hand-annotation. Consequently, the trained CNN can recognize different beads from various backgrounds at a pixel level. Case studies show that compared to the image-level classification in prior research, its pixel-level labeling permits real-time, complete characterization of weld beads (e.g., detailed morphology, discontinuity, spatter, and uniformity) for more informed process control. This research represents a significant step towards developing a truly human-like monitoring system with low-level scene understanding ability and general applicability.
Artykuł
Otwarty dostęp
An analytical model for the tool center point placement in Robotic Roller Forming
(Wydawnictwa AGH, 2024) Stewens, Thomas; Liu, Yi; Wang, Ling; Min, Junying
Robotic Roller Forming (RRF) is a novel process using an articulated robotic manipulator that can bend Ultra-High Strength materials into thin-walled profiles. For high strength or difficult-to-form sheet materials, a laser can be employed to synchronously heat and soften the local material during RRF. The aim of RRF is to establish itself as a highly flexible process for rapid prototyping as well as for small batch production. However, in finished parts formed with different materials, a new defect that shapes the profile like that of a hook was observed. To overcome this defect and to improve the adaptability of the process, a new analytical model is suggested for the automatic calculation of the tool center point based on the given process parameters. The model was compared to the previous state, where the hook defect was noticeably reduced. Additionally, the control of the bend radius was studied, and the resulting bend radius diverged from the target radius by 0.04 mm (2.45%). Further, when examining the reproducibility, the same bend angles could be achieved as in previous experiments using the constant laser power density. Finally, the development of the bend allowance was studied in various experiments. The analytical model for RRF is a promising method for calculating tool placement and controlling the bend radius in a freeform environment.