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Computer Methods in Materials Science
ISSN 2720-4081 · AGH University Press · 726 artikel di indeks
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A Digital Twin for temperature prediction in the laser hardening process of NC10 steel
In this study, Artificial Neural Networks (ANN) were created to develop a Digital Twin (DT) for temperature prediction in the laser hardening process of NC10 steel. The ANN were trained to predict temperature on the top layer during the laser hardening process of NC10 steel samples with different…
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Artificial intelligence-enhanced algebraic multigrid for 3D finite element simulations
The paper presents preliminary investigations into a strategy for solving linear systems resulting from 3D finite element simulations, based on the algebraic multigrid (AMG) method, enhanced using artificial intelligence techniques. In particular, we adapt to 3D problems the algorithm presented in…
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Geometric optimization of two-stage stamping dies for ultra-thin titanium bipolar plates using Sequential Physics-Informed Neural Networks
Bipolar plates are critical core components in proton exchange membrane fuel cells (PEMFCs). Titanium-based materials are highly favored due to their excellent corrosion resistance and high specific strength. However, the plates often experience severe local thinning and poor consistency in forming…
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Detecting dents in car bodies using machine learning and structured light projection
This article discusses feasible methods for detecting dents in car bodies caused by transportation damage, commuting collisions, and hail. The authors review existing approaches exploiting their limitations, including smartphone-based ML detection algorithms and drive-through tunnels. The paper…
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Analysis of the decay time and bound-states energies of a particle in a specific structure GaMnAs/GaAs quantum well
The bound states and decay time in a certain quantum well structure (GaMnAs/GaAs) were analyzed and identified at the minimum decay time. Through the analysis of quantum mathematical equations, we derived specific formulas for energies that significantly amplify in the numerical solutions of…
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Statistical evaluation of the measurement of residual stresses in the surface layer of CP1000 steel sheets using the magnetic Barkhausen noise and X-ray methods
The paper presents an analysis of the possibility of measuring the residual stresses of metal sheets with the application of the so-called Barkhausen effect. The aim of the research was to compare the residual stress levels measured by two methods of multiphase steel sheets…
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The application of numerical simulations to analyze the forward extrusion process along with the verification of results and tuning of the numerical model
The paper presents the application of numerical simulations based on the Finite Element Method (FEM) for analyzing and optimizing the extrusion processes of aluminum and lead. These processes are efficient methods for manufacturing critical machine parts and metal components, ensuring excellent…
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Mechanical behavior of adhesive joints: A review on modeling techniques
In the pursuit of lighter designs, many industries are shifting from conventional fasteners to adhesive joints, which offer a better strength-to-weight ratio and facilitate the use of fiber-reinforced polymers. However, modeling adhesive joints presents major challenges due to the complex behavior…
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The advent of the deep learning evolutionary algorithm EvoDN2 and its recent applications
The evolutionary deep learning algorithm EvoDN2 is an emerging strategy for data-driven intelligent learning and many-objective optimisation capable of handling a large volume of noisy and non-linear data. This article provides the essential details of this algorithm and highlights a number of its…
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Full-field approaches for austenite-ferrite phase transformation simulations
Understanding the local evolution of phase transformations in steels, particularly the γ (austenite) → α (ferrite) transformation, is crucial for controlling the microstructure and properties of steel components. Over recent decades, significant progress has been made in the numerical modeling of…
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Development of a constitutive material model of Mo-Mn-Fe-Co-Ni high entropy alloy through a structured two-phase inverse analysis
High entropy alloys, characterized by their near-equimolar compositions of five or more elements, exhibit unique properties including high strength, thermal stability, and corrosion resistance, making them ideal candidates for demanding applications. Unfortunately, experimental research on their…
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High-fidelity modeling of interface crossing in the diffusion welding process at the polycrystalline scale
Controlling the microstructure of a diffusion welded interface is a critical point to ensure optimum mechanical properties and the homogeneity of the joint. Beyond the intimate contact formation between bonded parts studied in the literature, this article focuses on the grain boundary crossing of…
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An evaluation of discrepancies between CPFE simulations and mean-field approximations for dual phase materials
This paper explores the discrepancies observed between 2D and 3D crystal plasticity finite element (CPFE) simulations and mean-field approximations in terms of macroscopic flow curves. Two hypotheses are proposed to address the discrepancies: (1) the type of yield function in the mean-field…
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Evaluation of deformation inhomogeneity in multi-layered steel-titanium and steel-magnesium systems
In the presented study, plastometric tests using channel die compression were employed to define the boundary conditions for numerical simulations of the deformation processes of heterogeneous multi-layer systems composed of microalloyed steel, titanium, or magnesium. Various configurations…
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Polycrystalline plasticity analysis of cyclic loading and stress relaxation in 316H austenitic stainless steel
The mechanical behavior of 316H austenitic stainless steel is investigated in this study under cyclic strain-controlled loading with and without hold periods at elevated temperatures. Understanding the low-cycle fatigue (LCF) and fatigue-creep interaction (FCI) characteristics is essential for…
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Design and implementation of a digital infrastructure for autonomous open-die forging
Open-die forging is a key process for manufacturing large components such as generator shafts and crankshafts for ship engines. Despite its industrial relevance, the process remains dependent on manual labour and operator expertise, leading to challenges in process stability, reproducibility, and…
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Review of XAI methods for application in heavy industry
In recent years, considerable progress has been made in the field of artificial intelligence and machine learning. This progress allows us to solve increasingly complex problems, but it also requires providing appropriate explanations to understand the actions taken by AI. For this purpose…
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Plastic deformation mechanisms in BCC single crystals and equiatomic alloys: Insights from nanoindentation
Deformation plasticity mechanisms in alloys and compounds may reveal the material’s capacity towards optimal mechanical properties. We conducted a series of molecular dynamics (MD) simulations to investigate plasticity mechanisms due to nanoindentation in pure tungsten, molybdenum, and vanadium…
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A universal convolutional neural network for the pixel-level detection and monitoring of weld beads
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…
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Modeling the thermo-mechanical response and phase changes in metallic additive manufacturing (MAM) processes using a dissipative phase-field model
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…