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Grain size impact on sheet metal behavior via CPFEM
No full textA novel multiscale computational framework based on Crystal Plasticity Finite Element (CPFE) modeling is proposed to investigate the effect of grain size on the mechanical behavior and ductility limits of thin metal sheets, featuring both uniform and gradient grain structures. This approach relies on designing unitcell models that reflect the microstructural characteristics of thin metal sheets. The overall response of the unit cell is obtained from that of its single crystal constituents using the periodic homogenization scheme. At the single crystal level, the mechanical behavior is modeled within a finite strain, rate-independent plasticity framework, where the plastic flow is governed by the classical Schmid law. The effect of individual grain size is incorporated at the single crystal scale by adjusting the critical resolved shear stress (CRSS) evolution, using a combination of the microscopic Hall–Petch relationship and a dislocation density-based hardening model. To efficiently solve the single crystal constitutive equations, a return-mapping algorithm coupled with the Fischer–Burmeister complementarity function is developed and implemented into ABAQUS/Standard through a user-defined material subroutine (UMAT). At the macroscopic level, the ductility limits are predicted by the Rice bifurcation theory. The performance of the proposed strategy is validated through a series of polycrystalline aggregate simulations. The numerical results demonstrate a significant influence of grain size on both the macroscopic strength and ductility limits of polycrystalline aggregates. Additionally, the introduction of gradient grain structures is shown to substantially enhance both strength and ductility. These findings provide valuable insights for optimizing material performance in engineering applications
Investigation of a constitutive law for the prediction of the mechanical behavior of WEEE recycled polymer blends
Full text linkThis research focuses on a mechanical study of an acrylonitrile–butadiene–styrene (ABS)/ polycarbonate (PC) blend totally derived from Waste Electrical and Electronic Equipment (WEEE) recycling. First, an experimental work was developed in laboratory for the preparation of different mixtures of ABS/PC blend. Then, mechanical tensile tests were performed on the injected specimens and the stress/strain experimental data were gathered to be used in the modelling part. In order to enable the prediction of the mechanical response of the blend, G’Sell and Jonas constitutive law was considered for this purpose. An optimization method based on the Generalized Reduced Gradient (GRG) nonlinear algorithm was developed to identify the input parameters governing the mechanical model. In addition, an uncertainty parametric study was assessed to qualitatively and quantitatively evaluate the constitutive law sensitivity versus the parameter uncertainty. Monte Carlo simulations were performed and the convergence of the numerical model was proved in terms of means and standard deviation statistical data. The results showed an excellent agreement between the numerical approach and the experiments. Besides, it was highlighted the crucial role of coupling uncertainty parametric study with modelling for accurately describing the mechanical behavior of the blend
Real-time forging process control: integrating billet-related surrogate and machine behavior models
Full text linkThis study introduces a predictive surrogate model for real-time control in cold upsetting processes, incorporating both material and machine behaviors. Traditional approaches often simplify machine behavior as rigid or with constant stiffness; however, the proposed method dynamically couples material and machine responses, accounting for efficiency changes across different upsetting operations. This is achieved through the integration of a data-driven billet-related surrogate model with a machine-related analytical blow efficiency prediction, accurately capturing elastic energy losses. For the construction of the surrogate model in this use case, a multilayer perceptron artificial neural network (MLP ANN) was employed, demonstrating high predictive accuracy with a dataset comprising 2000 entries generated using Latin Hypercube Sampling (LHS) and numerical simulations. The model provides precise predictions for key outputs like forging load and plastic energy. Experimental validation shows prediction errors below 5% for energy setpoints, reduced to under 1% with blow efficiency correction. The general methodology of surrogate model creation can be adapted for various metal-forming processes, providing a versatile framework for real-time simulation and control
Identification of key evaluation criteria for co-development of XR applications in industrial contexts
No full textExtended Reality (XR) technologies—including Virtual, Augmented, and Mixed Reality—offer useful possibilities for improving industrial processes such as training, design validation, maintenance, and quality control. However, their adoption in industry remains limited, often because existing solutions do not fully meet practical needs. This study focuses on the co-development of XR applications better adapted to industrial requirements. In a first phase, a literature review helped identify 37 evaluation criteria, grouped into eight categories. Definitions were refined based on expert input. In a second phase, over 20 professionals from different industrial sectors assessed the relevance of each criterion using a 5-point Likert scale and a ranking method. The results showed differences between academic and industrial perspectives. While academic work often highlights technical or sensory aspects, indus-trial stakeholders emphasized usability, relevance of content, and potential for innovation. These find-ings provide a clearer view of industry expectations and will inform future development of XR tools
Damage Assessment of Polyamide-Based Woven Composites Using Multi-Directional Lamb Waves After Fatigue or Impact Loading
Full text linkThis study presents a novel experimental methodology designed to assess damage in woven glass fibers reinforced polyamide 6,6/6 composites, specifically subjected to low-velocity impact and cyclic tensile loading. Conventional ultrasonic testing techniques often fail to detect subtle material degradation, particularly when dealing with barely visible impact damage (BVID), which can go unnoticed but still significantly compromise structural integrity. In contrast, the proposed approach utilizes multi-directional ultrasonic Lamb wave analysis, a more advanced technique that offers greater sensitivity and precision in identifying damage at various stages of the composite’s lifespan. In this work, a damage indicator is defined based on the velocity profile of Lamb waves, which are sensitive to changes in material properties such as stiffness degradation. The Lamb wave-based methodology is rigorously validated through detailed comparisons with X-ray tomography. These comparisons reveal strong correlations between the two techniques, highlighting the effectiveness of the proposed ultrasonic approach in detecting BVID. Moreover, the study demonstrates that this methodology is not only highly sensitive but also scalable, making it suitable for industrial applications where automated inspection of composite components is essential. The proposed method offers a significant advancement in non-destructive testing (NDT) techniques based on Lamb wave diagnostic tools in composite material testing
Physics-informed deep neural networks towards finite strain homogenization of unidirectional soft composites
No full textThe presence of heterogeneities and significant property mismatches in soft composites lead to complex be haviors that are challenging to model with conventional analytical or numerical homogenization techniques. The present work introduces a micromechanics-informed deep learning framework to characterize microscopic displacements and stress fields in soft composites with periodic microstructures undergoing finite deformation. The main obstacle we address is the construction of specific loss functions incorporating intricate knowledge of finite strain homogenization theory, which is valid for arbitrary macroscopic deformation gradients. Notably, a multi-network model is utilized to describe the discontinuities in material properties and solution fields within the composites. These neural networks communicate with each other through interface traction and displacement
continuity conditions within the loss function. In addition, to exactly impose the periodicity boundary in hexagonal and square unit cells, the neural network architectures are modified by incorporating a number of trainable harmonic functions. A significant advantage of the current framework is that it allows for a straight forward solution of the governing partial differential equations expressed in terms of the first Piola-Kirchhoff stresses, eliminating the need for iterative formulations of the residual vector and tangent matrix required by classical numerical methods. We extensively assess the effectiveness of the proposed approach upon extensive comparison with isogeometric analysis to determine the displacement and Cauchy stress fields in square and
hexagonal arrays of fibers/porosities, demonstrating neural networks as a powerful alternative to the conventional numerical approaches in finite deformation analysis of microstructural materials
Comparison of parametric model order reduction methods to solve magneto-quasistatic and electro-quasistatic problems
No full textIn this paper, we compare two parametric model order reduction methods, the multi-moment matching method and the interpolation of projection subspaces method for the magneto-quasistatic (MQS) and electro-quasistatic (EQS) problems derived from Maxwell’s equations and discretized with the Finite Element (FE) method. The two problems considered are both governed by the differential–algebraic equations. The material characteristic parameters as well as the geometry parameters have been considered. The applications are two realistic test cases: an EQS model of a transformer bushing under insulation defect uncertainty and a MQS model of a planar inductor with geometric and material variations. The result shows that both methods approximate well global quantities, such as the current or the voltage, as well as the local quantities like field distributions. The multi-moment matching method remains always faster in the online stage, since the reduced basis is not parameter dependent, requiring no reduced basis calculation. The multi-moment matching method requires an affine decomposition of the FE model, which is not easy to obtain when considering geometry parameters. A hybrid method is proposed and tested leading to more accurate results than the interpolation of projection subspaces method but much easier to implement than the multi-moment matching method
Exploring the usability and creativity enhancement of augmented reality in additive manufacturing-based product design
No full textAugmented Reality (AR), a technology that overlays digital content onto the physical environment, holds promise for enhancing creativity and usability in product design education. However, despite the advantages of Additive Manufacturing (AM) in enabling complex and customizable designs, designers often struggle to grasp its abstract principles. Grounded in theories of immersive learning and multimodal visualization, this study investigates whether integrating AR visualization can facilitate better understanding and stimulate creativity in AM education. A controlled experiment was conducted with 34 master's students in product design, randomly assigned to either an AR-based learning group or a traditional card-based learning group. Participants engaged with AM principles through either an interactive AR application featuring manipulable 3D cube models or static information cards. Usability perceptions and creativity of design outputs were assessed respectively through structured questionnaires and expert evaluations by five domain specialists. Mann–Whitney U tests, appropriate for non-normally distributed data, revealed that the AR group reported significantly higher usability ratings and produced more original design outcomes compared to the card-based group. These findings demonstrate that AR-based educational tools can directly improve the usability and creative engagement of students in learning AM principles. This study contributes to advancing the understanding of how immersive technologies can be effectively integrated into design education to foster both practical skills and innovative thinking
Influence of the defect size, type, and position on the High Cycle Fatigue behavior of Ti-6Al-4V processed by laser powder bed fusion
No full textThe present paper analyzes separately the effect of the features of the typical AM defects on the fatigue resistance of Ti-6Al-4V alloy. To do so, different defect population in terms of sizes and morphologies were obtained by varying the L-PBF process parameters. The distance of these defects with respect to the surface was also controlled. A uniaxial fatigue testing campaign (R = −1) has then been conducted. The results have showed great influence of the nature of the defect population on the fatigue strength of Ti-6Al-4V alloy, for the case of surface crack initiation. The results have also allowed to quantify the criticality of internal defects with respect to their sizes and showed that the defect morphology has no influence on the fatigue strength for the case of internal crack initiation
Converging narrow-channel flow of a super-critical fluid
Full text linkThe solution of a supercritical fluid flowing into a constricted narrow channel is presented in this study. The compressible Navier-Stokes equations in the lubrication limit coupled with the energy equation and the isothermal and non-isothermal van der Waals fluid and perfect gas have been solved. In order to find the semi-analytical solution of these non-linear coupled equations, homogenization technique in the transverse direction has been applied. Because of the high compressibility and high thermal expansion of supercritical fluids, waviness is observed in the flow and thermal fields near the exit of the channel. This effect is attributed to the channel constriction where the slope is maximum, where a strong coupling between the pressure and density gradients exists. Moreover, the density difference between the exit and inlet of the channel drastically increases when one approaches the critical point, corroborating the data from existing literature