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U-NET-based deep learning for automated detection of lathe checks in homogeneous wood veneers
No full textAutomated detection of lathe checks in wood veneers presents significant challenges due to their variability and the natural properties of wood. This study explores the use of two convolutional neural networks (U-Net architecture) to enhance the precision and efficiency of lathe checks detection in poplar veneers. The approach involves sequential application of two U-Nets: the first for detecting lathe checks through semantic segmentation, and the second for refining these predictions by connecting fragmented lathe checks. Post-processing techniques are applied to denoise the mappings and extract precise lathe check characteristics. The first U-Net demonstrated strong performance in predicting lathe check presence, with precision and recall scores of 0.822 and 0.835, respectively. The second U-Net refined predictions by linking disjointed segments, improving the overall lathe checks mapping process. Comparative analysis with manual methods revealed comparable or superior performance of the automated approach, especially for shallow lathe checks. The results highlight the potential of the proposed method for efficient and reliable lathe check detection in wood veneers
Advanced Meta-Modeling framework combining Machine Learning and Model Order Reduction towards real-time virtual testing of woven composite laminates in nonlinear regime
Full text linkThis paper presents an advanced meta-modeling framework that efficiently combines Machine Learning and Model Order Reduction (MOR) techniques for real-time virtual testing of woven composite materials. The framework is specifically de signed to develop a multiparametric solution capable of accurately predicting the macroscopic nonlinear stress–strain curves of woven composite laminates submitted to loading–unloading paths. It takes into account five key microstructural parameters: yarn weft width, yarn warp width, yarn spacing, fabric thickness as well as the reinforcement orientation. The methodology employs the Proper Orthogonal Decomposition (POD) technique to decompose the stress–strain curves, extracting principal features that effectively characterize the overall composite’s response. Subsequently, a Random Forest machine learning model is applied to interpolate these features across the microstructural parameter space, allowing for rapid retrieval of corresponding features for any new laminate configuration in the nonlinear regime. A key advantages of this approach is its capacity to dynamically generate extensive virtual test databases, in real-time, across a wide range of composite laminate configurations. This capability provides a comprehensive and efficient tool for analyzing and optimizing composite performance while substantially reducing both experimental and computational costs. Furthermore, to enhance usability for engineers and researchers, this multiparametric solution has been integrated into a user-friendly Graphical User Interface (GUI) application. This GUI empowers users to easily explore various laminate configurations, visualize results, and conduct virtual testing, establishing the framework as a powerful tool for real-time virtual testing and in-depth analysis of microstructural effects on composite materials
A crystal plasticity-damage coupled finite element framework for predicting mechanical behavior and ductility limits of thin metal sheets
No full textA new crystal plasticity finite element (CPFE) approach is developed to predict the mechanical behavior and ductility limits of
thin metal sheets. Within this approach, a representative volume element (RVE) is chosen to accurately capture the mechanical
characteristics of these metal sheets. This approach uses the periodic homogenization multiscale scheme to ensure the transition
between the RVE and single crystal scales. At the single crystal scale, the mechanical behavior is modeled as elastoplastic within
the finite strain framework. The plastic flow is governed by a modified version of the Schmid law, which incorporates the effects
of damage on the evolution of microscopic mechanical variables. The damage behavior is modeled using the framework of
Continuum Damage Mechanics (CDM), introducing a scalar microscopic damage variable at the level of each crystallographic
slip system (CSS). The evolution law of this damage variable is derived from thermodynamic forces, resulting in deviations from
the normality rule in microscopic plastic flow. This coupling of damage and elastoplastic behavior leads to a highly nonlinear
set of constitutive equations. To solve these equations, an efficient return-mapping algorithm is developed and implemented
in the ABAQUS/Standard finite element software via a user-defined material subroutine (UMAT). At the macroscopic scale,
the onset of localized necking is predicted by the Rice bifurcation theory. The proposed damage-coupled single crystal model
and its integration scheme are validated through several numerical simulations. The analysis extensively explores the impact of
microstructural and damage parameters on the mechanical behavior and ductility limits of both single crystals and polycrystalline
aggregates. The numerical results indicate that both of the mechanical behavior and ductility limits are significantly influenced
by the microscopic damage and deviations from normal plastic flow rule
On adaptive sampling techniques for metamodels based on NURBS entities from unstructured data
Full text linkThe paper investigates the influence of adaptive sampling strategies on the generation of a metamodel based on Non-Uniform Rational Basis Spline (NURBS) entities, obtained from unstructured data, with the purpose of improving accuracy while minimising computational resources. The metamodel is defined as solution of a constrained non-linear programming problem and it is solved through a three-step optimisation process based on a gradient-based algorithm. Moreover, this paper introduces a generalised formulation of the NURBS-based metamodel capable of handling unstructured sampling data, enabling simultaneous optimisation of control points and weights. Sensitivity analyses are performed to evaluate the influence of various adaptive sampling techniques, including cross-validation-based and geometry-based strategies, on the resulting metamodel, in terms of accuracy and computational costs. Analytical benchmarks functions and a complex real-world engineering problem (dealing with the non-linear thermomechanical analysis of a part produced with the fused deposition modelling technology) are used to prove the effectiveness of the NURBS-based metamodel coupled with adaptive sampling strategies in achieving high accuracy and efficiency. © 2025 Elsevier B.V
Wabi-Sabi in Virtual Reality Sketching: Toward a Digital Creator’s Posture Change
Full text linkTraditional design promotes abundant, inexpensive, and disposable ways to create that are not compatible with sustainability. The authors explore alternatives to this paradigm by comparing a virtual reality sketch method with a new approach inspired by the traditional Japanese concept Wabi-Sabi. An experiment limited users’ amount of virtual paint and removed users’ ability to erase, and while participants’ creative approaches and processes changed, they were satisfied with the results. Combining Wabi-Sabi with digital technologies provides a concrete opportunity to “go forward” by incorporating sustainable considerations in practice and in the development of tools for digital artists and creators
Revisiting Dynamic Fracture in PMMA: The Interplay Between Local and Global Methods
No full textPolymethyl methacrylate (PMMA) is a benchmark brittle material for dynamic crack propagation studies. Despite extensive research, significant inconsistencies persist in reported fracture parameter values, complicating the establishment of a consensus on their sensitivity to the cracking regime. This study aims to rigorously determine these properties while identifying the origins of these discrepancies. To minimize microbranching effects that can strongly influence fracture surface roughness, crack propagation was restricted to subcritical velocities using a strip-band-specimen (SBS) geometry and a dedicated experimental setup. This approach ensured a quasi-steady propagation regime with minimal inertial effects. Dynamic toughness was evaluated using resistance curves constructed from Williams series expansion and displacement fields obtained via digital image correlation (DIC). Fracture energy was assessed through two complementary methods: a global energy balance and an indirect analytical approach based on Irwin’s generalized relation. Two distinct propagation regimes were identified: a stable regime (90 – 180
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, marking the onset of microbranches. A key outcome of this study is the validation of global fracture energy estimation through the local approach, and vice versa, allowing the derivation of one fracture property from the other – an unprecedented achievement for PMMA in dynamic crack propagation. This was made possible by the experimental setup and specimen geometry, which effectively minimized parasitic effects such as inertia and microbranching. Additionally, the findings confirm a strong correlation between surface roughness and the evolution of fracture energy from the earliest stages of dynamic propagation
Passive neck stiffness and range of motion for males and females from early to late adulthood
No full textMETHODS : Eighty participants aged 20 to 79 years (nearly even distribution), who self-reported no history of significant health conditions and with no neck pain, were recruited. Two custom apparatus were used to support participants in relaxed lying. Their head was rotated to maximum ROM; applied moment and head-torso motion were recorded. Muscle activation was monitored in real-time to ensure electromyographic signals from agonist muscles remained below a passive threshold. Stiffness was determined from the moment-angle data within each of three zones, with zone boundaries delineated to maximize moment-angle linearity within each zone. The age and sex effects on passive stiffness and ROM were assessed using generalized linear models for flexion and extension, and linear mixed models for lateral bending and axial rotation.
RESULTS : Passive neck ROM decreased by 0.2° per year of age in lateral bending and axial rotation for males and females, and extension ROM for males was 5.8° lower than for females. Passive stiffness in lateral bending (zone 1 and 2: 0.9 and 3.5 Nmm/°/year; zone 3: 3%), axial rotation (zone 1 and 2: 1%; zone 3 for males and females: 1.9 and 0.9 Nmm/°/year) and some zones in extension (zone 2: 0.8 Nmm/°/year; males in zone 3: 2.7 Nmm/°/year) increased with age, and males had higher stiffness than females in lateral bending (zone 1 and 2: 22.3 and 43.9 Nmm/°; zone 3: 35%) and axial rotation (zone 1 and 2: 49% and 35%).
CONCLUSIONS: Passive neck ROM decreased with age in lateral bending and axial rotation, while passive neck stiffness tended to increase with age in all motions but flexion. Extension ROM was higher for females, and lateral bending and axial rotation stiffness at lower angles were higher for males.
CLINICAL SIGNIFICANCE: The neck ROM, stiffness, and moment-angle corridors developed in this study provide benchmarks for clinical assessment of cervical spine function, and can assist the development of surrogate and computational models incorporating minimal muscle activation, for injury simulation and clinical skill training
Screen Printed Piezoelectric Transducers for Structural Health Monitoring of Curved Thick Composite Panels
Full text linkThis research focuses on the development and experimental validation of a novel printed piezoelectric transducers
network employed on a foreign object damage panel substructure of an aircraft engine fan blade. The main goal of the
work is to leverage the screen printing technology to fabricate arrays of piezoelectric transducers and ultimately employ
these transducers for operations, enabling the development of structural health monitoring methods for the panel. The
printed transducer is made up of a piezoelectric layer sandwiched between two silver electrodes, each printed in a
controlled manner. Upon printing and drying of the layers, the transducers undergo polarization. The electromechanical
behaviour of the printed transducers, characterized using impedance measurements, exhibits high repeatability, thus
indicating its potential for large scale industrial deployment. Following this, it is demonstrated that the transducers
are capable of accurately sensing impact, which is one the most common yet critical sources of damage to an engine
fan blade. It is also shown that the printed transducers are able to detect acoustic emission events. The ability of the
printed transducers to actuate and sense guided wave signals over a range of ultrasonic frequencies is also demonstrated.
Furthermore, apart from the noticeable advantages of the non-intrusive nature, and negligible weight as compared
to their traditional ceramic counterparts, the printed piezoelectric transducers can potentially be integrated into the
manufacturing process in the future, and the presence of transducer arrays ensures the availability of other transducers
in case of an individual failure during service. This innovative printing technology for PZT transducer networks thus
holds significant promise in bridging the gap between research advancements and the industrial implementation of SHM
technology
Digital Twins, Extended Reality, and Artificial Intelligence in Manufacturing Reconfiguration: A Systematic Literature Review
Full text linkThis review draws on a systematic literature review and bibliometric analysis to examine how Digital Twins (DTs), Extended Reality (XR), and Artificial Intelligence (AI) support the reconfiguration of Cyber–Physical Systems (CPSs) in modern manufacturing. The review aims to provide an updated overview of these technologies’ roles in CPS reconfiguration, summarize best practices, and suggest future research directions. In a two-phase process, we first analyzed related work to assess the current state of assisted manufacturing reconfiguration and identify gaps in existing reviews. Based on these insights, an adapted PRISMA methodology was applied to screen 165 articles from the Scopus and Web of Science databases, focusing on those published between 2019 and 2025 addressing DT, XR, and AI integration in Reconfigurable Manufacturing Systems (RMSs). After applying the exclusion criteria, 38 articles were selected for final analysis. The findings highlight the individual and combined impact of DTs, XR, and AI on reconfiguration processes. DTs notably reduce reconfiguration time and improve system availability, AI enhances decision-making, and XR improves human–machine interactions. Despite these advancements, a research gap exists regarding the combined application of these technologies, indicating potential areas for future exploration. The reviewed studies recognized limitations, especially due to diverse study designs and methodologies that may introduce risks of bias, yet the review offers insight into the current DT, XR, and AI landscape in RMS and suggests areas for future research
Very high order finite volume solver for multi component two-phase flow with phase change using a posteriori Multi-dimensional Optimal Order Detection
Full text linkIn this work we propose a very high-order compressible finite volume scheme with a posteriori stabilization for the computation of multi-component two-phase flow with phase change. It is based on finite volume approach using moving least squares (MLS) reproducing kernels for high order reconstruction of the Riemann states. Increased robustness is achieved by using the multi-dimensional optimal order detection (MOOD) method to get a high-accurate and low-dissipation scheme while maintaining boundedness and preventing numerical oscillations at interfaces and strong gradient zones. The properties of the proposed framework are demonstrated on classical test problems starting with convergence order verification on simple scalar advection test cases. More complex shock and more stringent tube tests with various water, steam and air concentration are then simulated and compared with available references in the literature. Finally, the ability of the proposed approach to compute multi-component flows with phase change is illustrated with the simulation of a liquid oxygen jet in gaseous hydrogen