6558 research outputs found
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Automated classification of subsurface impact damage in thermoplastic composites using depth-resolved terahertz imaging and deep learning
No full textReliable detection of barely visible impact damage is critical to ensure the structural integrity of composite components in service, particularly in safety-critical applications such as pressure vessels and transportation systems. This study presents a solution for detecting such damage in woven glass fiber-reinforced thermoplastic composites using terahertz (THz) time-of-flight tomography and convolutional neural networks. THz provides non-contact, non-ionizing, high-axial-resolution imaging of subsurface and back-surface damage, addressing key limitations of surface-based inspection methods. While THz imaging alone may not always permit
conclusive damage identification, we bridge this gap by training neural network classifiers on depth-resolved THz B-scan images using ground truth from co-located X-ray micro-computed tomography. Among several pretrained architectures tested via transfer learning, DenseNet-121 exhibits the highest accuracy. The model remains robust even when trained on truncated B-scans excluding surface indentation features, confirming its ability to detect structural anomalies located internally or on the back surface. This is particularly relevant for applications where back-side access is not feasible. Experimental validation is performed on impacted
glass-fiber-reinforced thermoplastic coupons prepared in accordance with ASTM D7136, with damage severity quantified through forceโdisplacement data and micro-tomographic analysis. Labeling for supervised learning conforms to acceptance criteria from industrial standards for composite pressure vessels (ASME BPVC Section X, CGA C-6.2), ensuring regulatory alignment and enabling deployment in quality control workflows. The proposed method minimizes the need for expert interpretation or secondary validation and offers direct applicability to in-service inspection and manufacturing quality control
Why Artificial Intelligence Challenges the Foundations of Technology Acceptance Models
No full textDespite decades of refinement, technology acceptance models such as the Technology Acceptance Model (TAM; Davis, 1985, 1989) and the Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003) remain the dominant frameworks for evaluating digital technologies. Their resilience reflects robustness and parsimony. Yet Artificial Intelli-gence (AI) changes the game. Unlike earlier systems, AI learns, adapts and acts, increasingly participating in the decisions, challenging the very assumptions on which TAM/UTAUT rest. As Venkatesh himself admitted, the acceptance of AI tools remains โa question markโ, raising doubts on the adequacy of established models (Venkatesh, 2022). Drawing on a semi-systematic literature review (12,048 publications from 1985โ2025, including 155 focused on AI ac-ceptance), we show that while TAM/UTAUT still account for nearly 70% of studies, the field has entered a phase of conceptual displacement. Three converging dynamics stand out: an affec-tive and experiential turn, a vulnerability-centered perspective and a socio-technical orientation. Together, they crystallize into three new research streams: trust-centered, adoption-oriented and ethics-centered, that shift the field away from individual-utilitarian framings toward relational, organizational and governance logics. The challenge ahead is clear: to decide whether constructs such as trust, affect, privacy, ethics and anthropomorphism are merely contextual moderators or the building blocks of a new paradigm. The age of AI calls for more than incremental refine-ments, it demands a shared theoretical framework capable of steering organizations and societies through both the promises and risks of intelligent systems
Simulation of shockless spalling fragmentation using the Discrete Element Method (DEM)
No full textIn the present study a Discrete Element Method (DEM) is considered to model the dynamic behaviour and fragmentation mechanisms of alumina ceramic under high strain-rate shockless loading. GEPI (high-pulsed power) spalling experiments are simulated. The DEM allows to take into account the accurate propagation and interaction of stress waves within the samples upon calibration of microscopic bond parameters. The results indicate that a standard failure criterion can effectively represent the spalling phenomenon, though discrepancies with experimental data increase at higher strain rates. To address this, the study combines the DEM approach with a damage law, specifically the first and second order Kachanov damage law, to
model crack initiation and propagation. Comparative analysis with experimental rear face velocity profiles validates the approach. The strain-rate sensitivity of the present DEM model is explored using loading pulses of increasing intensity that induce different strain-rate levels. This research demonstrates that the DEM approach can effectively model dynamic behaviour in brittle solids leading to a multiple fragmentation sensitive to the strain rate
Automated classification of subsurface impact damage in thermoplastic composites using depth-resolved terahertz imaging and deep learning
No full textReliable detection of barely visible impact damage is critical to ensure the structural integrity of composite components in service, particularly in safety-critical applications such as pressure vessels and transportation systems. This study presents a solution for detecting such damage in woven glass fiber-reinforced thermoplastic composites using terahertz (THz) time-of-flight tomography and convolutional neural networks. THz provides non-contact, non-ionizing, high-axial-resolution imaging of subsurface and back-surface damage, addressing key limitations of surface-based inspection methods. While THz imaging alone may not always permit conclusive damage identification, we bridge this gap by training neural network classifiers on depth-resolved THz B-scan images using ground truth from co-located X-ray micro-computed tomography. Among several pretrained architectures tested via transfer learning, DenseNet-121 exhibits the highest accuracy. The model remains robust even when trained on truncated B-scans excluding surface indentation features, confirming its ability to detect structural anomalies located internally or on the back surface. This is particularly relevant for applications where back-side access is not feasible. Experimental validation is performed on impacted glass-fiber-reinforced thermoplastic coupons prepared in accordance with ASTM D7136, with damage severity quantified through forceโdisplacement data and micro-tomographic analysis. Labeling for supervised learning conforms to acceptance criteria from industrial standards for composite pressure vessels (ASME BPVC Section X, CGA C-6.2), ensuring regulatory alignment and enabling deployment in quality control workflows. The proposed method minimizes the need for expert interpretation or secondary validation and offers direct applicability to in-service inspection and manufacturing quality control
Experimental and modeling approach for estimating the psychological adaptation and perceived thermal comfort of occupants in indoor spaces
Full text linkThis study proposes a methodology for examining the relationship between environmental thermal conditions and occupant's perceived thermal comfort evaluation. Therefore, their psychological adaptation was examined to quantify and incorporate it in thermal comfort evaluations. To achieve the closure of the model's system of equations, experiments are carried out in which subjects are exposed to various thermal conditions in an enclosed space that simulates an office indoor environment; thermal measurements and perceived data are collected. Thus, the study aims to evaluate the adaptive factor that causes the difference between the physiological evaluation and the subjectsโ actual thermal perception. This adaptive factor is linked to the physical stimuli experienced owing to the thermal environment and the cognitive information within the occupant's memory systems; thus, the closure equation is derived from the outdoor air temperature and indoor operative temperature
Experimental study of capillary impregnation and wettability effects in porous cotton fiber structures
Full text linkThe study of capillary flows in cellulose fibers is important for various applications, including biomass pyrolysis
and drying processes. This work investigates the behavior of cotton fibers during capillary impregnation using
a dynamic approach. The analysis utilizes the Washburn equation and tensiometric methods to investigate
geometric factors, apparent advancing contact angles, surface free energy of cotton fiber, and capillary pressure.
The research is carried out in two phases. The first phase focuses on the theoretical application of the Washburn
equation in porous cotton fibers, specifically examining capillary wicking behavior within a cylindrical holder.
The second phase involves experimental analysis, using three different liquids: n-heptane, water, and glycerol.
The surface tension of the liquids was measured, and the capillary impregnation process was characterized
through the determination of geometric factors, apparent advancing contact angles, and surface free energy.
The geometrical factors of cotton fibers within the sample holder were found to be 10.39 ยฑ 1.28 mm5. The
apparent advancing contact angles for water and glycerol were 74.93โฆ ยฑ 2.20โฆ and 69.55โฆ ยฑ 1.83โฆ, respectively
Enhancing Asynchronous Learning in immersive Environments: Exploring Baseline Modalities for Avatar-Based AR Guidance
Full text linkThis study investigates baseline modalities for evaluating Augmented Reality (AR) avatar guidance in asynchronous collaboration on spatially complex tasks. A formative study with three participants compared smartphone video, HoloLens video, and AR avatars across usability, collaboration, learning, and spatial awareness. Results suggest smartphone video as a reliable baseline due to usability and familiarity. Avatars showed potential for enhancing spatial awareness, task engagement, and learning outcomes but require interface improvements. Despite the small sample size, this study offers insights into immersive technologies for industrial training and collaboration
Haptic Shape Discrimination in Virtual Environments Using Force Direction
Full text linkShape discrimination of objects relies on sensory and contextual cues. While existing studies explored cues for shape discrimination, an underexplored question remains what the minimal haptic cue (one kind of the sensory cues) is sufficient for such discrimination with contextual cues in virtual environments (VE). This study examined whether the changes of force direction โ as a haptic cue โ could serve this sufficiency. The results of the study confirmed the sufficiency for the discrimination under certain conditions. This confirmation implied a potential of applying force direction to simplify the design of haptic cues for VE applications
Comparison of Different Bipolar Construct Configurations for the Correction of Adult Spine Deformity: A Finite Element Analysis
No full textPurpose
A minimally invasive bipolar spinal fixation was recently developed to correct the deformity in pediatric neuromuscular scoliosis and has recently been adapted for adult scoliosis. Although the clinical results are promising, mechanical complications are still not negligible. In this work, alternative configurations of bipolar constructs were compared through numerical simulation, in order to evaluate stress distribution along the implant according to each configuration.
Methods
The configurations included doubling the rods, adding lumbar screws to strengthen the distal anchorage, and combining two different materials (titanium and chromium-cobalt alloy). This resulted in seven different configurations, which were implemented in a subject-specific and experimentally validated finite element model, based on the geometry of an asymptomatic subject. Von Mises stresses were compared between configurations.
Results
The results confirm that doubling the rods reduced mid-rod stresses, as expected, but also shifted some of the load from the distal anchorage to the rods, which is a common site of implant failure. The addition of pedicle screws also reduced the stress in the distal anchorage. The configuration showing the best compromise between stress reduction and the mini-invasive character of surgery included a doubling of both rods in titanium.
Conclusions
These results should be confirmed by clinical results, but they already provide clear guidelines for the surgeon
Safeguarding worker psychosocial well-being in the age of AI: The critical role of decision control
No full textAdvancements in artificial intelligence (AI) have ushered in the era of the fourth industrial revolution, transforming workplace dynamics with AI's enhanced decision-making capabilities. While AI has been shown to reduce worker mental workload, improve performance, and enhance physical safety, it also has the potential to negatively impact psychosocial factors, such as work meaningfulness, worker autonomy, and motivation, among others. These factors are crucial as they impact employee retention, well-being, and organizational performance. Yet, the impact of automating decision-making aspects of work on the psychosocial dimension of human-AI interaction remains largely unknown due to the lack of empirical evidence. To address this gap, our study conducted an experiment with 102 participants in a laboratory designed to replicate a manufacturing line. We manipulated the level of AI decision supportโcharacterized by the AI's decision-making controlโto observe its effects on worker psychosocial factors through a blend of perceptual, physiological, and observational measures. Our aim was to discern the differential impacts of fully versus partially automated AI decision support on workers' perceptions of job meaningfulness, autonomy, competence, motivation, engagement, and performance on an error-detection task. The results of this study suggest the presence of a critical boundary in automation for psychosocial factors, demonstrating that while some automation of decision selection can nurture work meaningfulness, worker autonomy, competence, self-determined motivation, and engagement, there is a pivotal point beyond which these benefits can decline. Thus, balancing AI assistance with human control is vital to protect psychosocial wellโbeing. Practically, industry and operations managers should keep employees involved in decision making by adopting partial, confirmโorโoverride AI systems that sustain motivation and engagement, boosting retention and productivity