Search Results (883)

The measurement of a probe impedance performed during eddy current inspections enables detection of flaws in electrically conductive materials. A correct interpretation of the measured impedance values constitutes a key aspect that determines the effectiveness of the inspections, and for this purpose, mathematical models are employed. Such models, which are becoming more and more frequently an integral part of eddy current measurement systems, enable carrying out the calculation of the probe impedance, through depicting the measurements being performed. What offer the shortest calculation time while maintaining high accuracy are analytical solutions. In this paper, to the best of the authors’ knowledge, this is the first time an analytical model of an eddy current probe placed over a small diameter cylinder containing a hole has been presented. The final formulas were obtained using the truncated region eigenfunction expansion (TREE) method, and then implemented in Matlab. The calculated values of the probe resistance and reactance were compared with the measurement results obtained for cylinders with a through defect. The tests were conducted on components made of several conductive materials with different geometric dimensions. The measurement error in all of the tests was small, i.e., it did not exceed 3% across the entire frequency range. The proposed solution can be used in defectoscopy for eddy current testing of tubes, pucks, washers, and any cylindrical elements. Full article

This review analyses and logically structures modern intelligent sensor technologies in the context of animal husbandry, feed production, and veterinary medicine. The main research discussed in the article focuses on machine learning based on modern neural network models, computer vision, and sensor systems that are transforming the methods for assessing the health, behavior, and nutrition of farm animals. The first part examines modern approaches to quality control and optimization of mineral and vitamin premixes, including visual inspection using visual sensors and neural networks. Key roles are played by precise dosing, component stability (minerals, vitamins), and the transition to more bioefficient organic forms of micronutrients to reduce environmental impact. Improvements in feed and premix production are analyzed, including automation, energy management, and the use of machine learning for non-destructive quality control, defect detection, mixing homogeneity assessment, and vitamin stability prediction. The second part analyzes methods for animal location and behavior detection. This article presents computer vision-based systems, including modifications of YOLO, for automatically tracking and classifying key behavioral patterns (lying down, standing, feeding, and aggression) in cattle and pigs, even in crowded conditions. It also discusses the use of ultra-wideband (UWB) systems and accelerometers combined with machine learning for high-precision positioning and detection of specific behavioral anomalies, such as lameness and playfulness. The third section focuses on the application of machine learning in veterinary diagnostics, including the automated interpretation of medical images (X-ray, ultrasound, and MRI) as sensor data streams for the diagnosis of cardiovascular, oncological, and orthopedic diseases in farm and small animals. Furthermore, the article examines the use of machine learning models for proactive disease diagnosis in farm animals and poultry based on multimodal data and image analysis. Considerable attention is given to methods and tools for radiometric diagnosis of animal diseases at an early stage using microwave sensors, as well as laser therapy and surgery in veterinary medicine. The review concludes that the integration of intelligent systems enables a transition to data-driven livestock management, significantly improving animal welfare and, consequently, the efficiency and sustainability of agricultural production. Full article

With the rise of Industry 4.0, digital manufacturing and smart measuring technologies are enabling the development of zero-defect manufacturing strategies, which leads to less material waste and lower energy consumption, moving from off-line metrology and dedicated measuring equipment to in-line measurements and automated inspection systems. This is especially important for the production and manufacturing of large-scale parts, because of the high component cost and long delivery cycle. However, establishing traceability for measurement systems is often complicated due to both the measurement technology and the objects being measured. Traceability of measurement in the manufacturing environment is not ensured yet, and uncertainty evaluation for in-process measurement remains a complex and active research challenge. This work introduces a new uncertainty modelling and evaluation framework for traceable measurement of the large-scale components in ‘shop floor’ conditions. The framework is verified using real data obtained from various instruments for in situ measurement of a large artefact. Experimental results demonstrate that uncertainty evaluation for large-scale metrology is crucial for precision manufacturing on the production floor. The methods can be extended to the evaluation of measurement uncertainty of components with a smaller size and off-line inspection. Full article

The Draw-a-Person (DAP) test has been a widely used practical instrument in psychological and developmental assessments to measure children’s cognitive development via human-figure drawings. Unfortunately, its traditional scoring process relies on manual inspections conducted by professionals, which is highly subjective and difficult to scale. In order to resolve these problems, this paper presents a hybrid approach that leverages deep-learning-based visual recognition and rule-based structural reasoning for automated evaluation of children’s DAP drawings. Specifically, the model assesses drawings based on 40 features, including anatomical parts, appearance-derived attributes, and high-level structural-drawing relations. A multi-label CNN built upon the ResNet-50 model predicts the visibles, and rule-based geometrical reasoning is adopted to infer structures, including attachments, proportions, symmetries, and placements. These two aspects are combined into a single hybrid representation yielding interpretable feature scoring consistent with developmental-evaluation standards. The proposed framework performs very well across multiple feature analyses, achieving a Micro-F1 of 95.32% and Macro-F1 of 91.72% on the test dataset, and demonstrating robust multi-label classification ability even on rare features. It provides a promising method for evaluating Draw-a-Person drawings, while offering reliable capabilities for feature analysis and scoring with accurate anatomical feature detection and reasonable structural and higher-level feature detection despite the challenging diversity of children’s drawing styles. The enforced rule-based structural reasoning improves interpretability and objectivity. Our future work includes extending the framework to cover further detailed DAP features. Full article

Achieving stable local focus-height measurement across different material surfaces is important for I-line-lithography-related inspection, where sub-micrometer height deviations can affect imaging quality, exposure uniformity, and subsequent autofocus performance. This study evaluates the local focus-height repeatability of a linear charge-coupled device (LCCD)-based focus metrology system under several I-line-lithography-related material-surface conditions. The prototype integrates fiber-coupled LED illumination, telecentric projection and imaging optics, reference marks, and a two-step localization procedure based on template matching and centroid estimation; the dual-wavelength source is treated as part of the fixed optical configuration. Tests were performed on silicon wafers, GaAs bright substrates, sapphire, infrared transmissive material, and SiC, covering different reflectivity levels and surface structures. The measured peak-to-valley repeatability was 35–37 nm for highly reflective samples and 40–54 nm for intermediate- or low-reflectivity and microstructured samples, all below the selected 70 nm conservative engineering criterion derived from the depth-of-focus estimate. These results indicate that the integrated LCCD measurement chain maintained stable local repeatability within the tested material-surface range, providing experimental support for further development of local focus metrology and precision optical inspection. Full article

This paper introduces a novel, domain-independent methodology for risk reduction based on the reverse engineering of sub-additive inequalities. The proposed approach establishes a robust and unifying theoretical framework for risk reduction that is applicable across a broad range of otherwise unrelated domains. As part of the methodology, a general sub-additive inequality is formulated and rigorously proved, together with sufficient conditions for its validity. The analysis demonstrates that, for the inequality to be reverse engineered, both the risk-controlling parameter and the measure of risk must be additive quantities. Several important special cases of general inequality are derived, and the influence of segmentation on risk reduction has been examined. The practical applicability of the proposed framework is demonstrated through diverse examples, including the reduction in expected failures through optimized inspection, mitigation of corrosion damage and environmental pollution, prevention of runaway reactions, enhancement of cutting tool reliability, and optimization of dose distribution to reduce health risks. Full article

The rise in resource consumption and environmental pressures are driving the development of a circular economy, in which the remanufacturing of machine and vehicle parts plays a significant role. Despite numerous studies on regeneration technologies (including additive manufacturing, cladding, and spraying), the literature lacks analyses concerning the non-destructive evaluation of the coating–substrate bond quality—when tested from the substrate side. The aim of this study was to evaluate the applicability of the ultrasonic method for the detection of defects (delaminations, detachments) in the adhesive joint between the regenerative coating and the base material. The study demonstrated that the ultrasonic method enables non-destructive, portable, and single-sided inspection of the integrity of the coating–substrate interface, making it a useful tool in industrial practice. Fifteen samples (including one reference sample) of varying thicknesses (from 1.9 µm to 1753.9 µm) were tested using ultrasonic probes with frequencies ranging from 1 MHz to 20 MHz. It was found that the adhesion of coatings approximately 74 µm thick can be estimated using an ultrasonic wave with a frequency of 1.66 MHz, whereas for coatings approximately 250 µm thick, adhesion can be estimated using higher frequencies, i.e., from 2.75 to 20 MHz. Full article

Strengthening existing structures and bridges allows us to continue using them, increase their reliability, resistance, durability and extend their service life instead of demolishing them and replacing them with new ones. This helps to reduce CO₂ (decarbonization). The use of prestressed CFRP strips represents the use of new modern materials and new technology for strengthening existing bridges. The paper is focused on the use of prestressed CFRP strips for strengthening a concrete bridge made of precast prestressed box girders as the most suitable strengthening alternative in a given case. This is a technology that is more commonly used for strengthening structures, but it is not common to use this technology for strengthening bridges. There are relatively few examples of using this technology for strengthening bridges, also because these are dynamically loaded structures. The paper firstly presents the diagnostics and calculation of the load-carrying capacity of the railway bridge on a narrow-gauge railway line in Štrbské Pleso, Slovakia, and then the strengthening of the given bridge. The bridge is located in the mountains of the High Tatras in the northern part of Slovakia and bypasses two local roads. The bridge was made from the precast prestressed post-tensioned box girders of six single spans. The visual inspection, diagnostics, and verification of real dimensions and material characteristics were requested. The non-destructive and semi-destructive methods of testing were used to determine the geometrical and materials’ properties. After that, the calculation of the load-carrying capacity was done. For this purpose, a numerical 3D FEM model was created. For determining the load-carrying capacity, the standard approach, given in Eurocodes, was used according to provisions, which take into account the modified (lower) reliability levels and their adequate partial safety factors. From the calculation, it follows that the bridge should be strengthened. The strengthening of the superstructure was done using prestressed CFRP strips in the lower part of the box girders. This is one of the first applications of this modern method of strengthening, not only in Slovakia but in Central Europe as well. Full article

Unmanned aerial vehicles (UAVs) have wide application prospects in disaster search and rescue, ecological monitoring and environmental inspection tasks, where target search is a key link to realize autonomous task execution. UAVs often face challenges related to limited onboard computational resources and inefficient environmental coverage when used for target search. To address these issues, this paper proposes an autonomous search method for UAVs based on combined lightweight target detection and coverage path planning. In this method, the target search task was decomposed into two core parts: target recognition and path planning. Firstly, in terms of target recognition, the YOLOv8n model was subjected to channel pruning and INT8 quantization to reduce its computational complexity, while HSV space data augmentation was incorporated to enhance recognition robustness in complex environments. Secondly, path planning was formulated as a dual-layer task comprising “spatial coverage + target confirmation.” A grid-based search environment model was constructed, and a coverage path planning strategy was put forward that integrated breadth-first search (BFS) with local greedy optimization to achieve efficient traversal of predefined search areas. Simultaneously, the A* algorithm was employed for path backtracking to cover omitted regions. Finally, a simulation platform for UAV target search was built to validate the recognition performance and search efficiency of the proposed method. The experimental results demonstrated that the proposed method significantly improved the UAV target search efficiency and reduced the path redundancy while ensuring the recognition accuracy, thereby offering an effective solution for autonomous UAV search on resource-constrained embedded platforms. Full article

Building fires involve numerous interacting hazard factors, making it difficult to identify which combinations are most likely to cause an incident and to design targeted interventions. Existing methods address only part of this problem: structural approaches map causal pathways but cannot quantify the probability of specific factor combinations, while probabilistic models compute risk values but offer little guidance on where to intervene. To bridge this gap, we develop the Causal Hierarchy Model (CHM), a data-driven framework that integrates causal structure analysis with probability calculation. Factor influence is derived from empirical co-occurrence data to distinguish driving factors from dependent ones. A hierarchical structure is then constructed using two layering rules, revealing causal transmission gradients and critical hub nodes. Finally, coupling probabilities are computed within the hierarchical constraints and weighted by the influence of hubs. Applying CHM to building fire records from California reveals clear functional differentiation among hazard factors. Coupling strength attenuates asymmetrically across hierarchy levels but amplifies sharply along pathways that pass through high-prominence hubs. By uniting structure and probability, CHM provides a quantitative basis for shifting fire safety management from uniform inspection toward risk-differentiated strategies. Full article

Explainable artificial intelligence is increasingly needed in high-stakes tabular classification, where predictions should be accurate, auditable, and easy to inspect. We propose GRS-ANFIS, a role-separated neuro-fuzzy model that decomposes inference into a Primary module for main decision formation and a Complementary module for targeted correction. During differentiable training, sigmoid gate values are applied only to consequent coefficients, while the antecedent part receives the original input without soft masking. After each stage, the learned gates are binarized into hard routing masks that define discrete antecedent and consequent subsets for module-specific fine-tuning. The Complementary module is restricted to variables not selected by the Primary module, yielding explicit role separation and disjoint variable usage across modules. To support stable learning in high-dimensional settings, all ANFIS-family models use the same HTSK-style firing computation. Experiments on four tabular benchmarks show that GRS-ANFIS achieves competitive predictive performance while maintaining compact, role-separated rule structures; rule-count compactness is clear, whereas the unified Nauck/HFSi interpretability values are dataset- and variant-dependent. Boundary-focused analysis further shows that the Complementary module mainly improves difficult, low-confidence samples through targeted correction. Full article

This review paper presents a comprehensive and system-oriented analysis of advanced non-destructive testing (NDT) technologies for metal additive manufacturing (AM), including X-ray computed tomography (XCT), ultrasonic testing (UT), infrared thermography, acoustic emission (AE), and electromagnetic techniques. While the existing literature often focuses on the physical principles of individual NDT methods, this work addresses a critical knowledge gap by analyzing NDT as a digitally integrated “quality intelligence layer” rather than a standalone post-process inspection tool. The primary motivation is to bridge the disconnect between raw inspection data and cyber–physical production systems. Particular focus is given to NDT data analytics and digitalization, where machine learning (ML) and digital twin (DT) integration are discussed as fundamental enablers of intelligent manufacturing. The review systematically examines image and signal processing pipelines required for quantitative defect characterization, highlighting challenges related to voxel resolution, signal-to-noise ratio, anisotropic microstructures, and operator dependency. It further analyzes supervised learning, deep learning, and multi-sensor data fusion approaches for automated defect classification and predictive quality assessment. Furthermore, the role of digital twins in coupling in situ monitoring data, ex situ NDT results, and physics-based models is discussed as a transformative pathway toward closed-loop process control and evidence-based certification. By synthesizing NDT science with digital manufacturing architectures, this review contributes a unique framework for transitioning from traditional inspection-centric quality control to a predictive, adaptive, and digital twin-enabled quality assurance paradigm. The work concludes by identifying key research gaps in data standardization and computational scalability, providing a strategic roadmap for the future of smart AM production. Full article

Medical device manufacturing requires strict quality control, reliable traceability, and compliance with regulatory requirements. In many cases, inspection activities are still carried out manually and production information is recorded separately, which can result in inconsistent defect detection and limited visibility of manufacturing performance. This paper presents the development and industrial implementation of an integrated closed-loop quality control architecture for a sterile single-use medical device assembly line, addressing the lack of integration between inspection, traceability, and control systems in existing manufacturing approaches. In the proposed approach, we combine radio-frequency identification, machine vision inspection, programmable logic control, and centralized production monitoring. RFID tags store the status of each unit at individual stations so that defective products cannot proceed to downstream operations. Machine vision systems verify component presence, detect missing parts, and confirm color-specific assembly requirements during production. The architecture was tested through implementation on an assembly line and evaluated with comparative pilot studies against a traditional manual inspection process. The upgraded line achieved scrap cost reductions of 52.77% and 53.23% while also improving inspection consistency and production traceability. The results demonstrate that integrating machine vision inspection with RFID traceability can significantly improve quality control and manufacturing efficiency in regulated medical device production. Full article

Impact-based acoustic inspection provides a rapid non-destructive approach for screening metallic components by analyzing the sound radiated after a controlled mechanical excitation. However, the limited availability of labeled data from defective parts remains a major challenge for deploying deep learning classifiers in production. This paper proposes a complete pipeline that converts raw impact-response audio recordings into magnitude log-spectrogram images and trains a semi-supervised Wasserstein GAN with gradient penalty (SS-WGAN-GP) designed to operate under extreme data scarcity. The architecture couples a shared convolutional backbone with two output heads: a Wasserstein critic for unsupervised discrimination between real and generated samples, and a binary classification head for supervised quality labeling, jointly optimized through a combined loss that balances Wasserstein distance, gradient penalty, and cross-entropy. A key property of the design is that the generator acts as a source of synthetic training samples, producing progressively more realistic spectrograms as training advances. These samples, in turn, enrich the feature representations learned by the shared backbone and improve the performance of the classification head. The classification head of the trained discriminator is deployed directly as the quality classifier, without requiring external data or post hoc retraining. Full article

The textile industry faces machinery maintenance challenges due to reactive practices, lack of real-time monitoring, and absent integrated management systems, resulting in unplanned downtime, elevated costs, and quality variability. This study addresses these limitations by proposing a hybrid predictive–prescriptive framework integrating XGBoost 3.2.0 and LSTM models with a multi-objective optimization engine to generate data-driven maintenance recommendations. The framework was validated on four critical components, needles, hooks, needle guides, and thread tensioners, using operational data from a textile plant (November 2024–January 2026). Plant-wide Mean Time Between Failures increased by 38% (15–21 to 24–28 h), while Mean Time To Repair decreased by 15% (5.31 to 4.6 h). These improvements yielded 5.5% lower maintenance costs, 9% less fabric waste, and reduced cost per operating hour from $25 to $23.5. The prescriptive module transformed imperfect predictions into robust decisions by evaluating interventions against production constraints, spare parts availability, and risk criteria. Beyond quantitative gains, the framework enabled sustainable practices including data-driven spare parts policies and condition-based inspections. This work demonstrates that integrating prediction with prescription effectively overcomes structural maintenance challenges in textile manufacturing, providing a replicable methodology for broader industrial adoption. Full article