Search Results (12)

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

We analysed the formal inspection reports of 108 licensed British zoos covering three consecutive formal inspections, equivalent to a licensing period, under the Zoo Licensing Act 1981 (ZLA). We examined the compliance of zoos against animal welfare standards, animal escape protocols and engagement in conservation measures, as well as the effects of licence type, zoo association membership, and collection type. Of the 324 inspection reports analysed, 59 (18%) reported that the zoo had passed every assessed question. Failure to undertake the necessary number of escape drills was the most reported area of non-compliance in 134 (41%) of the reports. Across a total of 15,876 welfare assessment criteria, 14,067 (89%) were scored as compliant, but only 83 inspection reports (26%) recorded that the zoo had met all welfare standards. Zoos were commonly found to fall into one of three classes, which predicted their probability of participating in each of the five conservation measures within the ZLA. Farm parks were identified as the collection type performing least well across inspection categories. With British zoos being required to meet new standards from May 2027, we propose a number of changes which could aid inspection consistency and legislative enforcement and drive improvements. Full article

This study, leveraging near-infrared spectroscopy technology and integrating vegetation index analysis, aims to develop a hyperspectral imaging-based non-destructive inspection technique for swift monitoring of crop chlorophyll content by rapidly predicting leaf SPAD. To this end, a high-precision spectral prediction model was first established under laboratory conditions using ex situ lyophilized Leaf samples. This model provides a core algorithmic foundation for future non-destructive field applications. A systematic study was conducted to develop prediction models for leaf SPAD values of Korla fragrant pear at different growth stages (fruit-setting period, fruit swelling period and Maturity period). This involved comparing various spectral preprocessing algorithms (AirPLS, Savitzky–Golay, Multiplicative Scatter Correction, FD, etc.) and CARS Feature Selection methods for the screening of optimal spectral feature band. Subsequently, models were constructed using BP Neural Network and Support Vector Regression algorithms. The results showed that leaf samples at different growth stages exhibited significant differences in their spectral features within the 5000–7000 cm⁻¹ (effective features for predicting chlorophyll (SPAD)) and 7000–8000 cm⁻¹ (moisture absorption valley) bands. The Savitzky–Golay+FD (Savitzky–Golay smoothing combined with first-order derivative (FD)) preprocessing algorithm performed optimally in feature extraction. Growth period specificity models significantly outperformed whole growth period models, with the optimal models for the fruit-setting period and fruit swelling period being FD-CARS-BP (Coefficient of determination (R²) > 0.86), and the optimal model for the Maturity period being Savitzky–Golay-FD+Savitzky–Golay-CARS-BP (Coefficient_of_determination (R²) = 0.862). Furthermore, joint modeling of characteristic spectra and vegetation indices further improved prediction performance (Coefficient of determination (R²) > 0.85, Root Mean Square Error (RMSE) 2.5). This study presents a reliable method for non-destructive monitoring of chlorophyll content in Korla fragrant pears, offering significant value for nutrient management and stress early warning in precision agriculture. Full article

The aim of this research is to investigate the potential of Raman and FT-IR spectroscopy as well as mathematical linear and non-linear models as a tool for the discrimination of different seed varieties of paprika, tomato, and lettuce species. After visual inspection of spectra, pre-processing was applied in the following combinations: (1) smoothing + linear baseline correction + unit vector normalization; (2) smoothing + linear baseline correction + unit vector normalization + full multiplicative scatter correction; (3) smoothing + baseline correction + unit vector normalization + second-order derivative. Pre-processing was followed by Principal Component Analysis (PCA), and several classification methods were applied after that: the Support Vector Machines (SVM) algorithm, Partial Least Square Discriminant Analysis (PLS-DA), and Principal Component Analysis-Quadratic Discriminant Analysis (PCA-QDA). SVM showed the best classification power in both Raman (100.00, 99.37, and 92.71% for lettuce, paprika, and tomato varieties, respectively) and FT-IR spectroscopy (99.37, 92.50, and 97.50% for lettuce, paprika, and tomato varieties, respectively). Moreover, our novel approach of merging Raman and FT-IR spectra significantly contributed to the accuracy of some models, giving results of 100.00, 100.00, and 95.00% for lettuce, tomato, and paprika varieties, respectively. Our results indicate that Raman and FT-IR spectroscopy coupled with machine learning could be a promising tool for the rapid and rational evaluation and management of genetic resources in ex situ and in situ seed collections. Full article

Powder Bed Fusion Laser Melting (PBF-LM) additive manufacturing technology is expected to have a remarkable impact on the industrial setting, making possible the realization of a metallic component with very complex designs to enhance product performance. However, the industrial use of the PBF-LM system needs a capability monitoring system to ensure product quality. Among the various studies developed, the investigation of methodology for the actual machine capability determination has been faced and still represents an open point. There are multiple authors and institutes proposing different investigation methods, ranging from the realization of samples (ex situ analysis) to installing monitoring devices on the machine (in situ analysis). Compared to other approaches, sample realization allows for assessing how the machine works through specimen analysis, but it is sensitive to the sample design. In this article, we first present an analysis of a well-known test artifact from an Axiomatic Design perspective. Second, based on the customer needs analysis and adjustments with respect to the use of hypothetical additive production lines, a new test artifact with an uncoupled design matrix is introduced. The proposed design has been experimentally tested and characterized using artifact made of Inconel 718 superalloy to evaluate its performance and representativeness in machine capability assessment. The results show an accurate identification of beam offset and scaling factor considering all the building platform positions. In addition, the artifact is characterized by a reduced building time (more than 90% with respect to the reference NIST artifact) and a halved inspection time (from 16 h to 8 h). Full article

Economically motivated adulteration (EMA) and/or cross-contamination are the two major factors resulting in the substandard quality of premium edible oil like extra virgin olive oil (EVOO) produced in food and beverage (F&B) fast-moving consumer goods (FMCG) industries. Current quality assurance methods (e.g., spectroscopy and chromatography) in FMCG involve intrusive sample extraction and ex situ analysis in a laboratory using expensive bulky instrumentation, which is neither integrable inline nor scalable to match the production throughput. Such techniques do not meet the industrial requirements of in situ testing, non-intrusive analysis, and high throughput inspection (100% product verification) leading to food loss and package waste from unwanted batch rejects. Herein, a low-cost electrical approach based on capacitance is proposed to show the proof of concept for screening EVOO-filled containers non-invasively for adulteration without any sample extraction by capturing the differences in the dielectric properties of mixed oils. The sensor system displayed a fast response (100 ms) and low detection limits for different adulterants (olive oil (32.8%), canola oil (19.4%), soy oil (10.3%) and castor oil (1.7%)), which is suitable for high-throughput (>60 sample/min) screening. Furthermore, a low-cost automated system prototype was realized to showcase the possibility of translating the proof of concept for possible scaling up and inline integration. Full article

Additive manufacturing technologies based on metal are evolving into an essential advanced manufacturing tool for constructing prototypes and parts that can lead to complex structures, dissimilar metal-based structures that cannot be constructed using conventional metallurgical techniques. Unlike traditional manufacturing processes, the metal AM processes are unreliable due to variable process parameters and a lack of conventionally acceptable evaluation methods. A thorough understanding of various diagnostic techniques is essential to improve the quality of additively manufactured products and provide reliable feedback on the manufacturing processes for improving the quality of the products. This review summarizes and discusses various ex-situ inspections and in-situ monitoring methods, including electron-based methods, thermal methods, acoustic methods, laser breakdown, and mechanical methods, for metal additive manufacturing. Full article

Polyamide 6 (PA6) is a commonly used material in many different sectors of modern industry. Herein, PA6 samples were coated with amorphous carbon layers (a-C:H) with increasing thickness up to 2 µm using radio frequency plasma enhanced chemical vapor deposition for surface adjustment. The morphology of the carbon coatings was inspected by ex situ atomic force microscopy and scanning electron microscopy. Surface wettability was checked by contact angle measurements. The chemical composition was analyzed using the surface sensitive synchrotron X-ray-based techniques near-edge X-ray absorption fine structure and X-ray photoelectron spectroscopy, supported by diffuse reflectance infrared Fourier transform spectroscopy. Particular attention was paid to the coating interval from 0 to 100 nm, to specify the interlayer thickness between the PA6 polymer and a-C:H coating, and the region between 1000 and 2000 nm, where dehydrogenation of the a-C:H layer occurs. The interlayer is decisive for the linkage of the deposited carbon layer on the polymer: the more pronounced it is, the better the adhesion. The thickness of the interlayer could be narrowed down to 40 nm in all used methods, and the dehydrogenation process takes place at a layer thickness of 1500 nm. Full article

Osteochondral injuries are increasingly prevalent, yet success in articular cartilage regeneration remains elusive, necessitating the development of new surgical interventions and novel medical devices. As part of device development, animal models are an important milestone in illustrating functionality of novel implants. Inspection of the tissue-biomaterial system is vital to understand and predict load-sharing capacity, fixation mechanics and micromotion, none of which are directly captured by traditional post-mortem techniques. This study aims to characterize the localised mechanics of an ex vivo ovine osteochondral tissue–biomaterial system extracted following six weeks in vivo testing, utilising laboratory micro-computed tomography, in situ loading and digital volume correlation. Herein, the full-field displacement and strain distributions were visualised across the interface of the system components, including newly formed tissue. The results from this exploratory study suggest that implant micromotion in respect to the surrounding tissue could be visualised in 3D across multiple loading steps. The methodology provides a non-destructive means to assess device performance holistically, informing device design to improve osteochondral regeneration strategies. Full article

The conservation of heritage structures is pivotal not only due to their cultural or historical importance for nations, but also for understanding their construction techniques as a lesson that can be applied to contemporary structures. Timber is considered to be the oldest organic construction material and is more vulnerable to environmental threats than nonorganic materials such as masonry bricks. In order to assess the structural vulnerability of heritage timber structures subjected to different types of risk, knowledge about their structural systems and configurations, the nature and properties of the materials, and the behavior of the structure when subjected to different risks, is essential for analysts. In order to facilitate the procedure, different assessment methods have been divided into the categories in situ and ex situ, which are applicable for vulnerability assessments at the element and full-scale level of a case study. An existing methodology for structural vulnerability assessments and conservation of heritage timber buildings is reviewed and a new methodology is proposed. Full article

Oil and gas well cements are multimineral materials that hydrate under high pressure and temperature. Their overall reactivity at early ages is studied by a number of techniques including through the use of the consistometer. However, for a proper understanding of the performance of these cements in the field, the reactivity of every component, in real-world conditions, must be analysed. To date, in situ high energy synchrotron powder diffraction studies of hydrating oil well cement pastes have been carried out, but the quality of the data was not appropriated for Rietveld quantitative phase analyses. Therefore, the phase reactivities were followed by the inspection of the evolution of non-overlapped diffraction peaks. Very recently, we have developed a new cell specially designed to rotate under high pressure and temperature. Here, this spinning capillary cell is used for in situ studies of the hydration of a commercial oil well cement paste at 150 bars and 150 °C. The powder diffraction data were analysed by the Rietveld method to quantitatively determine the reactivities of each component phase. The reaction degree of alite was 90% after 7 h, and that of belite was 42% at 14 h. These analyses are accurate, as the in situ measured crystalline portlandite content at the end of the experiment, 12.9 wt%, compares relatively well with the value determined ex situ by thermal analysis, i.e., 14.0 wt%. The crystalline calcium silicates forming at 150 bars and 150 °C are also discussed. Full article

Friction stir welding is a method of materials processing that enables the joining of similar and dissimilar materials. The process, as originally designed by The Welding Institute (TWI), provides a unique approach to manufacturing—where materials can be joined in many designs and still retain mechanical properties that are similar to, or greater than, other forms of welding. This process is not free of defects that can alter, limit, and occasionally render the resulting weld unusable. Most common amongst these defects are kissing bonds, wormholes and cracks that are often hidden from visual inspection. To identify these defects, various nondestructive testing methods are being used. This paper presents background to the process of friction stir welding and identifies major process parameters that affect the weld properties, the origin, and types of defects that can occur, and potential nondestructive methods for ex-situ detection and in-situ identification of these potential defects, which can then allow for corrective action to be taken. Full article