Search Results (87)

In the current data-driven era, effective data sharing is set to unlock billions in value for aerospace and complex manufacturing and their supply chains by enhancing product quality, boosting manufacturing and operational efficiency, and generating new value streams. However, current practices are hindered by fragmented data ecosystems, isolated silos, and reliance on paper-based documentation. Although the Digital Thread (DTh) initiative holds promise, its implementation remains impractical due to interoperability challenges, security and intellectual property risks, and the inherent difficulty of capturing and managing the overwhelming volume of data in such complex products as a holistic thread. This paper introduces the Manufacturing Digital Passport (MDP), a novel industry-driven concept that employs a product-centric, system-independent digital carrier to facilitate targeted, structured sharing of technical product data across the supply chain. The conceptual contribution of this work is the analytical formalisation of the MDP as a value-oriented carrier that shifts DTh thinking from costly, system-wide interoperability toward an incremental, ROI-driven record of lifecycle data. Rooted in real-world challenges and built on foundational principles of modularity, value creation, and model-based structures, the MDP, by design, enhances traceability, security, and trust through a bottom-up, incremental, use case-driven approach. The paper outlines its benefits through core design principles, definition, practical features, and integration strategies with legacy systems, laying the groundwork for a structured adoption roadmap in high-value manufacturing ecosystems. Full article

The development of Industry 4.0 has accelerated the adoption of sophisticated technologies, including Digital Twins (DTs), Artificial Intelligence (AI), and cybersecurity, within Additive Manufacturing (AM). Enabling real-time monitoring, process optimization, predictive maintenance, and secure data management can redefine conventional manufacturing paradigms. Although their individual importance is increasing, a consistent understanding of how these technologies interact and collectively improve AM procedures is lacking. Focusing on the integration of digital twins (DTs), modular AI, and cybersecurity in AM, this review presents a comprehensive analysis of over 137 research publications from Scopus, Web of Science, Google Scholar, and ResearchGate. The publications are categorized into three thematic groups, followed by an analysis of key findings. Finally, the study identifies research gaps and proposes detailed recommendations along with a framework for future research. The study reveals that traditional AM processes have undergone significant transformations driven by digital threads, digital threads (DTs), and AI. However, this digitalization introduces vulnerabilities, leaving AM systems prone to cyber-physical attacks. Emerging advancements in AI, Machine Learning (ML), and Blockchain present promising solutions to mitigate these challenges. This paper is among the first to comprehensively summarize and evaluate the advancements in AM, emphasizing the integration of DTs, Modular AI, and cybersecurity strategies. Full article

A new, simple machine was developed to address a long-standing challenge in biomedical and mechanical engineering: how to enhance the primary stability and long-term integration of screws and implants in low-density or heterogeneous materials, such as bone or composite substrates. Traditional screws often rely solely on external threading for fixation, leading to limited cohesion, poor integration, or early loosening under cyclic loading. In response to this problem, we designed and built a novel device that leverages a unique mechanical principle to simultaneously perforate, collect, and compact the substrate material during insertion. This mechanism results in an internal material interlock, enhancing cohesion and stability. Drawing upon principles from physics, chemistry, engineering, and biology, we evaluated its biomechanical behavior in synthetic bone analogs. The maximum insertion (MIT) and removal torques (MRT) were measured on synthetic osteoporotic bones using a digital torquemeter, and the values were compared directly. Experimental results demonstrated that removal torque (mean of 21.2 Ncm) consistently exceeded insertion torque (mean of 20.2 Ncm), indicating effective material interlocking and cohesive stabilization. This paper reviews the relevant literature, presents new data, and discusses potential applications in civil infrastructure, aerospace, and energy systems where substrate cohesion is critical. The findings suggest that this new simple machine offers a transformative approach to improving fixation and integration across multiple domains. Full article

This paper presents the design and implementation of an autonomous robotic facility for textile sorting and recycling, leveraging advanced computer vision and machine learning technologies. The system enables real-time textile classification, localization, and sorting on a dynamically moving conveyor belt. A custom-designed pneumatic gripper is developed for versatile textile handling, optimizing autonomous picking and placing operations. Additionally, digital simulation techniques are utilized to refine robotic motion and enhance overall system reliability before real-world deployment. The multi-threaded architecture facilitates the concurrent and efficient execution of textile classification, robotic manipulation, and conveyor belt operations. Key contributions include (a) dynamic and real-time textile detection and localization, (b) the development and integration of a specialized robotic gripper, (c) real-time autonomous robotic picking from a moving conveyor, and (d) scalability in sorting operations for recycling automation across various industry scales. The system progressively incorporates enhancements, such as queuing management for continuous operation and multi-thread optimization. Advanced material detection techniques are also integrated to ensure compliance with the stringent performance requirements of industrial recycling applications. Full article

This paper presents a novel framework addressing the fundamental challenge of concurrent real-time audio acquisition and motor control in resource-constrained mobile robotics. The ESP32-based system integrates a digital MEMS microphone with rover mobility through a unified Bluetooth protocol. Key innovations include (1) a dual-thread architecture enabling non-blocking concurrent operation, (2) an adaptive eight-bit compression algorithm optimizing bandwidth while preserving audio quality, and (3) a mathematical model for real-time resource allocation. A comprehensive empirical evaluation demonstrates consistent control latency below 150 ms with 90–95% audio packet delivery rates across varied environments. The framework enables mobile acoustic sensing applications while maintaining responsive motor control, validated through comprehensive testing in 40–85 dB acoustic environments at distances up to 10 m. A performance analysis demonstrates the feasibility of high-fidelity mobile acoustic sensing on embedded platforms, opening new possibilities for environmental monitoring, surveillance, and autonomous acoustic exploration systems. Full article

Threaded connections are fundamental in engineering structures, yet their elastic–plastic behavior under load remains challenging to model analytically. The yield limit can be reached under relatively small external loads, and elastic–plastic behavior has predominantly been studied using finite element models. While these models are highly valuable, they are often restricted to specific cases. This paper presents a novel extension of Maduschka’s classical method, offering a fast and efficient analytical approach to evaluate the behavior of screw–nut–washer assemblies. The method tracks plastic strain progression from initial yielding to full yield conditions and is validated against high-fidelity axisymmetric and 3D finite element analyses (FEAs) across a range of thread dimensions (M16–M36). Results demonstrate strong agreement with FEA benchmarks while achieving significant computational speedups, making the method suitable for iterative and large-scale analyses. In addition, the comparison with results available in the literature further supports the reliability of the proposed method. Its robustness to variations in geometry, friction, and thread count positions it as a foundation for reduced-order models, ready for integration into complex finite element frameworks commonly used in structural health monitoring and digital twin technologies. Full article

Geometric Dimensioning and Tolerancing (GD&T) are among the basic concepts of functional fitness and quality assurance in modern manufacturing. The historical development of GD&T took place primarily in the ambit of subtractive manufacturing; the advent of Additive Manufacturing (AM) now presents novel challenges due to the complexity of geometries, material variability, and process-induced variances. The present Perspective Paper briefly hints at key challenges for the future of GD&T in AM, with an eye to the necessary adaptation of tolerancing principles to AM-specific geometries, integration of Model-Based Definition (MBD) in digital threads, and development of new standards for surface texture and tolerance stack-up. New inspection techniques are also highlighted for the AM parts, which would become more prominent. This study underscores the need for continued research and collaboration to develop comprehensive GD&T frameworks tailored to AM, ensuring its industrial scalability and interoperability with traditional manufacturing systems. Full article

In an era marked by digital transformation and political polarization, the European Union faces significant challenges in maintaining effective communication and public trust. This study examines the European Parliament’s use of Threads and X (formerly Twitter) during the 2024 European Parliament elections, analyzing the types of content published, multimedia resources employed, and engagement generated on both platforms. Using a quantitative content analysis of 171 posts from the official English-language accounts, this research identifies key differences in communication strategies across platforms. Findings reveal that X prioritizes video content, mentions, and reposts, fostering higher user engagement, whereas Threads leans toward infographics and a more informative approach. The study highlights the fragmented nature of digital political communication and underscores the necessity for the European Parliament to adapt its strategies to the dynamics of each platform. These insights contribute to a broader understanding of institutional communication in an evolving digital ecosystem and its implications for electoral mobilization and public discourse. Full article

Material selection is essential for advancing sustainability in construction. Biocomposites contribute significantly to raising the awareness of materials derived from biomass. This paper explores the design development and application of novel natural fibre pultruded biocomposite profiles in a deployable system. Development methods include geometrical studies to create a system that transforms from flat to three-dimensional. Physical and digital models were used to refine the geometry, while connection elements were designed to suit material properties and deployability requirements. The first case study, at a furniture scale, demonstrates the use of the profiles connected using threading methods to create a lightweight multifunctional deployable system enabling easy transport and storage. This system can be locked at various heights for different purposes. The realised structure weighs 4 kg, supporting weights up to 150 kg. The second case study applies the system architecturally in an adaptive kinetic facade, adjusting to the sun’s position for optimal shading, providing up to 70% daylight when open and as little as 20% when closed. These two structures validate the developed deployable system, showcasing the versatility of biocomposite profiles in such configurations. This approach enhances sustainability in architecture by enabling lightweight, adaptable, and eco-friendly building solutions. Full article

Manufacturing-as-a-service consists of an Industry 4.0 innovation and has been a focal point of research and business attention in recent years. MaaS provides a matching connection between manufacturing resources or capabilities as service and organizations that need to utilize them, via a digital thread. Through this approach, not all companies have to invest in heavy capital, which allows them to focus on other critical capacities. This review provides bibliometric insights, such as quantitative results regarding the annual trends, dominant publications, journals, institutions, and VOS-viewer-software-extracted networks that highlight co-occurrence of keywords and journal co-citations. Moreover, the publications are presented concisely and are critically evaluated and categorized in pilar categories based on their core concept. The current limitations and future trends in the MaaS field are identified as well. Even though it is an established, important Industry 4.0 innovation, the available literature may not provide such a holistic, comprehensive overview of MaaS. The current study not only presents critical observations of contemporary and distinguished scientific papers but also guides researchers in a systematic examination of MaaS theoretically, architecturally represented, or regarding its data-driven opportunities. Full article

The Internet of Things is used in many application areas in our daily lives. Ensuring the security of valuable data transmitted over the Internet is a crucial challenge. Hash functions are used in cryptographic applications such as integrity, authentication and digital signatures. Existing lightweight hash functions leverage task parallelism but provide limited scalability. There is a need for lightweight algorithms that can efficiently utilize multi-core platforms or distributed computing environments with high degrees of parallelization. For this purpose, a data-parallel approach is applied to a lightweight hash function to achieve massively parallel software. A novel structure suitable for data-parallel architectures, inspired by basic tree construction, is designed. Furthermore, the proposed hash function is based on a lightweight block cipher and seamlessly integrated into the designed framework. The proposed hash function satisfies security requirements, exhibits high efficiency and achieves significant parallelism. Experimental results indicate that the proposed hash function performs comparably to the BLAKE implementation, with slightly slower execution for large message sizes but marginally better performance for smaller ones. Notably, it surpasses all other evaluated algorithms by at least 20%, maintaining a consistent 20% advantage over Grostl across all data sizes. Regarding parallelism, the proposed PLWHF achieves a speedup of approximately 40% when scaling from one to two threads and 55% when increasing to three threads. Raspberry Pi 4-based tests for IoT applications have also been conducted, demonstrating the hash function’s effectiveness in memory-constrained IoT environments. Statistical tests demonstrate a precision of ±0.004, validate the hypothesis in distribution tests and indicate a deviation of ±0.05 in collision tests, confirming the robustness of the proposed design. Full article

Background: Canines play a vital functional and aesthetic role in human dentition, yet impacted canines, particularly in the mandible, are rare and can lead to functional disorders, such as the absence of canine guidance, while negatively affecting a patient’s self-esteem. Transmigration of mandibular canines adds complexity to treatment. One method to reduce the treatment time, especially for impacted teeth, is corticotomy-assisted orthodontic therapy (CAOT). Methods: A 13-year-old patient presented with a horizontally impacted lower right canine, positioned below the roots of the lower incisors, showing transmigration. A digitally designed and 3D-printed lingual appliance was attached to the lower molars with hooks on the lingual side, enabling the application of multi-directional orthodontic forces. CAOT was performed using an Er:YAG laser (LightWalker, Fotona, Ljubljana, Slovenia) at 200 mJ, 12 Hz, 2.4 W, with a pulse duration of 100 µs, and an MSP H14 conical tip (0.6 mm spot diameter). Photobiomodulation (PBM) with a 635 nm diode laser (Lasotronix, Smart ProM, Piaseczno, Poland) was applied at 10 J per point (20 J/cm²) for 100 s per point, with a total energy of 20 J per session to reduce the risk of root resorption, manage pain, and accelerate healing as the tooth was moved into the alveolar ridge. Results: The treatment duration was two and a half years. The lingual appliance with hooks allowed precise traction of the canine, aided by exposure from the lingual side and the attachment of a hook. Gentle forces applied via orthodontic thread gradually moved the canine beneath the oral mucosa. Mid-treatment cone beam computed tomography (CBCT) scans confirmed the absence of root resorption of the lower incisors. A corticotomy, enhanced by laser therapy, was performed before moving the canine into the alveolar ridge. The canine was successfully rotated 180° and positioned without any signs of resorption in the canine or adjacent teeth. Conclusions: The use of a digitally designed and 3D-printed lingual appliance with hooks allowed the precise control of the traction of impacted teeth. When combined with corticotomy and laser therapy, it minimised root resorption risks, reduced pain, accelerated healing, and improved the overall success of the orthodontic treatment. Full article

Tiny machine learning (TinyML) demands the development of edge solutions that are both low-latency and power-efficient. To achieve these on System-on-Chip (SoC) FPGAs, co-design methodologies, such as hls4ml, have emerged aiming to speed up the design process. In this context, fast estimation of FPGA’s utilized resources is needed to rapidly assess the feasibility of a design. In this paper, we propose a resource estimator for fully customized (bespoke) multilayer perceptrons (MLPs) designed through the hls4ml workflow. Through the analysis of bespoke MLPs synthesized using Xilinx High-Level Synthesis (HLS) tools, we developed resource estimation models for the dense layers’ arithmetic modules and registers. These models consider the unique characteristics inherent to the bespoke nature of the MLPs. Our estimator was evaluated on six different architectures for synthetic and real benchmarks, which were designed using Xilinx Vitis HLS 2022.1 targeting the ZYNQ-7000 FPGAs. Our experimental analysis demonstrates that our estimator can accurately predict the required resources in terms of the utilized Look-Up Tables (LUTs), Flip-Flops (FFs), and Digital Signal Processing (DSP) units in less than 147 ms of single-threaded execution. Full article

In this modern era, the constant increase in computational and sensing power has lead to the development of multiple data-driven concepts. Amongst these, the ‘digital thread’ is an architecture that aims at optimising the knowledge of a system by merging prior knowledge of the product with information from multiple stages of its lifecycle in order to improve the performance of new products to be designed, thanks to the increased accuracy of this updated knowledge. Even though the use of these data-driven architectures is becoming increasingly widespread, most of the corresponding developments remain currently limited to the component level. In this respect, this article extends the application of the digital thread from the component level to the structural assembly level and enriches it with additional multi-physics considerations and non-linear failure constraints. To this end, it details the development of a digital thread dedicated to an aircraft vertical tail plane structure made of carbon fibre reinforced polymer, as well as the tools and models required to implement such an approach using Bayesian inference, multi-physics simulations, and empirical models. Full article

This study aimed to evaluate the retrievability and potential damage to implant–abutment connections caused by fractured abutment screw removal using conventional and drilling techniques. A total of forty abutment screws were randomly inserted into forty dental implants, and then they were fractured and extracted using different removal methods: Group A employed a conventional approach utilizing an exploration probe and an ultrasonic device without irrigation (n = 10) (conventional); Group B used the Phibo drilling removal system without irrigation (n = 10) (Phibo); Group C utilized the Rhein83^® drilling removal system without irrigation (n = 10) (Rhein83); and Group D implemented the Sanhigia^® drilling removal system without irrigation (n = 10) (Sanhigia). Pre- and postoperative micro-computed tomography (micro-CT) scans were performed on the dental implants, and Standard Tessellation Language (STL) digital files were generated for morphometric analysis to measure the wear volume. ANOVA was used to assess the volumetric differences (mm³) and percentage ratios of the internal thread volumes of the implant–abutment connections before and after the procedures. Results: This study found no statistically significant differences in the volumetric and percentage ratios of internal threads among the implant groups (Phibo, Rhein83, Sanhigia, and conventional). However, the success rate for retrieving fractured abutment screws was higher (90%) with the drilling systems compared to the conventional technique (50%). These results suggest that drilling systems are more effective for the retrieval of damaged screws. Although drilling techniques without irrigation demonstrated higher removal efficiency compared to the conventional method, both approaches resulted in similar wear volumes at the implant–abutment connections when used to extract fractured screws. Full article