Abstract: This study investigates hybrid brake-pad composites made by adding different percentages of silicon carbide (15% and 20% SiC) and zinc oxide (10%, 15%, and 20% ZnO). The goal was to find a composite that improves brake working efficiency. Wear and hardness tests were carried out according to ASTM standards. The experimental results were analyzed using Design of Experiments method to study how wear changes over time under different loads. Time-series trend analysis visualizes how the specific wear rate developed. The results showed that sample A5 had the best wear resistance and certified A5 as the optimum structural stability over time composite sample. The hardest samples were A2 and A5. The best composite was selected for a static structural analysis using ANSYS 2022-R1 to evaluate stress, strain, deformation, and elastic energy. The thermal analysis examined heat distribution, heat generation, and heat flux in the hybrid composite material. The numerical results showed that stress levels are lower at outer surfaces compared to the inner regions. The outer surfaces exhibit a uniform distribution heat flux. Directional heat flux showed a slight increase near the inner radius, the disk protrusions and edges. These findings clarified how the optimal composite behaves under braking conditions.

Abstract: Wire Arc Additive Manufacturing (WAAM) is a cost-effective and scalable technique for producing large metallic components; however, coarse columnar microstructures, strong crystallographic texture, and significant residual stresses limit its widespread adoption. In recent years, hybrid WAAM processes integrating deformation-based techniques have been developed to address these limitations. This review provides a comprehensive analysis of deformation-assisted WAAM, encompassing interlayer rolling, friction stir processing (FSP), hammer peening, laser shock peening, and ultrasonic vibration-assisted approaches. These hybrid techniques introduce additional thermomechanical parameters—strain, strain rate, and applied stress—that significantly influence microstructure evolution. The governing physical metallurgy mechanisms are discussed in detail, including dislocation accumulation, recovery, static and dynamic recrystallization, and severe plastic deformation. Studies from 2022 to 2025 are critically reviewed, highlighting the effectiveness of hybrid WAAM in promoting columnar-to-equiaxed grain transformation, reducing anisotropy, mitigating defects, and improving mechanical properties across aluminum, titanium, steels, and nickel-based alloys. The integration of auxiliary processes such as in-situ machining and heat treatment is also discussed. This review establishes a process-structure-property framework for hybrid WAAM and provides guidance for the development of advanced additive manufacturing systems capable of delivering near-net-shape components with microstructures and properties approaching those of wrought or forged counterparts.

Abstract: Smart textiles represent a transformative convergence of materials science, electronics, and sustainability principles, enabling the creation of fabrics that sense, respond, and adapt to environmental stimuli. This systematic review examines recent advancements in sustainable smart textile technologies, synthesizing findings from peer-reviewed literature published between 2018 and 2024. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, 78 studies were identified and reviewed. Key innovations include biodegradable electronic fibers, solar-energy-harvesting textiles, thermoelectric wearable systems, self-healing polymer matrices, and bio-based conductive inks. The review highlights a paradigm shift toward circular economy models in textile manufacturing, driven by regulatory pressure and consumer demand for eco-conscious products. Findings reveal that sustainable smart textiles can achieve performance parity with conventional counterparts while reducing environmental impact by up to 60%. The article concludes with targeted recommendations for researchers, industry practitioners, and policymakers seeking to accelerate the responsible commercialization of sustainable smart textile technologies.

Abstract: Petroleum products have led to challenges for the environment on a global scale, such as pollution from microplastics, persistent waste in the environment that is not biodegradable, and an increasing carbon footprint due to plastic production. In quest of sustainable alternatives, environmentally friendly cellulose nanofiber (CNF) based nanopapers have gained potential as biodegradable materials to replace most of plastic derived industrial products. (Cellulose nanofibers derived from renewable biomass that possesses small dimensions which exhibit excellent mechanical properties, low density, high optical transparency, and very good barrier properties against gases and oils.) Advantages like these render CNF nanopapers promising for flexible electronics, packaging materials, coatings, filtration membranes and sustainable structural materials. This study aims to provide a conceptual framework for the development of cellulose nanofiber (CNF) based nanopapers as substitutes for common plastic materials by investigating their performance in industrial applications. This framework combines biomass extraction techniques, nanofibrillation processing, fabrication of nanopaper and material performance validation. Numerical simulations reveal that CNF nanopapers offer mechanical and barrier performance approaching synthetic polymer films with substantial decrease in environmental footprint. The findings demonstrate that cellulose based nanopapers provide a scalable route for sustainable material innovation in industry. Bio nanomaterials hold great promise for contributing to the sustainability of circular manufacturing systems as they can address environmental sustainability challenges linked with plastic based materials.

Abstract: Repeated machining passes (i.e., continuous toolpaths) are common in CNC manufacturing, including multi-level machining of prismatic parts and iso-contour passes in contour machining. They present an opportunity to exploit pass-to-pass learning to improve productivity without sacrificing quality through feedrate optimization. Traditional iterative learning methods provide a means to exploit pass-to-pass learning for quality improvements, but they are not well-suited to feedrate optimization because the reference trajectories change as the feedrate increases. In the authors’ prior work, learning-based feedrate optimization was demonstrated for repeated machining along identical toolpaths. This paper extends that concept to the more challenging case of similar but non-identical cutting paths, as encountered in contour machining. A pass-to-pass learning strategy is proposed in which corresponding sections of non-identical iso-contour passes are identified using a contour-matching method based on geometric similarity. Bayesian linear regression models are then used to learn and predict contour error and spindle torque across passes, with uncertainty explicitly quantified through credible intervals. These predictions are embedded in a window-based feedrate optimization framework solved via sequential linear programming, enabling feedrate maximization subject to kinematic, contour-error, and spindle-torque constraints. The proposed approach is experimentally validated on a 3-axis desktop CNC milling machine through multiple 2.5D contour machining case studies. Results show that the method can rapidly approach near-optimal feedrates after only a few passes, culminating in up to 16.4% increase in productivity compared to an equivalent learning-based feedrate optimization approach for identical toolpaths.

Abstract: Laser hard overlaying is an advanced, perspective technology with wide industrial applications, for example dies. The aim is to improve surface properties like wear resistance using special layers of powder sintered or remelted by laser beam. At present, dies are manufactured by machining with following bulk heat treatment, which is an expensive process particularly due to use of expensive high-alloyed tool steels. Repairs performed using arc or plasma welding introduce a big amount of heat to the part, which can cause dimension changes and material degradation, These methods often fail also due to low weldability of the materials. An advantage of laser overlaying is minimization of these difficulties. The paper contains a comprehensive evaluation of several types of hard overlayed powder of H13 tool steel on a S355 structural steel and on H11 tool steel using laser beam. Macro- and microstructure, hardness and fatigue resistance are evaluated including fatige damage mechanisms. Results are completed with basic measurement of residual stresses using destructive strain-gauge methods. Fatigue resistance is sensitive on surface and subsurface defects, which can significantly reduce endurance limit.

Abstract: In this study, we investigated the change of carbon dioxide (CO₂) gas in PET bottles over time using experimental and numerical methods, as this is an important quality criterion in carbonated beverage production. Gas loss was modeled using the finite element method (FEM) on 2.5-liter PET bottles, and the effects of temperature, internal pressure, and packaging wall thickness were theoretically evaluated within the framework of the ideal gas equation and Fick's law. Validation was achieved by comparing model results with experimental data, and ideal production conditions were determined. Analyses revealed that gas loss was concentrated primarily in the top and shoulder regions of the bottle, and increasing the thickness in this region reduced diffusion. Furthermore, lowering the filling temperature and increasing internal pressure significantly reduced the transfer of dissolved CO₂ from the packaging to the external environment. Modeling studies were conducted using a three-dimensional design of the bottle geometry, defining boundary conditions to investigate the effects of different material distributions and thicknesses. Based on the findings, production processes were reorganized, and standardized recipes were created. As a result, the combination of experimental and numerical data has shown that gas losses have been largely controlled, and quality standards can be maintained for longer periods. This study can provide guidance not only for 2.5-liter PET bottles but also for other packaging types. Thus, it was concluded that more planned, higher standard production can be achieved in the carbonated beverage industry, consumer complaints can be reduced, and product performance can be maintained sustainably.

Abstract: (1) Background: Hugging Face is one of the largest platforms for machine-learning datasets, hosting collections of all kinds beyond its core focus on natural language processing. Whether and how these datasets can be leveraged for agricultural informatics is an open question. (2) Methods: A systematic data-space analysis structured by the PRISMA 2020 methodology was conducted. Using the search terms “farming” and “agriculture”, 128 datasets were identified on the platform, of which 126 could be fully analysed. (3) Results: Datasets cover mostly crops (42 %). English dominates (71 %); 13 languages are represented in total. The distribution of dataset sizes is strongly right-skewed (mean 156,346 entries; median 1,000). Parquet is the most common format (43 %); 92 % of datasets appear to contain human–LLM dialogues. (4) Conclusions: The available agricultural datasets on Hugging Face are thematically and qualitatively heterogeneous. Future work should develop prototypes to test if the available datasets are usable as data base for crop-related applications, and to identify potential gaps in the data space.

Abstract: In the underground operation scenario, with the development of intelligence, the underground electronic monitoring and control systems have gradually become an important tool for mining practitioners to obtain operational information. In view of the existence of low-light environment under the mine and the obvious difference from the above-ground natural light, screen-related factors have a significant impact on the visual search task, so it is critical to study the impact of interface layout in the low-light environment under the mine on the visual search efficiency. In this study, the application scenario of the electronic monitoring and control system under the mine is simulated. The information layout of the current electronic monitoring and control system in the mine and different lighting environments are set as experimental variables. The effects of interface layout design features on user search performance, visual behavior and usability satisfaction are discussed. The experimental results show that interface layout and illumination change have significant main effects on task completion time, fixation times, saccade ratio and subjective usability score. Among them, Three-Column layout mode has outstanding performance in the aspects of task completion time, fixation number and subjective usability score, and the search efficiency is higher in 50lx illumination environment.

Abstract: With Industry 4.0, modern manufacturing systems have undergone significant changes, allowing the collection of data in real time, automation through intelligent systems, and interconnection of production environments. At the same time, one of the most popular approaches to continuous improvement is LSS (LSS), which focuses on the eradication of waste, the efficiency of the process, and the enhancement of quality. The combination of LSS and Industry 4.0 is a developing area of research, even though combining these two paradigms is complementary. This article includes a systematic literature review in which the combination of LSS practices and Industry 4.0 technologies, including the Internet of Things (IoT), artificial intelligence (AI), cyber-physical systems (CPS), and big-data analytics, is discussed. The literature review is based on recent publications published between 2018-2025 and relies on significant academic databases such as Scopus and the Web of Science. The results show that Industry 4.0 technologies significantly improve traditional LSS instruments such as value stream mapping, root cause analysis, and statistical process control owing to their ability to monitor reality in real time, predictive maintenance, and decision-making based on statistics. Nevertheless, integration has also brought up several challenges, including the resistance of the organization to digital transformation, the high cost of its implementation, the skill gap among employees, and cybersecurity issues. Through an overall summary of the available literature and industry case studies and analyses, this study suggests a template for integrating LSS approaches into Industry 4.0. The proposed framework provides viable recommendations for organizations planning to shift to intelligent, data-driven, and sustainable manufacturing systems.

Abstract: Organizations are increasingly investing in Process Innovation with Analytics, i.e., the usage of analytics to innovate operational processes. Process innovation with analytics is a challenging and complex endeavor encompassing 1) redesign of processes, 2) development of digital infrastructure, and 3) analytics development. As a result, organizations need guidance on how to approach this complex challenge. While research on IT-enabled process innovation and analytics each offer valuable insights, process innovation with analytics necessitates contextualization of these knowledge bases due to its distinct characteristics. This paper aims to inspire further research into process innovation with analytics by 1) reconceptualizing analytics in the context of process innovation, and 2) proposing a research agenda, consisting of three research directions and five research challenges. The reconceptualization and research agenda are based on the authors’ experience from an Action Design Research study at a large global manufacturer and retailer focused on process innovation with analytics. Bridging analytics, process innovation, and infrastructure perspectives, the paper offers a foundation for future scholarly endeavors and calls for further research into 1) digital infrastructures for process innovation with analytics, 2) the relationship between process change and analytics development, and 3) governance of process innovation with analytics.

Abstract: The paper presents the cutting tool-life of uncoated and DLC-coated inserts used for machining of aluminum-lithium components used in the structure of Airbus A350 aircraft. Based on the collected data, a feed-forward artificial neural network with two hidden layers was created, trained using the Bayesian Regularization (trainbr) algorithm in MATLAB. The obtained results indicate a high performance of the model, with a low mean square error (MSE) and a correlation coefficient R > 0.98, which reflects an excellent generalization capacity and a close correlation between the actual and estimated values. The regression plot and error analysis confirmed the accuracy of the predictions made by the network. The internal parameters of the algorithm, such as the gradient and μ, provided additional information regarding the optimization process.

Abstract: (1) Background: Privacy usability in IoT smart home companion applications remains an underexplored domain despite mounting regulatory requirements and accelerating user adoption. Heuristic evaluation offers a scalable pathway to privacy usability assessment, yet validated frameworks for applying such methods in real industrial settings are scarce. This study presents the first empirical application of the ABCDE Privacy Framework, a ten-heuristic instrument grounded in Nielsen’s usability principles and Privacy by Design, to an IoT companion application developed with a major European home appliance manufacturer. (2) Methods: A structured workshop was conducted with a multidisciplinary team of seven participants (five industry professionals and two researchers) following a two-round protocol: a qualitative heuristic discussion phase (Round 1) and an individual scoring phase (Round 2). Data were analysed through MAXQDA. (3) Results: Average heuristic scores ranged from 3.6 (H9: error recovery) to 4.8 (H6: recognition; H10: documentation), with an overall mean of 4.32. Six second-order themes were identified, including Transparency Asymmetry, Centralised but Decontextualised Privacy, and Shared Household Complexity. (4) Conclusions: The ABCDE Privacy Framework is feasible, time-efficient, and analytically productive in real industrial contexts, generating design-relevant insights and enabling cross-role team alignment within a two-hour session. These findings support its potential as a scalable tool for Privacy by Design practice in IoT product development.

Abstract: Rising energy costs and strict CO₂ traceability regulations create demand for monitoring energy and CO₂ emissions in manufacturing. This paper presents a framework for modelling component-wise energy models with deployable accuracy. In many factories, power meters log data at a sampling rate of 1–2 Hz, so short start-up peaks of components are underestimated. Manufacturers want to exploit this information to support operational decisions, such as peak shaving and optimising energy contract costs. To enable data-driven decisions with limited measurement infrastructure, energy models must extrapolate component behavior from sparse data. The framework is based on power measurements in accordance with ISO 14955-3, ensuring that the load characteristics required for subsequent modelling are known. The measurements are then segmented, and regressions are fitted for each segment. As a case study considering the mist extractors of two different machine tools, the proposed segmentation achieved determination coefficients (R²) of up to 0.94 in the complex ramp-up phase. The resulting models are compact, interpretable, and suited for energy monitoring on edge devices. The contribution is a reproducible framework for delivering peak-aware, component-level energy models from low-frequency industrial power meter data.

Abstract: Hydrodynamic cavitation is attracting increasing interest in food processing as a non-thermal approach for preserving product quality and supporting the recovery of valuable bioactive compounds. Conventional Venturi devices are usually designed for fixed operating conditions, whereas real process streams may vary in temperature, viscosity, and gas or solid content. This can make it difficult to maintain stable and effective operating conditions when a fixed geometry is used. In this work, an adjustable circular Venturi is presented as a simple conceptual device for hydrodynamic cavitation in food applications. The external body and pipeline connections remain unchanged, while the throat section can be adjusted to adapt the device to different process requirements. In this sense, the proposed concept may also serve as an adjustable platform for exploring different operating conditions and identifying suitable throat configurations for specific food matrices and process targets. Once identified, such conditions may support the definition of a dedicated final Venturi configuration for the intended application. The proposed concept may be of interest for applications such as green extraction, food by-product valorization, and mild processing strategies aimed at preserving or enhancing bioactive compounds. This study is presented as a conceptual design contribution for food applications.

Abstract: Digital Twin (DT) systems combining physics-based simulation with hardware execution are critical for Industry 4.0 manufacturing, yet proprietary software solutions remain expensive and platform-dependent. This work addresses three technical challenges: maintaining geometric and kinematic fidelity across CAD-to-simulation conversion pipelines, synchronizing dual physics engines (Unity and ROS middleware) under hardware latency constraints, and optimizing motion planning while preserving trajectory quality and interactive responsiveness. We developed an integrated framework for a 7‑Degree of Freedom manipulator using CAD modeling, URDF/SRDF semantic representation, and bidirectional Unity-ROS (Robot Operating System) communication via WebSocket connectors. Motion planning uses RRTConnect from OMPL with collision-aware optimization through the Flexible Collision Library. Validation across 12 manipulation trials demonstrated positional synchronization accuracy of ±2.0 degrees, motion planning performance of 0.064 ± 0.020 seconds. Latency analysis reveals that hardware execution to be the dominant system bottleneck, significantly exceeding network communication delays. The system achieves performance metrics comparable to proprietary industrial solutions. This work establishes a replicable, cost-effective Industry 4.0 framework, demonstrating that modern game engine technology combined with open-source robotics middleware can deliver DT systems matching proprietary solutions. The architecture and validated implementation enable adaptation to alternative robotic platforms and support broader adoption of simulation-validated automation in manufacturing contexts.

Abstract: Engineering design increasingly uses generative AI to explore large form spaces, yet concept-driven generation is only useful if observers consistently perceive the intended attribute. We propose a ranking-based human validation layer that tests whether AI-generated concept-intensity gradients are interpretable, reliable, and usable. For each Product–Concept pair, a controlled generative workflow produced six variants intended to increase concept expression (A–F). In an online study, 26 design engineers ranked the variants by perceived intensity, with an optional not-applicable (NA) flag when category recognition failed. We analyse rankings with heatmap diagnostics, inter-observer agreement, monotonic alignment with the intended order, and Plackett–Luce aggregation with uncertainty, while using NA trends to bound operational ranges. Across nine pairs, most gradients aligned with the intended direction, but performance depended on the concept and product context, revealing both stable and failure-prone segments. The approach provides an evidence-based gate for concept implementation in AI-generative design.

Abstract: Digital twins are becoming an important tool in biomedical systems. They help with real time monitoring, prediction, and control. They work well only when they can combine many types of physiological data. They must also stay closely in sync with the real system.This paper describes a digital twin framework that uses a Unified Namespace. The UNS acts as a central data hub. It collects signals from sensors, organ level models, and patient information. It keeps all data in one clear and interoperable structure. It separates data producers from data users. This makes the system easier to scale. It also supports fast data flow and constant model updates.A multiscale computational model sits at the center of the twin. It joins physiological behavior with predictive methods. It supports real time decisions in a closed loop system. A sample biomedical case shows how the UNS improves system speed, prediction quality, and control actions. The results show that UNS based digital twins can support personalized medicine. They can also improve biomedical workflows and help build advanced cyber physical healthcare systems.

Abstract: Hybrid robotic manufacturing systems integrating additive and subtractive processes enable fabrication of complex, high-value components but are typically executed sequentially, resulting in long cycle times. Concurrent execution of Directed Energy Deposition (DED) and milling promises productivity gains but introduces coupled thermal, mechanical and spatial interactions that challenge conventional process planning. This work addresses the methodological problem of planning milling operations in the presence of an ongoing DED process. The concurrent planning task is formulated as a mixed-integer, nonlinear, multi-objective optimisation problem capturing sequencing and orientation decisions, cutting parameters and temporal coupling to the deposition trajectory. A hierarchical, surrogate-assisted optimisation framework is proposed, combining unified decision-variable encoding, deterministic decoding and staged feasibility enforcement to ensure robotic executability. Disturbance mechanisms such as thermal interaction, particulate interference and pose-dependent dynamic compatibility are incorporated as modular objective abstractions, enabling systematic trade-offs between machining productivity and preservation of deposition process integrity. The proposed framework is demonstrated on a large-scale hybrid manufacturing case study with sparsely distributed machining segments, illustrating interaction between spatial sequencing, temporal feasibility and disturbance-aware optimisation under stated assumptions. The framework is methodological and provides a transferable foundation for future development and validation of disturbance-aware planning strategies for concurrent additive-subtractive manufacturing.

Abstract: The paper deals with comparative analysis of machined surfaces by classic electrical discharge machining (EDM) and hybrid ultrasonic EDM of CoCr alloys, using computer vision aimed at emphasizing the advantages of this hybrid technology. The analysis revealed generally the superior stability of EDM+US process against classic EDM explained by better evacuation of debris from the working gap due to ultrasonically induced cavitation. This key phenomenon also contributed to the enhancement of machining rate by removing the material in liquid state and also the in solid state from the microgeometry peaks but also reducing the surface roughness if the power on the ultrasonic chain is optimzed.