Polymer-ceramic hybrid composites are emerging as attractive candidates for lightweight, corrosion-resistant absorber components in solar thermal collectors; however, their adoption is constrained by the intrinsically low thermal conductivity of polymers, processing-induced anisotropic heat transport, interfacial thermal resistance at tube/laminate joints, and durability challenges under outdoor exposure. This review provides a collector-centered synthesis of polymer-ceramic hybrid materials, emphasizing the translation of composite properties into collector-level outcomes rather than conductivity enhancement alone. A structure-property-performance mapping approach is presented to connect directional thermal conductivity ((k_in-plane), (k_perp)), thermal diffusivity, heat capacity, coefficient of thermal expansion, and service temperature with collector performance parameters such as heat removal effectiveness, overall heat losses, and stagnation behavior. Ceramic fillers (e.g., boron nitride, aluminum nitride, silicon carbide, alumina) are examined for stable conduction-network formation, coating compatibility, and long-term reliability, while carbon fillers (graphite, graphene nanoplatelets, carbon nanotubes) are evaluated for combined heat spreading and solar absorption benefits, with attention to emissivity penalties. Hybrid ceramic-carbon architectures and multilayer absorber designs are identified as the most promising routes to balance thermal transport, optical selectivity (high solar absorptance and low thermal emittance), manufacturability, and durability under UV, humidity, and thermal cycling.

Carbon/polymer composites are increasingly designed as microstructure-engineered multifunctional materials that combine mechanical reinforcement with electrical/thermal transport, electromagnetic interference (EMI) shielding, and sensing. Performance is governed less by filler fraction than by the coupled control of network topology, junction resistance, and interfacial thermal boundary resistance under processing-induced shear and thermal histories. Electrical response follows percolation combined with tunneling/contact-controlled junctions, producing nonlinear σ(φ) behavior and high piezoresistive sensitivity near the percolation threshold. In contrast, thermal transport is commonly limited by Kapitza resistance and filler-filler junction resistance, restricting exploitation of the intrinsic conductivity of CNTs and graphene. Recent advances emphasize hybrid and 3D carbon architectures that densify connectivity, reduce junction losses, and enable programmable anisotropy via scalable routes such as masterbatch extrusion and additive manufacturing. However, translation remains constrained by dispersion-driven variability, transport-toughness trade-offs, and incomplete durability assessment under cycling, humidity, and reprocessing. This review consolidates mechanistic structure-processing-property relationships and provides application-driven design rules for sensors, EMI shielding, and thermal management.

This study used directed energy deposition-arc (DED-Arc) with the cold metal transfer (CMT) process to produce a thin magnesium wall. The microstructure of DED-Arc AZ31 reveals equiaxed grains, with the grain sizes increasing from the bottom (20.53 μm) to the middle (42 μm) and top layers (36.18 μm) due to heat conduction variation. The mechanical properties of DED-Arc AZ31 are anisotropic and superior to those of squeeze-cast counterparts. X-ray computed tomography (XCT) shows a non-uniform porosity distribution at the start of the deposition due to the high heat sink and height variation. The DED-Arc process outperforms the high-pressure die-casting (HPDC) process in preventing shrinkage porosity. No shrinkage porosity is observed in the middle of deposition for DED-Arc, occurring only at the edge. K-means clustering identifies a sphericity threshold of 0.4 to distinguish shrinkage and gas porosity. Two classification methods of porosity, the bounding box (BB) and shape morphology, were evaluated. It is recommended to use the BB method in combination with the porosity diameter for porosity classification, ensuring a more comprehensive approach. The BB classification reveals a dominance of cube-like pores, while the formation of rod-like and blade-like pores is minimal. Defect orientation analysis shows that plate-like pores result in a higher stress concentration factor (SCF) of 6 along the build direction compared to 3 in the longitudinal direction. A novel method based on point clouds measures geometric parameters and achieves errors of 18.68% for the surface waviness and 13.7% for the effective wall area.

Operation and maintenance (O&M) events resulting from environmental factors (e.g., precipitation, temperature, seasonality, and unexpected weather conditions) are among the primary sources of operating costs and downtime in run-of-river small hydropower plants (SHPs). This paper presents a data-driven methodology for predicting such long events using machine learning models trained on historical power production, weather radar, and forecast data. Case studies on two Slovenian SHPs with different structural designs and levels of automation demonstrate how environmental features—such as day of year, rain duration, cumulative amount of rain, and rolling precipitation sums—can be used to forecast long events or shutdowns. The proposed approach integrates probabilistic classification outputs with threshold-consistency smoothing to reduce noise and stabilize predictions. Several algorithms were tested—including Logistic Regression, Support Vector Machine (SVM), Random Forest, Gradient Boosting, and k-Nearest Neighbors (k-NN)—across varying feature combinations for O&M model development, with cross-validation ensuring robust evaluation. The models achieved an F1-score of up to 0.58 in SHP1 (k-NN), showing strong seasonality dependence, and up to 0.68 in SHP2 (Gradient Boosting). For SHP1, the best model (k-NN) correctly detected 36 long events, while 15 were misclassified as no events and 38 false alarms were produced. For SHP2, the best model (Gradient Boosting) correctly detected 69 long events, misclassified 23 as no events, and produced 42 false alarms. The findings highlight that probabilistic machine learning-based forecasting can effectively support predictive O&M planning, particularly for manually operated or service-operated SHPs.

Directed Energy Deposition (DED) processes offer the advantage of producing larger parts with higher deposition rates compared to Powder Bed Fusion (PBF) additive manufacturing (AM). However, DED typically results in simpler geometries and lower resolution. When using Wire and Arc- based DED, even larger components can be manufactured at an accelerated rate, but the higher heat input may lead to undesirable microstructures, adversely affecting mechanical properties. To ensure defect-free depositions, precise process control is essential, including optimizing deposition paths, regulating inter-layer temperature, and maintaining a consistent nozzle-to-layer distance. One effective approach to improving material integrity is the application of in-situ vibrations during deposition. This technique helps reduce porosity and grain size while also enhancing surface waviness and mitigating residual stress buildup. Further refinement of material properties can be achieved through appropriate thermo-mechanical processing, leading to mechanical characteristics comparable to conventionally produced steel. This paper explores the impact of in-situ vibrations and heat treatment through case studies, analysing their effects on surface waviness, residual stress distribution, porosity, microstructure, grain size, mechanical properties, and fracture toughness. The findings demonstrate the significant benefits of these process enhancements in improving the mechanical performance of DED- fabricated components.

The development of electromechanical linear actuators (EMLAs) aims at compactness, energy efficiency, and high reliability. Conventional design methods often rely on costly prototypes and individual considerations of mechanics, electromagnetics, and control dynamics. This leads to long development cycles, inadequate treatment of nonlinear effects, and suboptimal performance. To address these challenges, our paper introduces a novel hybrid design methodology, integrating Analytical Modeling, Finite Element Analysis (FEA), Genetic Algorithms (GAs), and targeted experiments. Analytical Modeling provides rapid sizing, FEA combined with a GA refines geometry, and targeted experiments quantify nonlinear effects (friction, wear, thermal variability, and dynamic resonances). Unlike conventional methods, the integration is performed within iterative loops, using empirical data to refine simulation assumptions. As a result, development time is reduced by 30% and nonlinear effects are precisely addressed. The method is demonstrated on an automotive-grade EMLA. Its design is based on a claw-pole Permanent Magnet Stepper Motor, a trapezoidal lead screw, and an open-loop control with Hall effect end-position detection. After applying the method, the EMLA delivers more than 40 N of push force and achieves 600,000 actuations under the required conditions, making it suitable for various applications.

The industrial implementation of automated visual inspection leveraging deep learning is limited due to the labor-intensive labeling of datasets and the lack of datasets containing images of defects, which is especially the case in high-volume manufacturing with zero defect constraints. In this study, we present the FuseDecode Autoencoder (FuseDecode AE), a novel reconstruction-based anomaly detection model featuring incremental learning. Initially, the FuseDecode AE operates in an unsupervised manner on noisy data containing predominantly normal images and a small number of anomalous images. The predictions generated assist experts in distinguishing between normal and anomalous samples. Later, it adapts to weakly labeled datasets by retraining in a semi-supervised manner on normal data augmented with synthetic anomalies. As more real anomalous samples become available, the model further refines its capabilities through mixed-supervision learning on both normal and anomalous samples. Evaluation on a real industrial dataset of coating defects shows the effectiveness of the incremental learning approach. Furthermore, validation on the publicly accessible MVTec AD dataset demonstrates the FuseDecode AE's superiority over other state-of-the-art reconstruction-based models. These findings underscore its generalizability and effectiveness in automated visual inspection tasks, particularly in industrial settings.

This study explores an innovative method to enhance Directed Energy Deposition (DED) of aluminum 5356 products by integrating an electromagnetic vibration system into the DED setup. The application of vibrations significantly improved surface quality, reducing surface waviness and increasing building efficiency by 14%, from 78.5% to 92.25%.Gas porosity was reduced from 1.5 ± 0.04% in as-built (AB) components to 0.34 ± 0.07% in vibration-assisted (VA) parts. Tensile tests showed a marked reduction in anisotropy, with the tensile strength deviation between the x and z directions decreasing to less than 0.4% for vibration-assisted samples, compared to 7.9% for asbuilt ones. Additionally, secondary phase analysis revealed a homogenization effect, with magnesium- and iron-rich precipitates displaying a finer dispersion (3.57 ± 3.42 µm²) compared to 11.28 ± 12.49 µm² in as-built parts. Overall, the findings highlight the potential of vibration-assisted DED to improve part properties, reduce defects, and advance the DED manufacturing process.

This study introduces an innovative approach of incorporating in-situ vibrations into Directed Energy Deposition (DED) process, utilizing aluminium 5356 wire, a welding arc heat source, and an electromagnetic vibration system. The findings underscore the method's efficacy in mitigating defects of aluminium 5356 alloy fabricated part. Specifically, the anisotropic distribution of residual stress was reduced by 60%, leading to improved structural integrity. Moreover, the application of in-situ vibrations resulted in a marked reduction in surface waviness, thereby enhancing surface quality and increasing the building efficiency factor by 14% (from 78.5% to 92.25%). Gas porosity was substantially decreased, with a reduction from 1.5 ± 0.04% in as-built parts (AB) to 0.34 ± 0.07% in vibration-assisted (VA) parts. A notable 25% reduction in grain size was observed. The synergistic effect of vibrations and lower interpass temperatures effectively influenced the solidification kinetics, reducing microstructural anisotropy by eliminating characteristic columnar growth and promoting the formation of equiaxed grains. Tensile tests confirmed elimination of anisotropy in strength, with the ultimate tensile strength deviation between the x and z directions of less than 0.4% for VA samples, compared to 7.9% for AB samples. Analysis of secondary phases confirmed a homogenisation effect, evidenced by a lower segregation rate and more finely dispersed peaks of magnesium and iron-enriched precipitates in the VA samples compared to the AB ones (3.57 ± 3.42 μm2 vs. 11.28 ± 12.49 μm2). These results highlight the substantial potential of vibration-assisted DED in improving the properties of manufactured parts.

A non-contact optical inspection method for detecting voids in translucent composites is presented. Structured laser light is used to illuminate the inspected part. As the light penetrates the matrix, it scatters and is reflected from internal structures, rendering them perceptible in close proximity of the laser illumination. A systematic image acquisition and scanning approach is employed along with image processing to reconstruct and visually represent the internal composition of the inspected part. Experiments involving translucent epoxy and polyester based composites demonstrate capability to detect voids with depths reaching up to 5 mm. The detection depth is predominantly influenced by the light transmittance properties of the matrix, as well as the density and quantity of fiber layers. The arrangement of the camera and laser on the same side of the inspected part facilitates the examination of parts with varying thicknesses. The presented method is intended for automated inspection in mass production by leveraging its non-contact characteristics and high operational velocity.

This paper discusses directed energy deposition of 15-5 PH, which was successfully tailored with precipitation hardening (PH) to achieve the desired properties. Parametric analysis of solution annealing and aging at various time and temperature combinations was performed. Material characterisation was done in the as-deposited, solution annealed, and PH states. Investigations included microscopy, SEM/EDS, fractography, hardness, tensile, and impact toughness test. The as-deposited microstructure was composed of martensite laths along with delta ferrite. Optimisation of solution annealing was mandatory to achieve homogeneous austenite, which allowed PH. PH resulted in similar properties compared to conventionally produced steel. Peak aging resulted in 450 HV10 and an Rm of 1350 MPa, while the over-aged condition resulted in an impact toughness of over 77 J/cm(2).

Efficient workspace awareness is critical for improved interaction in cooperative and collaborative robotic applications. In addition to safety and control aspects, quality-related tasks such as the monitoring of manual activities and the final quality assessment of the results are also required. In this context, a visual quality and safety monitoring system is developed and evaluated. The system integrates close-up observation of manual activities and posture monitoring. A compact single-camera stereo vision system and a time-of-flight depth camera are used to minimize the interference of the sensors with the operator and the workplace. Data processing is based on a deep learning to detect classes related to quality and safety aspects. The operation of the system is evaluated while monitoring a human-robot manual assembly task. The results show that the system ensures a high level of safety, provides reliable visual feedback to the operator on errors in the assembly process, and inspects the finished assembly with a low critical error rate.

In this paper an IoT application of LoRa is presented. The application is related to the so-called precision beekeeping. This term is associated with the monitoring of many variables within a hive and in its vicinity, which can assist the beekeeper at all the activities that have to be done in beekeeping practice. In order to achieve that kind of functionality the so called wireless sensor network has to be realized. First an overview of such technologies is given. Our decision was to take LoRa. The core of the system is an Arduino microcontroller with the addition of the LoRa shield. In the paper a process of communication between master and slave unit is described. In order to demonstrate the applicability, the slave unit has an additional temperature sensor attached. The system was verified by a successful temperature measurement lasting several days

Friction riveting represents a promising technology for joining similar and/or dissimilar materials of light-weight components. However, the main drawback of the technology is that it is primarily used only with special machines for friction welding that have a force control. In this study we used accessible CNC machines with a position control. A set of friction riveting experiments was performed to establish the relationship between the processing parameters, the rivet formation and its mechanical strength. During the manufacturing process, the axial force and torque were constantly measured. The fabricated joints were examined using an X-ray imaging technique, microstructural analyses, and mechanical tests. The samples were subjected to the pull-out test to analyse the joints’ strength and determine the failure mode type. In addition, a correlation between the friction riveting processing parameters, the rivet penetration depth, the rivet shape and the joint strength was established. The results depict that a higher axial force in the first production phase at the higher feeding rate increases the penetration depth, while in the second phase at lower feeding rate, an anchoring shape of a rivet forms.

This paper discusses directed energy deposition (DED) of precipitation hardening martensitic stainless steel 15-5 PH, using filler wire and welding arc heat source. This steel has a wide range of applications from turbine and aerospace to marine sector due to its tailoring properties. 15-5 PH provides the ability to tailor its material properties through post deposition heat treatment, which is especially interesting in the flexible nature of AM. In this study the deposited material was precipitation hardened by solution annealing and aging heat treatments. Parametric analysis of both heat treatments was done at various time and temperature combinations. Material characterization was done in as deposited, solution annealed, and precipitation hardened conditions. Studies included optical microscopy and scanning electron microscopy (SEM/EDS) microstructure analysis, hardness measurement, tensile and instrumented impact toughness testing. SEM fractography was done on tensile and Charpy fractured surfaces. As deposited 15-5 PH steel microstructure was composed of martensite laths along with delta ferrite. Optimization of solution annealing was done to achieve homogeneous austenite and dissolve delta ferrite in DED-specific microstructure. This enabled precipitation of copper rich precipitates. Precipitation hardening of DED 15-5 PH resulted in similar hardness, tensile and impact properties compared to conventionally produced 15-5 PH steel.

An analysis presents a weld leakage problems of automotive pressure sensor, caused by weld crater cracking. A two-beam laser welding (TBLW) was used to weld a circumferential weld, which undesirably increases the probability of weld leakage by creating two weld end craters on a single weld. Microstructural analysis showed that microsegregation of alloying elements combined with imposed strains causes solidification cracking at the weld end crater. A novel "zigzag" laser power ramp-down was used and the results showed a limited crack propagation by producing significantly shorter discontinuous cracks. In such weld crater endings the leakage is no longer an issue.

Wire Arc Additive Manufacturing (WAAM) is recently receiving increasing attention as it offers a way to produce parts on a meter scale with high deposition rates and low production and capital costs. A major challenge in WAAM is the inherently variable layer height, which causes geometric inaccuracies in the final product. This paper proposes an on-line layer height control and in-process toolpath replanning for Gas Metal Arc (GMA) WAAM system, which enables better geometric accuracy when depositing tall shell parts. Deposition arc current and voltage were considered as control variables for layer height control. Arc current proved to be a more suitable control variable due to its higher sensitivity and higher correlation with changes in the contact tip to work distance. Different types of layer height controllers were developed and evaluated by depositing 30-layer high thin walls. Additionally, a re-slice algorithm was developed and implemented in the system controller. It compares the deposited section of the part to the CAD model after each layer has been built. The algorithm replans (re-slices) the toolpath to ensure better geometric accuracy of the finished part when the deviation exceeds the maximum allowable threshold. A 160 mm high case study part was manufactured and 3D scanned to demonstrate the novelty of the developed system. Results show that the layer height control keeps distance between the welding torch and the part surface during the deposition. Re-slice control, combined with layer height control, enables the manufacture of parts with correct final geometry.

Research in the area of robotic systems has greatly benefited from the use of simulation models. Recent approaches allow the transfer of developed algorithms from simulation to reality (sim-to-real) and increasingly accurate representations of real systems as simulation models (real-to-sim). The paper presents an architecture based on open software that supports simultaneous experiments on real robots and their simulation models. Two illustrative examples are shown: a digital twin of an industrial robot and a sim-to-real transfer in an autonomous mobile robot system. The possibilities of future research on the interaction between robotic systems and their simulation models are discussed.