J. Exp. Theor. Anal., Volume 4, Issue 1 (March 2026) – 13 articles

This paper proposes a new Unified Namespace (UNS)-based architecture to improve predictive and prescriptive maintenance of industrial equipment and overcome challenges such as incomplete data, poor interoperability, and disconnected IT/OT environments. The framework combines interoperable data formats in real-time sensor data, predictive modeling, prescriptive analytics, and simulations of digital twins, using UNS as a centralized, protocol-agnostic data layer that is scalable and complies with Industry 4.0 and Pharma 4.0 standards. The suggested methodology increases data accessibility, reduces integration complexity, and allows low-latency analytics and automated decision-making. Machine learning predictive models achieved more than 94% accuracy in predicting equipment failures. Prescriptive analytics provides maintenance recommendations to reduce downtime and risks. The feedback loops of digital twins can enhance the accuracy of predictions and allow decision optimization through what-if analysis. A test-bench deployment showed a higher performance compared to traditional point-to-point integration, with lower latency (approximately 18 ms vs. approximately 31 ms), decreasing packet loss (0.40% vs. 3.11%), and higher model accuracy (94.20% vs. 87.51%). The structure avoided more than 4000 simulated breakdowns in the test-bench environment, indicating dependability. The study connects the theoretical applications of the UNS with the actual maintenance processes and provides a sound approach to the industrial analytics and optimization of the equipment. Full article

Electrochemical machining (ECM) offers precise shaping by material dissolution with negligible mechanical or thermal impact on the workpiece. Metal parts with three-dimensional shapes, such as freeform surfaces or additively manufactured parts, can be addressed by robots with up to six degrees of freedom without significant mechanical impacts on the end-effectors and robots. This study summarizes the state-of-the-art of the use of robots in ECM by assessing the relevant literature. Several investigations were found that implemented or conceptualized the use of robotic arms in ECM sinking, jet-ECM or wire ECM, mainly for effective utilization of the processes. This study includes results of pure ECM, as well as hybrid ECM processes and the use of robots considering their accuracy, degrees of freedom and their application potential. Special emphasis is given to the role of robots in improving machining accessibility and their usability for valuable components in the aerospace, biomedical, and tooling industries. Furthermore, the review provides insights into electrolyte delivery mechanisms and pump configurations that facilitate efficient process performance. Overall, the utilization of robots in ECM not only enhances the process flexibility and surface quality but also aligns well with the aim of intelligent, automated, and high-precision manufacturing. Full article

Deep learning models have become central to automated Printed Circuit Board (PCB) defect detection. However, recent work has raised concerns about how reliably these models express confidence in their predictions, particularly when deployed in safety-critical inspection systems. This study conducts an empirical investigation of epistemic uncertainty across representative architectures used in PCB inspection: the two-stage Faster R-CNN detector, the one-stage YOLOv8 detector, and their corresponding classification counterparts, ResNet-50 and YOLOv8-Cls. Monte Carlo Dropout (MCD) was applied during inference to compute predictive entropy, mutual information, softmax variance, and bounding-box variability across multiple stochastic forward passes on both multiclass and binary inspection datasets. On the multiclass SolDef_AI dataset, Faster R-CNN achieved substantially stronger detection performance (mAP = 0.7607, F1 = 0.9304) and lower predictive entropy, with more stable localisation. In contrast, YOLOv8 produced markedly weaker performance (mAP = 0.2369, F1 = 0.3130) alongside higher entropy and greater bounding-box variability. On the binary Jiafuwen datasets, the YOLOv8-Cls model achieved higher overall performance (F1 = 0.6493) compared with the ResNet-50 classifier (F1 = 0.4904), reflecting its strength in simpler binary inspection tasks. Across uncertainty metrics, predictive entropy and mutual information were more sensitive to dataset size, showing higher and more variable values in the smaller multiclass dataset, whereas softmax variance and bounding-box variability appeared more architecture-dependent. These findings demonstrate that architectural choice, dataset structure, and task formulation jointly influence both performance and uncertainty behaviour. By integrating conventional metrics with uncertainty estimates, this study provides a transparent benchmark for assessing model confidence in automated optical inspection of PCBs. Full article

In this work, the sintering behavior of tapes prepared via tape casting from stainless-steel and zirconia powders is investigated by optical—as well as push-rod—dilatometry. Both methods are compared in terms of sample preparation, measurement conditions, and advantages and disadvantages. The experimental work shows the advantages of optical dilatometry in the characterization of the sintering behavior of load-free sintering tapes and the possibility of simultaneously observing sample warpage and deformation. Push-rod dilatometry requires a constant load on the sample, which influences measurement in the case of tapes with lower mechanical stability due to their sensitivity to deformation, but it has advantages because of its higher accuracy in measuring dimensional changes. In the case of warpage, shrinkage due to the sintering of the sample is superimposed by an irregular deformation process that can be separated by analytical methods. No in-plane shrinkage anisotropy of the tapes is observed for either type of tape. In the case of the push-rod dilatometer, an additional peak in the shrinkage rate is observed in the early stage of compaction, along with a slight shift and an increased maximum in the compaction rate. This is most likely due to the effects of the contact pressure of the push-rod. Full article

Tangential turning produces an asymmetric cutting-force system that may cause tool and workpiece deflection, leading to cylindricity, coaxiality, and roundness deviations in practice. This study investigates the relationships between three cutting force components and form errors during tangential turning of 42CrMo4 steel. Tangential, axial, and radial forces were measured under systematically varied cutting speed, feed, and depth of cut, and the resulting cylindricity, coaxiality, and roundness parameters were obtained through precision form measurements. The depth of cut showed the strongest influence on cutting forces, with high correlations to all components (r = 0.709–0.870). Feed was most closely associated with coaxiality error (r = 0.730), while cutting speed was primarily related to cylindricity deviation (r = 0.766). The novelty of this work lies in the combined and quantitative analysis of full cutting-force components and multiple form–accuracy descriptors within a single experimental framework for tangential turning. The results directly link process load to geometric accuracy and provide guidance for selecting cutting parameters to improve dimensional precision in tangential turning of alloy steels. Full article

A vibration energy harvester (VEH) based on the principle of variable magnetic reluctance has been developed to enable wireless and maintenance-free power supply for condition monitoring sensors in vibrating machinery. Conventional battery or wired solutions are often impractical due to limited lifetime and high installation costs, motivating the use of vibration-based energy harvesting. The proposed VEH converts mechanical vibrations into electrical energy through the relative motion of a movable ferromagnetic core within a magnetic circuit. Unlike conventional VEH designs, where the magnet is the moving element, this concept utilizes a movable ferromagnetic core in combination with a stationary pole piece for voltage induction. This configuration enables a compact and easily adjustable proof mass, as neither the coil nor the magnet needs to be moved. The VEH is designed to operate effectively under excitation frequencies between $16 \mathrm{H}\mathrm{z}$ and $50 \mathrm{H}\mathrm{z}$ and acceleration levels from $9.81 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ (equivalent to $1 \mathrm{g}$) up to $98.1 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ (equivalent to $10 \mathrm{g}$). To ensure a reliable power supply, the VEH must deliver a minimum electrical output of $0.1 \mathrm{m}\mathrm{W}$ at the lowest excitation ($1 \mathrm{g}$) while maintaining structural integrity. Additionally, the maximum permissible displacement amplitude of the movable core is limited to $1.15 \mathrm{m}\mathrm{m}$ to avoid mechanical damage and ensure durability over long-term operation. Coupled magnetic-transient and mechanical finite element method (FEM) simulations were conducted to analyze the system’s dynamic behavior and electrical power output across varying excitation frequencies and accelerations. A laboratory prototype was developed and tested under controlled vibration conditions to validate the simulation results. The experimental measurements confirm that the VEH achieves an electrical output of $0.166 \mathrm{m}\mathrm{W}$ at $9.81 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ and $16 \mathrm{H}\mathrm{z}$, while maintaining the maximum allowable displacement amplitude of $1.15 \mathrm{m}\mathrm{m}$, even at $98.1 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ ($10 \mathrm{g}$) and $50 \mathrm{H}\mathrm{z}$. The strong agreement between simulation and experimental data demonstrates the reliability of the coupled FEM approach. Overall, the proposed VEH design meets the defined performance targets and provides a robust solution for powering wireless sensor systems under a wide range of vibration conditions. Full article

Multi-laser powder bed fusion is an emerging additive manufacturing technology that enables the production of high-performance components with intricate geometries and large aspect ratios. These tall, slender structures are highly susceptible to steep thermal gradients and residual stress, leading to deformation that compromises dimensional accuracy and structural integrity. This study investigates how geometric compensation, support structure design, and part scaling influence thermal deformation in Inconel 718 components fabricated via multi-laser powder bed fusion. Using pre-compensation, iterative support refinements, and scaled experimental builds, the deformation response across multiple geometries and print strategies is evaluated. Both compensated and original designs are printed on a commercial system equipped with three simultaneously operating lasers. Results show that printing high-angle surfaces without support structures is infeasible, as thermally induced warping and delamination lead to catastrophic failures. Conical support structures spanning critical regions reduce deformation by more than 50% compared to unsupported builds. Reduced-scale parts, however, do not reliably replicate full-scale deformation behavior due to altered boundary conditions and thermal pathways. These findings highlight the need for integrated design-for-AM workflows where compensation, support design, and scale effects are addressed jointly. The study demonstrates that deformation mechanisms do not scale linearly, emphasizing the limitations of small-scale proxies and the necessity of full-scale validation when developing reliable, deformation-aware design strategies for multi-laser powder bed fusion. Full article

This study explores the viability of ultra-high-performance concrete (UHPC) as a structural material for compressed air storage (CAES) systems, combining comprehensive experimental testing and numerical simulations. Scaled (1:20) CAES tanks were designed and tested experimentally under controlled pressure conditions up to 4 MPa (580 psi), employing strain gauges to measure strains in steel cylinders both with and without UHPC confinement. Finite element models (FEMs) developed using ANSYS Workbench 2024 simulated experimental conditions, enabling detailed analysis of strain distribution and structural behavior. Experimental and numerical results agreed closely, with hoop strain relative errors between 0.9% (UHPC-confined) and 1.9% (unconfined), confirming the numerical model’s accuracy. Additionally, the study investigated the role of a rubber interface layer integrated between the steel and UHPC, revealing its effectiveness in mitigating localized stress concentrations and enhancing strain distribution. Failure analyses conducted using the von Mises criterion for steel and the Drucker–Prager criterion for UHPC confirmed adequate safety factors, validating the structural integrity under anticipated operational pressures. Principal stresses from numerical analyses were scaled to real-world operational pressures. These thorough results highlight that incorporating rubber enhances the system’s structural performance. Full article

This paper presents a practical and cost-effective method for in-motion estimation of a vehicle’s CoG position in all three directions by measuring accelerations during two types of maneuvers: braking (longitudinal and vertical CoG estimation) and cornering (lateral and vertical CoG estimation). The proposed method’s main advantage is that it does not require knowledge of vehicle characteristics, such as mass distribution, suspension geometry, or inertia parameters. It relies solely on the known distances between the sensors and their positions relative to a defined reference point on the vehicle. To validate the developed method, experimental tests were conducted on a prototype vehicle, varying the load conditions for the proposed driving scenarios. The CoG position obtained from dynamic maneuvers was compared with reference values derived from static measurements. The results showed that the proposed method could estimate the CoG position with an average error of 3% in the longitudinal direction, a maximum error of 12% in the lateral direction, and a maximum error of 14% in the vertical direction. Full article

Simplified and scalable models of physical systems are extremely valuable in a variety of different engineering fields to test and diagnose particular modes of failure and optimize build conditions. In this work, we develop a practical method to prepare and analyze giant unilamellar vesicles (GUVs) for detailed biophysical interrogations. The method is rapid, scalable, and versatile, where characterization of lipid membrane conformational changes can be performed on multiplexed samples using tissue culture plates and a convenient, high-throughput fluorescence microscopy setup. The simplicity of the setup is enabled by an AI image recognition model that, when trained on the appearance of GUVs in the images, outperforms other image segmentation methods such as the watershed algorithm or the Hough transform. The method allows for the rapid quantification of entire 96-well plates containing in total O (1,000,000) GUVs and provides a potential testbed for the development of drugs. We highlight the power of our system by including large-scale data on the screening of lipophilic analogs of the small molecule antimetabolite carmofur. Full article

This study investigates the fatigue strength of a motor hanger used in high-speed electric multiple units (EMUs). Finite element analysis and field measurements revealed that reduced weld penetration significantly increases stresses in welded regions. Line tests demonstrated that a 100 Hz torque ripple induces elastic vibration of the hanger, serving as the primary driver of stress propagation, with stress and acceleration levels increasing proportionally with the torque ripple amplitude. This 100 Hz excitation lies close to the hanger’s constrained modal frequency of about 109 Hz, creating a near-resonance condition that amplifies dynamic deformation at the welded joints and accelerates fatigue crack initiation. Hangers with lower in situ modal frequencies exhibited higher equivalent stresses. Joint dynamic simulation further showed that increasing motor mass reduces the longitudinal acceleration of the hanger, while enhancing the radial stiffness of rubber nodes markedly decreases both longitudinal and vertical vibration accelerations as well as stress responses. Based on these insights, a structural improvement scheme was developed. Strength analysis and on-track tests confirmed substantial reductions in overall and weld stresses after modification. Fatigue bench tests indicated that the critical welds of the improved hanger achieved a service life of 15 million km, more than twice that of the original structure (7.08 million km), thereby satisfying operational safety requirements. Full article

Optimising the learning rate is essential for efficient neural network training, but static methods can cause overshooting or undershooting, while adaptive techniques like ADAM often struggle to balance exploration and exploitation. We introduce the Polling Method, an ensemble-based optimisation approach that dynamically selects the most effective learning rate at each step, improving convergence and mitigating issues inherent in traditional optimisation strategies. By evaluating base models with varying learning rates at each epoch, the method adaptively balances exploration and exploitation without being constrained by predefined functions or gradient noise. This study details the theoretical foundation, implementation, and integration of the Polling Method with the ADAM optimiser, demonstrating its effectiveness in Artificial Neural Networks and Bayesian variational inference. The results demonstrate that Polling Method-ADAM reduces absolute error by 50% compared to ADAM alone, while also accelerating convergence. In Bayesian optimisation, it reduces the mean gradient shift from 0.85 to 0.7835 over 500 iterations, indicating improved stability in high-dimensional problems. By introducing adaptive learning rate selection within training, the Polling Method enhances optimisation efficiency while mitigating noise accumulation. This framework provides a computationally efficient, flexible alternative for deep learning applications, offering significant improvements over traditional optimisers and a potential breakthrough in neural network training strategies. Full article