Engineering Research Express

ISSN: 2631-8695

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Engineering Research Express (ERX) is a broad, multidisciplinary journal devoted to publishing new experimental and theoretical research covering topics extending across all areas of engineering science including interdisciplinary fields.

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The following article is Open access
Robust and deterministic optimal design of tunable metastructures for vibration suppression under uncertainty
Samet Ceri 2026 Eng. Res. Express 8 165534
View article, Robust and deterministic optimal design of tunable metastructures for vibration suppression under uncertainty PDF, Robust and deterministic optimal design of tunable metastructures for vibration suppression under uncertainty
Locally resonant metastructures provide an effective passive solution for vibration attenuation through tailored distributions of attached resonators. However, most existing designs are developed under deterministic assumptions and are evaluated primarily under idealized excitation conditions, limiting understanding of their performance under uncertainty and realistic operating environments. This study investigates the vibration attenuation and uncertainty sensitivity of a one-dimensional constant-mass lumped metastructure using three absorber-frequency distributions: a reference graded design, a deterministic-optimal design obtained by minimizing the band-limited $H_2$ response, and a robust-optimal design obtained through uncertainty-aware optimization under bounded absorber-frequency perturbations. The resulting configurations are evaluated using frequency-domain and time-domain analyses, stochastic excitation simulations, modal analysis, and probabilistic performance metrics including cumulative distribution functions and conditional value-at-risk. The results demonstrate that vibration attenuation is governed by the redistribution of resonant interactions between the host structure and the absorber array. The graded configuration promotes broadband energy diffusion through distributed resonance mechanisms, while the deterministic-optimal design concentrates modal coupling and achieves the lowest nominal energy response. Modal analyses reveal distinct resonance-redistribution mechanisms associated with each absorber-frequency distribution. Under realistic broadband, narrowband, and coloured stochastic excitations, the relative performance of the designs depends strongly on the spectral characteristics of the forcing environment. The uncertainty analyses further show that uncertainty-aware optimization does not necessarily yield superior robustness under moderate uncertainty levels. For the uncertainty range considered in this study ( $\pm8\%$ absorber-frequency variation), both the graded and deterministic-optimal configurations remain highly competitive and, in several performance metrics, outperform the robust-optimal design. These findings highlight the importance of simultaneously considering excitation characteristics, uncertainty magnitude, and resonance-redistribution mechanisms when designing absorber-based metastructures. The proposed framework provides new physical insight into vibration attenuation in locally resonant metastructures and offers a systematic methodology for evaluating the trade-off between nominal performance and robustness.
https://doi.org/10.1088/2631-8695/ae98e7
The following article is Open access
Unsupervised deep learning for IoT botnet detection in a surveillance VLAN via multi-source traffic, threat intelligence and honeypot corroboration
Özkan Zeybek and Hasan Güler 2026 Eng. Res. Express 8 165339
View article, Unsupervised deep learning for IoT botnet detection in a surveillance VLAN via multi-source traffic, threat intelligence and honeypot corroboration PDF, Unsupervised deep learning for IoT botnet detection in a surveillance VLAN via multi-source traffic, threat intelligence and honeypot corroboration
The increase in internet of thing devices, especially within VLANs in corporate networks, introduces significant security risks from advanced botnet attacks. Traditional signature-based detection methods struggle to identify encrypted, stealthy command-and-control traffic, while high false-positive rates overwhelm security teams with excess data. This study proposes an unsupervised deep-learning detection system using autoencoder (AE)-family models that learn normal traffic behaviors and identify anomalies through reconstruction error (MSE). An architecture comparison across five random seeds shows that a simple convolutional neural network (CNN)-AE performs similarly to a CNN-long short-term memory-AE (mean ROC-AUC difference of −0.0195 ± 0.0194); the hybrid is less stable and slower. The key contribution is the integrated, operational pipeline with a transparent evaluation approach, not architectural innovation. Tested on multi-source metadata—Wireshark, firewall logs, T-Pot honeypot—collected over fifteen days from a real enterprise VLAN, the system incorporates external threat intelligence (AbuseIPDB, OTX) and contextual behaviors through a multi-dimensional scoring system (MDSS), converting raw detections into prioritized risk scores. Under deployment, the model flags 3.75% of traffic as anomalous, with approximately 1% false positives on benign devices. Stress tests—including feature-leakage ablation, multiple seed runs, temporal holdouts, and clean-device testing—suggest a realistic record-level ROC-AUC of 0.72–0.78, compared to near-perfect (>0.99) results in saturated IP-partition testing, which is reported solely as an upper bound. The MDSS assigns risk tiers: 0 Very-High, 2 High, 26 Medium, and 37 Low for the 65 monitored devices. Anomalies are independently validated via two MITRE ATT&CK pipelines—one focused on honeypot/policy sources and one based solely on the model’s detections—both identifying the same techniques (T1071, T1573, T1046, T1090). This confirms that model decisions are not circularly based on the evaluation labels. With low-latency, GDPR/KVKK-compliant, metadata-only analysis and operational prioritization, this framework bridges the gap between deep-learning security solutions’ theoretical potential and their real-world enterprise application. It provides a transparent, reproducible methodology for proactive, scalable, and interpretable botnet detection.
https://doi.org/10.1088/2631-8695/ae9b66
The following article is Open access
Simulation-Driven Diagnosis of Stator Phase-Current Imbalance in Wind Turbine Induction Generators Using Multi-Signal Features and Explainable Machine Learning
Sara SGHIOURI et al 2026 Eng. Res. Express
View article, Simulation-Driven Diagnosis of Stator Phase-Current Imbalance in Wind Turbine Induction Generators Using Multi-Signal Features and Explainable Machine Learning PDF, Simulation-Driven Diagnosis of Stator Phase-Current Imbalance in Wind Turbine Induction Generators Using Multi-Signal Features and Explainable Machine Learning
Wind turbine reliability depends on timely identification of electromechanical faults, especially in generator-related subsystems under variable mechanical loads. This study presents a simulation-based, multi-signal, and physically interpretable diagnostic workflow for wind turbine electrical systems. It combines multiphysics simulation, FFT feature extraction, and explainable machine learning, emphasising the integration of existing methods rather than developing new AI models. A COMSOL Multiphysics (2D electromagnetic with 3D multibody dynamics) model of an induction machine simulated both healthy and imbalanced operating conditions with increasing stator phase-A current imbalance (parameter ε). The verified fault mechanism was incorporated into the model. From these simulations, a multisignal dataset was built using electromagnetic torque, rotor speed, electromagnetic force, and foundation force responses across 9 configurations, resulting in 54 samples (each with 100 features) classified into healthy, minor, and major imbalance groups. We tested Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms. Repeated stratified cross-validation showed Random Forest performed best, with an average accuracy of 92.3% (±4.1%) and macro-F1 of 0.764 (±0.146). A leave-one-configuration-out test, where no data from the same configuration appear in both training and testing, produced more conservative results: 46.3% accuracy and 0.317 macro-F1, with no healthy-condition samples correctly classified, because only one independent healthy configuration was available. SHAP analysis identified foundation-force spectral energy in the 50-150 Hz range as the most important predictor, suggesting imbalance severity at the configuration level. Since foundation-force features are fixed within each configuration, this indicator should be considered a configuration-level marker rather than an individual sample marker. Overall, the sample-level results are promising, indicating that the multi-signal, physics-based feature set and interpretability are useful. However, the configuration-level results suggest that the current 9-configuration simulation setup is not yet sufficient for definitive diagnostic accuracy.
https://doi.org/10.1088/2631-8695/ae9f1c
The following article is Open access
Evaluation of pulsed and standard gas metal arc welding waveforms for energy efficiency and carbon mitigation in automotive assembly: a systemic industrial approach
Isaratat Phung-on et al 2026 Eng. Res. Express 8 165407
View article, Evaluation of pulsed and standard gas metal arc welding waveforms for energy efficiency and carbon mitigation in automotive assembly: a systemic industrial approach PDF, Evaluation of pulsed and standard gas metal arc welding waveforms for energy efficiency and carbon mitigation in automotive assembly: a systemic industrial approach
The automotive industry is transiting toward carbon neutrality. Optimizing energy intensive manufacturing processes like gas metal arc welding (GMAW) has become an important aspect. This research provides a multi-dimensional comparative analysis of standard current and pulsed current waveforms for welding Steel Plate Cold Commercial cold-rolled steel (1.2 mm and 2.8 mm thickness), specifically evaluating their contribution to energy consumption, productivity, and Scope 2 carbon emissions. All tested configurations were validated for structural integrity conforming to American Welding Society (AWS) D1.3 standards. There were 7 types of welding in this study, but only 5 types could be passed conforming to AWS D1.3 requirements. The time study was implemented to determine Standard Time which considered rating factors and industrial allowances. Power measurement was performed for both input and output sides of welding machine. Results for 1.2 mm thin-gauge specimen showed that pulsed current welding reduced unit energy consumption and carbon emissions by 14.26% compared to Standard current welding. For 2.8 mm specimen, a ‘Productivity Paradox’ was identified: while the pulsed current welding required higher peak power, its character to achieve complete joint penetration in a single-pass with square butt configuration that eliminated the requirement for the V-groove preparation and double-sided welding required by the standard current welding. This led to a 59.89% reduction in Standard Time and a 58.87% decrease in total operational costs. By estimating annual impact for producing 100 000 units, switching to Pulsed GMAW with 180 000 THB investment would pay back within 5.793 months through energy and labor-saving providing a compelling economic benefit for manufacturers to invest in and implementing pulsed current welding machines on the production floor. The findings confirm that pulsed current welding could provide pathways for compliance with reduction of carbon emissions with low capital expense strategy for achieving ‘Green Industry’ objectives in high-volume automotive production.
https://doi.org/10.1088/2631-8695/ae95bf
The following article is Open access
Comparative evaluation of static and dynamic EIS parameters for non-destructive strength prediction of plain concrete
Thu Huong Nguyen et al 2026 Eng. Res. Express 8 165115
View article, Comparative evaluation of static and dynamic EIS parameters for non-destructive strength prediction of plain concrete PDF, Comparative evaluation of static and dynamic EIS parameters for non-destructive strength prediction of plain concrete
Accurate assessment of compressive strength is critical for ensuring the structural integrity and long-term durability of concrete infrastructures. While traditional destructive testing shows reliable data, its application is hindered for the continuous inspection of in-service structures. This study discusses electrochemical impedance spectroscopy (EIS) as a non-destructive evaluation technique for predicting the concrete compressive strength of plain concrete. We evaluated four concrete mixtures with varying water-to-cement ratios (w c⁻¹ = 0.54–0.79). Standard compressive strength tests and broadband alternating current impedance measurements (20 Hz–20 MHz) were performed at curing ages of 14, 28, and 56 d. The experimental results show that as the cement hydration process progresses, the microstructural pore network densifies, which is closely linked to the increase of compressive strength. Concurrently, these microstructural changes are aligned with distinct electrochemical signatures. Specifically, the high-frequency bulk resistance (R_b) and the geometrically normalized bulk resistivity (ρ_b) increased stability with both curing age and compressive strength. Conversely, the dynamic cut-off frequency (f₁) demonstrated a consistent downward trend due to the severely restricted ionic charge mobility. Based on these results, empirical regression models were developed using EIS characteristic parameters as independent variables to estimate concrete strength under laboratory conditions. Comparative analyses reveal that ρ_b shows higher predictive accuracy and greater stability compared to the cut-off frequency, primarily due to its effective compensation for specimen dimensional variations. Ultimately, this research provides insights into the relationship between the micro-electrochemical behavior of cementitious materials and their macroscopic mechanical properties, establishing a stable laboratory-based framework for the non-destructive strength evaluation of concrete.
https://doi.org/10.1088/2631-8695/ae98e9
The following article is Open access
Impact of Climatic Conditions on the Performance of PV-PCM Systems: A CFD Study across Tunisian Regions
Abir Bouzid et al 2026 Eng. Res. Express
View article, Impact of Climatic Conditions on the Performance of PV-PCM Systems: A CFD Study across Tunisian Regions PDF, Impact of Climatic Conditions on the Performance of PV-PCM Systems: A CFD Study across Tunisian Regions
The increasing deployment of photovoltaic (PV) systems in hot climates highlights a major limitation: the rise in moduletemperature under solar irradiation, which reduces electrical efficiency and accelerates material degradation. Phase ChangeMaterials (PCMs) have emerged as an effective passive cooling solution by storing excess thermal energy through latent heatduring phase transition. This study numerically investigates the thermal and electrical performance of PV modules integratedwith three commercial paraffin-based PCMs (RT35HC, RT42, and RT55) under the climatic conditions of three representativeTunisian regions: Bizerte (north), Kairouan (central inland), and Gabès (south), covering the country's principal climatic zones.Validated three-dimensional transient CFD simulations were performed using realistic summer meteorological data to analyzethe temporal evolution of PV cell temperature, spatial temperature distribution, PCM melting behavior, and electricalefficiency. The results show that the cooling performance strongly depends on both the PCM melting temperature and the localclimate. Among the investigated materials, RT35HC provides the highest cooling effectiveness, reducing the maximum PVtemperature by 25.57 °C (37.58%) in Kairouan, 22.07 °C (34.80%) in Gabès, and 14.26 °C (26.75%) in Bizerte, whileimproving the electrical efficiency by up to 1.99 percentage points compared with the conventional PV module. Liquid fractionanalysis reveals that RT35HC achieves the earliest melting onset and the highest latent heat utilization, whereas RT42 exhibitsintermediate performance and RT55 remains only partially melted under the investigated operating conditions. An energybalance analysis confirms that the PCM stores a significant fraction of the absorbed thermal energy as latent heat, reducingconvective losses and stabilizing the PV temperature. These findings demonstrate that selecting an appropriate PCM meltingtemperature is essential for maximizing passive cooling performance and provide practical guidance for the design of climateadaptedPV-PCM systems.
https://doi.org/10.1088/2631-8695/ae9e82
The following article is Open access
A distributed K-means-based improved energy distance LEACH routing protocol for wireless sensor networks
Md. Yasin Arafat et al 2026 Eng. Res. Express 8 165326
View article, A distributed K-means-based improved energy distance LEACH routing protocol for wireless sensor networks PDF, A distributed K-means-based improved energy distance LEACH routing protocol for wireless sensor networks
Wireless sensor networks (WSNs) are increasingly prominent due to their applicability across diverse domains. WSNs represent the future of intelligent sensing, offering robust, flexible, and monitoring solutions to support the proliferation of the Internet of Things (IoT), in applications such as smart cities, autonomous systems, digital twins, bio-integrated sensing, and large-scale climate monitoring. Although numerous clustering-based routing protocols have been proposed, achieving energy-efficient clustering and cluster head (CH) selection remains a significant challenge. Additionally, most of the approaches are either non-adaptive or centralized. Addressing these challenges, this paper proposes a distributed K-means clustering algorithm integrated with a modified low energy adaptive clustering hierarchy-improved energy distance (DK-means-LEACH-IED) protocol for clustering and CH selection. The proposed method starts with a timer-based selection of initial centroid nodes, then applies K-means clustering to those nodes. CHs are then selected using an adaptive, weighted energy-distance function that accounts for nodes’ residual energy and their distance from the cluster centroid. The proposed protocol is implemented in OMNET++ using the Castalia framework, followed by a comprehensive performance evaluation and comparison with LEACH and the centralized Energy-driven K-means-based LEACH routing protocols. The results demonstrate that DK-means-LEACH-IED achieves improved network lifetime, energy balancing, and scalability while maintaining competitive throughput performance compared with existing protocols. The proposed protocol improves network stability during the critical depletion phase by extending the 70% node survival lifetime by up to 14.04% and achieves throughput improvement of up to 49.18% compared with the evaluated benchmark protocols, demonstrating its ability to balance energy utilization and communication efficiency for emerging IoT applications.
https://doi.org/10.1088/2631-8695/ae9676
The following article is Open access
Channel-aware feature-guided fusion for multivariate control chart pattern recognition under autocorrelation and class imbalance
Mohammed Modar et al 2026 Eng. Res. Express 8 165414
View article, Channel-aware feature-guided fusion for multivariate control chart pattern recognition under autocorrelation and class imbalance PDF, Channel-aware feature-guided fusion for multivariate control chart pattern recognition under autocorrelation and class imbalance
Control chart pattern recognition (CCPR) is an important task in statistical process control because abnormal chart patterns provide early evidence of assignable causes and support faster process diagnosis. While many machine learning and deep learning approaches have been developed for univariate CCPR, the multivariate setting remains more challenging due to cross-channel dependencies, overlapping temporal structures, autocorrelation, and class imbalance. This paper presents a channel-aware feature-guided fusion network, termed (CAFG-Net), for multivariate CCPR. The proposed model combines deep temporal representations with complementary statistical descriptors to improve discrimination among seven pattern classes: normal, cyclic, upward trend, downward trend, upward shift, downward shift, and systematic patterns. A reproducible synthetic benchmark is developed under variable-length signals, multiple autocorrelation levels, additive noise, and imbalanced class distributions, with experiments conducted for one-, three-, and five-channel settings. The results show a clear benefit from multivariate information, with balanced accuracy increasing from 0.713 in the single-channel setting to 0.952 with three channels and remaining high at 0.950 with five channels. In the five-channel case, CAFG-Net achieves an accuracy of 0.961, a macro-F1 score of 0.952, and a Matthews correlation coefficient of 0.952. These findings indicate that channel-aware and feature-guided fusion is a promising strategy for robust multivariate CCPR under synthetic monitoring conditions that better reflect practical challenges.
https://doi.org/10.1088/2631-8695/ae98f6
The following article is Open access
Recent achievements in application of magneto-rheological fluid for force-feedback and haptic systems
Le Hai Zy Zy et al 2026 Eng. Res. Express 8 163001
View article, Recent achievements in application of magneto-rheological fluid for force-feedback and haptic systems PDF, Recent achievements in application of magneto-rheological fluid for force-feedback and haptic systems
Magnetorheological fluids (MRFs) represent a class of smart materials characterized by their ability to undergo rapid, reversible changes in rheological behavior under the influence of an external magnetic field. This property, transitioning from a Newtonian liquid to a non-Newtonian semi-solid with a controllable yield stress, enables real-time force and torque modulation. Consequently, MRFs have been widely adopted across diverse engineering fields. This paper presents a systematic review focused on the application of MRF-based force-feedback systems within teleoperation, specifically for master–slave manipulators. The increasing demand for intuitive and reliable human-machine interaction in areas such as robotic surgery and hazardous environment operations necessitates force-feedback mechanisms that offer fast response, precise control, and high stability. MRF technology addresses these requirements by providing smooth, continuous, and controllable force sensations with minimal delay. While existing reviews have explored MRF applications broadly, they lack a focused analysis of the advantages and limitations specific to teleoperation. This review aims to fill that gap by first outlining the fundamental operating principles of MRF, followed by a description of MRF-based force-feedback devices. It then systematically examines existing studies on haptic systems employing MRF in teleoperation applications.
https://doi.org/10.1088/2631-8695/ae99ad
The following article is Open access
New geomagnetically induced current modeling in the Malaysian power grid
Zmnako Mohammed Khurshid et al 2026 Eng. Res. Express 8 165329
View article, New geomagnetically induced current modeling in the Malaysian power grid PDF, New geomagnetically induced current modeling in the Malaysian power grid
Geomagnetic disturbances (GMDs) caused by solar activity-related space weather events can generate geomagnetically induced current (GIC) and affect the normal operation of electrical power grid infrastructure, leading to several issues such as transformer saturation, reactive power demand increment, and relay misoperation. Since the GIC can impact power systems in lower latitudes as well as high-latitude grids, this paper presents new GIC computations on the extended Malaysian power grid. The updated grid model includes the complete 275 and 500 kV power lines provided by the grid operator. This can help to obtain more accurate GIC results in the Malaysian power system, as GIC distribution strongly depends on the network topology. In the analysis, the network was exposed to a 1 V km⁻¹ geoelectric field in different directions. Results were obtained for different system voltage levels. The vulnerability maps show that the 500 kV systems in the network are the most affected by GMD and experience higher GICs. The most intense GIC with the value of 135.8 A was obtained at substation 1 due to a 135° southeast-directed geoelectric field. This induced current can cause serious problems for transformers in the system under certain geoelectric field conditions.
https://doi.org/10.1088/2631-8695/ae9a3c

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