Solar cookers are an alternative to the traditional cooking systems; they are clean and sustainable; however, the practical use of solar cookers is still limited by the intermittency of the sun and the lack of sufficient amount of heat during the sunset. The lack of successful thermal energy storage (TES) implementation ends up in the lack of consistent cooking performance and lower reliability in domestic application. In dealing with this challenge, the current review critically reviews solar cookers that have sensible and latent heat storage materials to improve effectiveness, reliability, and off-sunshine cooking abilities. The aims of this review are to: (i) determine the performance of solar cookers that use sensible, latent and hybrid TES systems; (ii) analyse thermo-physical characteristics and applicability of widely used phase change material (PCM), e.g. acetanilide and stearic acid and binary salt mixtures; (iii) determine the effectiveness of sensible heat storage materials such as granite, sand and thermal oils; and (iv) determine critical advancements in technology, limitations and gaps in research associated with heat transfer enhancement, system integration and long-term stability. Research literature shows that the latent heat storage with the help of PCMs significantly increases the thermal retention that allows cooking several hours after dark and increases the efficiency of the entire cooker by about 20-40% in comparison with traditional solar cookers. Sensible storage of heat utilizes reliable short term heat availability of three to five hours, as compared to hybrid storage systems that combine high heat storage capabilities and predictable heat output. Overall, the combination of sensible and latent TES resources improves the work of the solar cookers and encourages the use of energy resources in the households in a more sustainable way. The research in the future ought to concentrate on high-end storage materials, cost-efficient designs, and scalable systems to enhance efficiency and acceptance by the users.
Delamination is an important failure mode of composite materials and can cause structural failure without visible signs. Early and reliable detection is crucial, especially in safety related sectors such as aerospace, automotive or wind energy. Fiber Bragg grating (FBG) sensors have gained widespread popularity for structural health monitoring (SHM), thanks to their high sensitivity, immunity to electromagnetic interference, compactness, and multiplexing ability. This paper reviews the use of FBG sensors for acoustic signal-based delamination detection. The paper categorizes sensor configurations (embedded, surface mounted, hybrid), methods for obtaining signals estimation and extracting features. Time Frequency analysis techniques, including Short Time Fourier Transform (STFT), wavelet transforms, and Fast Fourier Transfer (FFT) are discussed together with machine learning and deep leaning techniques such as support vector machines (SVM), Convolutional Neural Networks (CNNs), long short-term memory models. Their performance of detecting and categorizing delamination events is indeed critically scrutinized. Hybrid systems between FBG and acoustic emission (AE) or piezoelectric sensors are described as a result of their improved detectability. Recent advancements are also discussed including nano enhanced coatings that aid in increasing sensor sensitivity and environmental hardiness. Environmental effects, signal loss and sensor location problems and cost of the system are considered. The review wraps up with discussion on the significant limitations that need to be addressed and suggestions for future directions, such as adaptive signal processing, integration of AI and field ready interrogation units. The extensive evaluation presented here establishes the ground for further development of FBG based SHM methods into real time delamination inspection tools for composite structures.
Coastal zones in Southeast Asia face escalating threats from climate change, necessitating robust monitoring techniques for effective management; however, a fragmented understanding of the methodologies used over the past decade persists. This study addresses this gap by conducting a systematic literature review, guided by the PRISMA (Preferred Reporting Items for Systematic review and Meta analysis), to comprehensively catalogue and analyse the techniques and methodologies for mapping coastal edge and zone dynamics in Southeast Asia from 2014 to 2024. The review was driven by the central research question: “What are the most commonly used techniques and methodologies for mapping coastal edge in Southeast Asia over the past decade?” Following a rigorous screening and quality appraisal process of 437 identified records, 48 high-quality articles were synthesized. The findings reveal four dominant themes: (1) the evolution and synergistic use of data sources, primarily Landsat and Sentinel constellations; (2) prevalent techniques for shoreline extraction and change analysis, notably the Digital Shoreline Analysis System (DSAS); (3) the adoption of advanced methodologies, including machine learning and integrated modeling; and (4) a synthesis of integrated workflows and a distinct geographical research bias. The study’s significance lies in providing a consolidated methodological framework for researchers and a critical evidence base for policymakers, while its primary recommendation is the urgent need to address the identified geographical research gaps in under-studied Southeast Asian nations to foster equitable and informed coastal governance.
Long waiting times refer to the prolonged length of time patients spend waiting for medical treatment. This is due to a lack of human resources or equipments such as doctors, nurses or treatment rooms. This study investigates the optimization of resources allocation in private specialist centre to address long waiting times and enhance operational efficiency. A simulation model was developed by using Discrete Event Simulation (DES) approach to represent the actual patient flow in the centre and identify system bottlenecks that contributed to the prolonged waiting times. To determine the most effective resource configuration, the DES model was optimized using the Simulation-Optimization capabilities of OptQuest in Arena software. The optimization process focused on minimizing the Total Average Waiting Time (TAWT) while adhering to operational constraints such as limited staff and equipment. The results significantly improved the Total Average Waiting Time (TAWT) by 52.74%, reduction from 234.26 minutes to 110.71 minutes. This finding highlights the effectiveness of Simulation-Optimization approach in healthcare resource planning and offers valuable insights for optimizing operations in private specialist centres as well as reducing the congestion.
Space missions require reliable and safe physiological monitoring systems, as well as capable of continuous operation, as microgravity affects cardiovascular function and heart rhythm stability. This study develops and validates an intelligent in-ear photoplethysmography (PPG) system for continuous cardiac rhythm monitoring and real-time arrhythmia detection. A total of 2,926 PPG signal segments were collected, covering both normal and arrhythmic conditions. Signals were recorded at 75 Hz and upsampled to 256 Hz, then preprocessed using a 0.56 Hz Butterworth filter, artifact detection, and segmentation into 2,560 data points (≈10 s). A hybrid ResNet–LSTM–Attention model was developed to extract morphological and temporal features, while performance was evaluated using 5-fold crossvalidation. The results demonstrated consistent and robust performance with accuracy 0.7138 ± 0.1115, AUC 0.8078 ± 0.0492, and F1-Score 0.7198 ± 0.0788, confirming the model’s capability to detect cardiac rhythm abnormalities from in-ear PPG signals. These findings highlight the potential of this technology as a continuous cardiovascular monitoring platform, suitable for integration into wearable devices and mobile health applications, as well as for use in extreme environments such as space, where conventional electrocardiography is less practical.
This study investigates the enhancement of material properties with a particular focus on the antibacterial performance of Ag-doped ZnO for potential biomedical applications. Undoped ZnO often exhibits limited antibacterial efficiency, therefore, this study investigates property enhancement through controlled Ag doping. Undoped and Ag-doped ZnO nanoparticles were synthesized via the precipitation method using silver (Ag) doping concentrations of 0.2%, 0.6%, and 1.0%. X-ray diffraction (XRD) revealed that the crystallite size decreased from 42.97 nm for undoped ZnO to to 43.18 nm, 36.39 nm, and 36.26 nm for 0.2%, 0.6%, and 1.0% Ag-doped ZnO, respectively, indicating inhibited crystal growth with increasing Ag content. Fourier-transform infrared spectroscopy (FTIR) confirmed the Zn–O and Ag–O stretching vibrations at 500–600 cm-¹, verifying the successful synthesis. Field emission scanning electron microscopy (FESEM) showed agglomerated plate-like structures in undoped ZnO, while Ag-doped samples exhibited smaller, compact, and densely clustered morphologies, demonstrating the influence of Ag incorporation on surface features. Energydispersive X-ray (EDX) analysis confirmed Zn and O as main constituents, with Ag signals detected up to 1.51 weight % in the 1%Ag-doped sample, validating successful doping. Antibacterial activity, tested using the disc diffusion method against Escherichia coli, showed an average inhibition zone of 15.67 mm for undoped ZnO, increasing to 19.33 mm at 0.2%Ag-doped ZnO, highlighting enhanced antibacterial performance. These findings provide valuable insights into the potential of Ag-doped ZnO as an advanced material for biomedical and health-related applications.
Water quality is a critical determinant of fish health and productivity in freshwater aquaculture, with parameters such as pH, dissolved oxygen (DO), temperature, and turbidity directly influencing growth, metabolism, and disease susceptibility. Traditional monitoring methods, based on manual sampling and laboratory analysis, are often slow, labor-intensive, and unable to detect rapid environmental changes. This paper presents the design and implementation of a hybrid edge–cloud integrated smart monitoring system for sustainable freshwater fishpond management. The system employs an ESP32-based edge node to collect real-time water quality data, detect threshold violations locally, and trigger immediate responses, including LED/buzzer alarms and actuator activation, while simultaneously sending email alerts via SMTP. Long-term data storage, visualization, and remote access are enabled through a cloud platform integrated with MQTT-based data publishing. Experimental evaluation demonstrated a total detection-to-action latency of 3.4 s, local alerts in 1.0 s, email notifications within 7.0 s, and cloud synchronization latency of 150 ms. Sensor measurements achieved high accuracy, with maximum deviations of only ±0.11 pH units, 0.3 mg/L DO, 0.21 NTU turbidity, and 0.2°C temperature. The system maintained 100% uptime during stable network conditions, confirming its reliability for continuous aquaculture monitoring. By combining low-latency local decision-making with cloud-based analytics, the proposed solution enhances operational efficiency, supports proactive management, and aligns with SDG 6 (Clean Water and Sanitation) and SDG 14 (Life Below Water). Future work will focus on integrating machine learning models for predictive water quality analysis and expanding the platform to other aquaculture and smart farming applications.
Weld defects represent a critical challenge in the structural reliability of submarine pressure hulls, where components operate under severe cyclic hydrostatic loading and corrosive deep sea environments. In high strength steels and titanium alloys commonly used for submarine fabrication, welding introduces microstructural heterogeneity, residual tensile stresses, and geometric discontinuities that significantly reduce fatigue life. Even small imperfections such as porosity, inclusions, lack of fusion, and weld toe cracks act as potent stress concentrators, accelerating crack initiation and early propagation. These effects are intensified in spherical and cylindrical pressure hulls, where complex three dimensional stress fields and nonstandard crack shapes limit the applicability of conventional fatigue models. Hydrostatic compression, combined with residual stress redistribution, further promotes unexpected crack growth behavior, reducing fatigue thresholds and shortening service life. Evidence from experimental studies and numerical simulations consistently reveals that defect morphology, HAZ softening, local stiffness reduction, and environmental factors jointly govern fatigue degradation in submarine welds. Moreover, early short crack growth contributes disproportionately to total fatigue damage, emphasizing the need for defect sensitive assessment methods. Furthermore, localized plastic deformation at defect tips under extreme hydrostatic pressure necessitates fracture mechanics models explicitly accounting for non idealized crack geometries. This review synthesizes current understanding of how welding induced imperfections influence submarine pressure hull fatigue performance and highlights major research gaps related to multi axial loading, crack shape evolution, and deep sea failure mechanisms. The findings support the development of improved welding practices, advanced inspection techniques, and refined fatigue life prediction frameworks tailored for submarine structural safety.
Manufacturing documentation often contributes to process inefficiencies, human error, and productivity constraints within the RF brazing workflow. This study aimed to improve manufacturing documentation at NGK Electronics Devices by replacing manual check sheets with a digital RF Brazing Checksheet System. Guided by the Lean Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) framework, the study adopted a mixedmethods approach involving observation, time tracking, and user feedback to evaluate the existing process. The main issues identified included lengthy documentation, inaccurate data entry, limited traceability, and inefficient retrieval of historical records. A digital check sheet incorporating autofill, field validation, and expiry alerts was subsequently developed and piloted over four weeks. Following implementation, form completion time decreased by more than 60%, error rates fell by over 80%, and technician workload was reduced. In addition, integrating real-time dashboards and feedback loops strengthened continuous improvement in line with Kaizen principles. The success of the system has prompted plans for broader deployment across additional product lines, including optical parts, as well as possible integration with digital work instructions (WORKI) to support fully paperless operations. The findings demonstrate that lean-driven digital transformation can modernize legacy documentation practices, improve data quality and accessibility, and enhance RF brazing throughput in manufacturing settings. This study also confirms the value of the DMAIC framework for designing, piloting, and sustaining digital interventions on the shop floor, resulting in measurable gains in accuracy, efficiency, and operator experience. Future work will evaluate cost savings, quality yield, and long-term sustainability metrics.
Magnesium alloys have emerged as essential materials in biomedical applications due to their biocompatibility and biodegradability. However, the complex geometries and specialized qualities required for sophisticated implants are beyond the reach of traditional manufacturing methods. To overcome these issues, this work explores a novel additive manufacturing (AM) technique called plasma arc-based hybrid heating (arc fusion and laser-based). By leveraging the synergistic effects of plasma and hybrid energy, it enables precise control over extreme processing temperatures, facilitating the fabrication of magnesium alloys with optimized microstructures and enhanced mechanical performance. This research systematically examines the relationship between plasma-driven thermal dynamics, grain refinement mechanisms, and resultant mechanical properties, including tensile strength and fatigue resistance. Furthermore, the study highlights the role of rapid solidification in minimizing defects such as porosity and residual stresses, which are critical factors for implant longevity. These findings underscore the transformative potential of plasma technology in advancing the production of patient-specific biomedical devices, offering a pathway to high-performance, custom-designed magnesium implants. This work not only bridges existing gaps in the additive manufacturing of bio-compatible metals but also establishes a foundation for future innovations in sustainable medical solutions.
ratio values ranging from −1.0 to −0.2. These findings confirm the effectiveness of the origami-inspired geometry in achieving auxetic properties. The simplicity and adaptability of the four-leaf clover pattern make it a promising candidate for future applications in flexible structures, biomedical devices, and protective systems.
This paper systematically reviewed and evaluated the existing literature on dimple profile modification methods and parameters to enhance surface tribology in various engineering applications. A comprehensive search was conducted using Scopus, ASME, and Mendeley databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standard. The search focused on publications from 2021 to 2024, yielding 1635 related articles, of which 66 were shortlisted for full review based on inclusion criteria. These studies were categorized into four primary themes: Dimple Profile Application, Dimple Profile Modification Techniques, Tribological Performance Enhancement, and Future Trends in Dimple Profile Development. The review highlights the critical role of dimple profiles in reducing friction, improving lubrication, and enhancing wear resistance, which are pivotal for the reliability and efficiency of mechanical systems. A variety of techniques for dimple fabrication, such as laser texturing, through-mask electrochemical machining (TMECM), and electrical discharge machining, were analyzed, showcasing their respective advantages and limitations. Furthermore, the influence of parameters such as dimple shape, size, depth, and density on tribological performance was thoroughly examined. The findings contribute to a deeper understanding of the interplay between surface topography and tribology, offering insights into optimizing dimple profiles for specific applications. The review also identifies existing challenges and outlines future research directions to advance surface engineering solutions in tribological systems.
Maximum utilization of the workspace of five-axis CNC machines is a difficult task. Maximum utilization means machining a larger part on a smaller CNC machine by using optimized setup parameters of the workpiece on the machine table. Determination of these setup parameters is a core problem of this research. Currently, these setup parameters are selected randomly and simulation is done for the verification of the complete machining without overtravel of the machine translational axes. A spindle-tilting/rotating five-axis CNC milling machine is chosen for its superior ability to machine larger parts. In this research, a method is developed for determining the feasible regions on the table, where part can be mounted. These feasible regions will give the guarantee of complete machining of a bigger part without over travel of machine translational axes. By bounding the translational axis movements determined by the postprocessing equations with the limits set by the manufacturer, a linear programming model can be developed and solved for the feasible regions for mounting the workpiece on the table. Validation of the valid setup parameters determined by the proposed method is done in virtual machining environment. Through this method, it is easy to determine whether all machining features can be completed in one setup or if multiple setups are required. It also identifies the minimum number of setups, significantly reducing the workpiece setup time.
Successive forging is a multi-stage process for tailoring ultra-high strength steel (UHSS) sheets thickness which is normally done using rolling or welding process. These two processes often have problems of long transition zone and degradation of mechanical properties. This study investigates the geometrical change and deformation at the transition zone after successive forging process. A 1.6 mm thick JSC1180YN UHSS sheet was subjected to successive forging process using a 1500-kN servo press and an automatic feeder, aiming to achieve a thickness reduction of 0.8 mm. 20 forging steps were set with a constant feed rate of 5 mm. The punch was designed with 1 mm chamfered edges to promote uniform strain distribution, while the lower die is set to be flat. The results showed that the thickness reduction to 0.8 mm was maintained within ±0.1 mm, with an increase of 0.84% in width and 8.1% in length. The geometric control was followed by an improvement in mechanical properties, where there was an increase in Vickers hardness from 415 HV to 442 HV. Tensile testing showed an increase in ultimate tensile strength of 37.5%, while a reduction in ductility was observed due to strain hardening. The tailor forged blanks maintained a high structural integrity. Successive forging can shorten the transition zone length with minimal impact on shape changes. Overall, successive forging proves to be a process that can effectively modify the geometry and mechanical performance, providing an alternative to automotive applications.
To enrich and refine the understanding of how secondary pouring intervals affect the mechanical properties of concrete with cold joints, building upon prior experimental studies examining the impact of cold joints on the mechanical properties of highly fluid C45 concrete, Abaqus software was employed to conduct finite element analysis on the splitting tensile strength and flexural strength of concrete with cold joints at multiple factor levels of secondary pouring time intervals ranging from 0.5T to 15T (where T represents the initial setting time of concrete). The results were compared with experimental data, showing consistent trends between finite element and experimental values, with errors within 10%. This demonstrates the applicability of the finite element model for analyzing the tensile and flexural properties of concrete with cold joints. Applying this model to explore the impact of cold joints on concrete mechanical properties at secondary pour intervals of 7-70d revealed that the effect diminishes and gradually stabilizes after 7 days. Compared to monolithic pouring, when secondary pouring occurred at 7d, 28d, 42d, and 56d, the splitting tensile strength decreased by 57.2%, 60.7%, 61.3%, and 61.9%, respectively, with reduction rates of 57.2%, 3.5%, 0.6%, and 0.6%. The flexural strength decreased by 59.9%, 67.6%, 68%, and 68.2%, respectively, with reduction rates of 59.9%, 7.7%, 0.4%, and 0.2%. This indicates that when the secondary pouring interval exceeds 28 days, the time interval has an extremely negligible effect on the mechanical properties of concrete with cold joints.
A Delay Tolerant Network (DTN) is designed for intermittent connectivity and long delays environments, using a store-and-forward approach to transmit data. Machine Learning in Delay Tolerant Networks (DTNs): Prediction of link availability, routing optimization and adaptation to network dynamics. Machine learning models improve the efficiency of data transfer, thus reducing latency and enhancing decision making. This improves the robustness of the communication in the applications of remote sensing and disaster recovery. In addition, machine learning has shown promising application to design intelligent routing strategies for DTNs, which can improve communication efficiency in highly dynamic environments. The data unbalance due to different message generation rates and network connectivity has a negative impact on the performance of ML-based DTN routing algorithms. This results in biased predictions and sub-optimal routing decisions. The issue of imbalanced data in machine learning based DTN routing is examined in this study using four common protocols: Epidemic, Spray and Wait, Prophet, and MaxProp. We compare different oversampling strategies with six classifiers: Random Forest, Extra Trees, XGBoost, Bagging Classifier, Decision Tree and K-Nearest Neighbors. We discuss different data pre-processing techniques, algorithmic approaches and pertinent evaluation measures. Our analysis finds the best approaches for handling data imbalance problems. Among the methods considered, SMOTENN with Extra Trees classifier consistently obtained the highest accuracy in all four procedures. The application of these tactics resulted in an impressive improvement in model performance - accuracy increased by 13.58% (from 81% to 92%) and thus improving the robustness and fairness of ML-based DTN routing protocols.
The incorporation of crumb rubber (CR) into bitumen offers significant performance and sustainability advantages, yet its widespread adoption is impeded by poor storage stability caused by thermodynamic incompatibility. To address this fundamental challenge, this study investigates the role of bitumen chemistry, specifically the balance of fractions determined via SARA analysis, and the efficacy of crumb rubber pre-treatment in creating a stable binder system. Two base bitumen with distinct chemical compositions (PG 58-28 and PG 70-22) were modified with 10% and 15% CR, comparing two distinct modification philosophies: a palliative approach using styrene-butadienestyrene (SBS) and a fundamental approach employing devulcanized CR : a conventional stabilization approach using low dosages of styrene-butadiene-styrene (SBS) (1-3%) with untreated CR, and employing pre-treated, devulcanized CR without SBS. Binder performance was thoroughly investigated using storage stability and the Multiple Stress Creep Recovery (MSCR) test. The SARA analysis revealed that the softer bitumen, possessing a higher maltene-toasphaltene ratio, provided a superior solvent medium for polymer swelling. Despite this, all untreated CRMB blends exhibited severe phase separation (ΔSP>7°C), and the addition of SBS at 1-3% dosages proved insufficient to achieve stability, failing to meet the standard specification criterion (ΔSP≤2.5°C). In contrast, pre-treatment of the CR via thermo-mechanical devulcanization resulted in inherently stable blends (ΔSP<2.5°C) across both bitumen grades, even without the addition of any stabilizing co-polymer. Furthermore, the devulcanized CRMB demonstrated superior rheological performance, achieving an “Extreme” traffic rating (Jnr3.2<0.5 kPa−1) with excellent elastic recovery. Statistical analysis confirmed that the modification strategy is the most significant factor influencing both stability and performance. The findings demonstrate that at the investigated dosages (1–3%), SBS is an inadequate palliative for stabilizing untreated CRMB, establishing CR pre-treatment via devulcanization as a more effective and fundamental engineering solution.
Zingiber zerumbet is a medicinally important rhizome, with subcritical water extraction (SWE) offering a sustainable approach to recover its bioactive compounds. However, the influence of raw material moisture and process parameters on extract quality remains unclear. This study investigates two factors which affect the pH of Z. zerumbet extracts produced via SWE; (i) its initial rhizome moisture content (MC), (ii) the extraction time. Fresh rhizomes were extracted at subcritical condition for 5 - 25 min. MC and pH were measured, and statistical analyses were performed using General Linear Model (GLM) and Multiple Linear Regression (MLR). All extracts were slightly acidic (pH 5.85 ± 0.03 – 6.24 ± 0.02). GLM showed that extraction time significantly impacted pH (F(4,9) = 55.92, p < 0.001, partial η² = 0.961). The final multivariate model explained 98.3% of the pH variance (Adjusted R2=0.973), but the MC which shown explanatory bivariate association with pH was non-significant after controlling extraction time (p=0.129). Visual observations indicated stable, homogeneous extracts, with only darkening at longer extraction times. Overall, extraction time is the dominant factor influencing acidity of Z. zerumbet extracts under SWE, while initial moisture exerts only minor and inconsistent effects. These findings underscore the importance of controlling extraction duration for process optimization and product reproducibility, with moisture monitoring serving as a secondary safeguard. Future studies should expand on other SWE parameters and predictive validation to strengthen industrial applicability.
Cold Mix Asphalt (CMA) is not only appealing in low-energy and low-emission pavement work but it tends to have poor mechanical and surface performance. The research question is the investigation of the possibility of using low-dose polyurethane (PU) modifier to improve CMA without jeopardizing skid safety. PU (6% by aggregate mass) was mixed into CM-100 emulsion and tested by laboratory tensile and direct shear tests in polyol: isocyanate ratios (1:1, 2:1), curing time (1, 3, 7, 15 days), and environmental conditioning (dry, wet). The British Pendulum Tester (ASTM E303) was used to measure field skid resistance on six patched potholes on dry and wet surfaces at early age (4 hours) and after curing (7 days). The findings demonstrate that the 1:1 ratio gave the best ultimate tensile stress at 15 days, which is a densely cross-linked yet brittle matrix, and 2:1 gave a consistently higher ductility and strain capacity. Shear behaviours have different pattern, where 2:1 variant delivered the most balanced stress-strain response, while 1:1 generally have reduced shear strength. Both PU-CMA and control CMA had a higher skid resistance than the Transport and road Research laboratory (TRRL) threshold values at all ages and anticipated decreases on wet surfaces, PU-CMA was similar to CMA in dry and wet conditions. In general, low-dose PU can be implemented in CMA to enhance mechanical strength and toughness without compromising the necessary skid resistance, which is sustainable and quick in pavement maintenance (median-to-heavy traffic) conditions.
In critical sectors such as oil and gas, more than 90% of bolted joint failures result from improper preload during installation. Loss of bolt tension can cause flange leakage, leading to environmental damage or fire hazards. This study aims to improve the performance of a modified M18 stud bolt integrated with Fiber Bragg Grating (FBG) sensors to reduce structural damage while enabling accurate real-time stress–strain monitoring. Finite Element Analysis (FEA) was conducted using ANSYS. Five bolt designs were evaluated under an applied torque of 70 Nm. The models varied in groove position to accommodate FBG sensor placement. Stress and strain responses were analyzed using a mesh size of 0.15 mm. The top groove extending to the flange end produced 2877.90 µε strain but generated a maximum von mises stress of 582 MPa, approaching nearly the 640 MPa yield strength of Grade 8.8 steel. The centre groove extending to the flange end exceeded both 3000 µε strain and the yield limit, indicating structural instability. In contrast, the top groove extending to the bolt end achieved 1639.5 µε strain while maintaining a lower maximum stress of 323.04 MPa, well within the elastic range. Based on mechanical response and manufacturability considerations, the top groove till end configuration provides the most consistent balance between strain response and structural safety. The numerical results demonstrate that controlled groove placement enables strain sensing while preserving elastic behavior in bolted flange systems. The simulation confirms that this approach improves the safety of bolted flange systems and enables reliable real-time monitoring.
