This study aims to diagnose the early-stage degradation of 18650 lithium-ion batteries by conducting 20 constant-current/constant-voltage (CC-CV) charge-discharge cycles on both new (N.C.) and used (U.C.) cells. During the charge-discharge processes, strain was measured by attaching a strain gauge to the central region of the cell casing, while the voltage response was simultaneously monitored. The N.C. exhibited nearly constant strain behavior and reversible elastic deformation throughout all cycles. In contrast, the U.C. showed an increase in maximum strain, accumulation of residual strain, and pronounced nonlinear hysteresis, confirming the progression of internal structural degradation. Although both cells maintained similar voltage ranges under protection circuit module (PCM) control and appeared to show no apparent performance degradation, the strain response of the U.C. clearly revealed early degradation signals. These findings suggest that casing strain-based measurements provide an effective and non-destructive approach for diagnosing internal damage in lithium-ion batteries that is difficult to identify using electrical signals alone.
Prior-art search in patent examination is challenging due to the mismatch between IPC (text-based) and Locarno (visual/functional) taxonomies. This study establishes a corpus by matching Korean patent/ utility-model documents with Locarno codes. We propose a multimodal classifier that integrates IPC, text, and images. The text branch combines KorPatBERT embeddings with embeddings of the IPC hierarchy (section/class/subclass). The image branch fuses AlexNet features with local binary pattern and adaptive hierarchical density histogram descriptors. By comparing text+IPC, image+IPC, and text+image+IPC settings, we demonstrate that the fusion model consistently outperforms unimodal baselines. IPC injection and visual cues improve discrimination for sparse or noisy claims and for visually similar Locarno classes.
This study presents a fault diagnosis method utilizing deep learning-based vibration signal analysis to ensure the safety of electric vehicles equipped with in-wheel motors (IWMs). IWMs are susceptible to road-induced impacts, which can degrade power performance and compromise vehicle safety. Consequently, three-axis accelerometers were installed on the vehicle's upper arm, brake caliper, and mounting bolts to collect vibration data under normal-road and speed-bump driving conditions. The normal condition was defined as stable operation without abnormalities, while the fault condition included electrical and mechanical faults such as inverter damage and W-phase short circuits. The acquired vibration signals were processed using a bandpass filter and converted into log-Mel spectrograms, which were then classified using a Convolutional Neural Network (CNN) model with transfer learning. Experimental results from real vehicle tests demonstrated an average classification accuracy of 98.80% for four driving condition classes: normal-road, normal-bump, fault-road, and fault-bump. These results confirm the applicability of deep learning-based vibration analysis for IWM fault diagnosis.
In this paper, we propose an inverse method using proper orthogonal decomposition (POD) to obtain the boundary loads of elastic bodies from measured displacements. Reduced POD vectors are constructed from snapshot displacements for various training loads, and an inverse formulation is developed to estimate the boundary loads from measured displacements. Numerical results show that the error in the estimated loads decreases as the measurement location approaches the loading location. In addition, the error tends to steadily decrease when the number of POD modes increases. The proposed inverse method can stably estimate boundary loads from measured displacements, and can be used to estimate unknown loads and evaluate residual stresses in structures.
Automatic balancing control is a technique to reduce synchronous control current due to mass unbalance using active magnetic bearings (AMBs). It uses a synchronous notch filter in addition to a levitation control, with the filter frequency equal to the rotor speed. An alternative to a separate speed sensor is to use a speed estimator utilizing the synchronous component in vibration signals. To overcome the complexity of previous efforts on speed estimation where two orthogonal vibration signals are needed, we proposed a simple estimator based on phase-locked loop (PLL) applied to a single vibration signal. The loop filter in PLL is designed to obtain the target performance of the estimation. The effects of spurious signals having the frequency twice of the input signal is also investigated. The speed estimator is implemented on a test rig, demonstrating an estimation accuracy of 96.5%. The synchronous notch filter using the estimated speed is added to the levitation control, resulting in more than 90% reduction in synchronous control currents.
As global efforts toward carbon neutrality accelerate, nuclear-powered ships are emerging as a next-generation eco-friendly maritime technology. However, marine nuclear systems differ from land-based reactors in that they are subject to both the Maritime Safety Act and the Nuclear Safety Act. This dual regulatory structure requires additional safety measures that reflect the unique operational conditions of the marine environment. This study compares and analyzes domestic and international safety regulations in the maritime and nuclear fields to identify the specific characteristics and safety requirements of marine nuclear systems. Based on these findings, the study proposes a set of safety design guidelines that can be applied to reactor design and licensing processes. These guidelines aim to support the safe implementation of marine nuclear technology under complex regulatory conditions.
Glass fiber reinforced plastic (GFRP) composite leaf springs have been widely investigated as lightweight alternatives to conventional steel leaf springs to improve fuel efficiency and meet stringent environmental regulations. However, GFRP leaf springs are directly exposed to impact loads from road debris during service, which can cause severe internal damage such as delamination and debonding. Therefore, quantitative evaluation of impact damage and the establishment of damage-tolerance criteria are essential. In this study, impact tests considering temperature variations were conducted to determine the impact-damage tolerance of GFRP composite leaf springs. The impact tests conducted at room temperature under impact energies of 80, 100, and 120 J showed that 120 J satisfied the visible impact damage criterion as the reference impact energy. This energy was applied to both low-and high-temperature tests, resulting in a 31.9% increase in the maximum load at low temperature compared with room temperature. The experimental results were further validated using explicit finite element analysis with LS-DYNA, showing similar impact behavior with less than 2% error in the maximum load.
In this paper, a hybrid multiscale analysis technique is proposed by combining the direct FE2 method and homogenization techniques to accurately and efficiently analyze the nonlinear behavior of composite materials. First, a homogenized analysis is performed to determine the overall behavior of structures and equivalent plastic strain distributions. The direct FE2 method is used only for the plastic region, and the other region is analyzed by the homogenization method to reduce computational costs. Various load conditions are applied to a single RVE to obtain the equivalent yield strength that is used to determine the region for the direct FE2 in the hybrid multiscale analysis. The proposed method is applied to cantilever beam and S-beam problems, demonstrating that the use of the direct FE2 in the plastic region enhances the accuracy of the results, while using a homogenized analysis in the elastic region significantly reduces computation time.
This study develops a thermal-structural coupled safety evaluation system for a novel innovative small modular reactor (i-SMR) based on finite element analysis. The proposed system integrates parametric modeling with an automated analysis workflow, effectively addressing the issues of repetitive modeling and low computational efficiency during the early design stage. A three-dimensional parametric finite element model of the helical heat-transfer tube in a once-through steam generator (OTSG) is established, and multiphysics thermal-structural coupling analyses are conducted over a temperature range of 210 similar to 321 degrees C. The results demonstrate that the heat-transfer tube exhibits satisfactory structural integrity, with the maximum thermal stress remaining below the allowable limit of Inconel 690, and the deformation satisfying design criteria. Stress linearization analysis further confirms that the structural safety margins exceed code-specified limits. The proposed system enables efficient evaluation of the effects of geometric and material parameters on structural performance, providing reliable technical support for multi-objective optimization of heat-transfer tube design and significantly improving the development efficiency of i-SMR engineering applications.
This study conducted experiments and analyses to identify the causes of failure in motorcycle suspension coil springs. The coating layer and metal surface adjacent to the spring fracture were damaged due to contact and corrosion. Furthermore, the breakage locations on each spring were similar. As a result of examining the fracture surface and surrounding area using SEM, no unusual features were observed other than the deformation and corrosion damage caused by contact at the crack initiation site. No microstructural factors directly contributing to the failure were detected, and the chemical analysis results conformed to specifications. In the micro-Vickers hardness test, no abnormal forming process factors, other than the work hardening caused by spring operation, were observed. Based on qualitative and quantitative analyses, the spring failure is attributed to repeated contact under severe operating conditions.
This study evaluates the structural integrity of a hypersonic combustor sector rig through thermal-structural coupled analysis. Because hypersonic engines operate under extreme temperatures and temperature gradients, assessing the structural integrity based on the internal thermal environment in ground environments before designing an actual aircraft is essential. To achieve this, thermal-structural coupled analysis was conducted following a simulation of the thermal test environment using finite element analysis. The von-Mises stress was confirmed to have exceeded the yield strength at the same location as the weld on the lower plate, where failure occurred in the actual ground test environment. A reinforcement design model for the combustor sector rig was then proposed, and an analysis was performed using the same methodology. The results indicated that the von-Mises stress was within the yield strength, thus confirming the structural integrity of the reinforcement design model.
Continuous carbon fibre reinforced plastics (CFRP) are attractive lightweight materials for automotive applications due to their high specific strength and stiffness. Recent advances in additive manufacturing enable improved structural performance through controlled continuous fibre path design. This study proposes an integrated design methodology combining ABAQUS-Python coupled topology optimization with G-code based continuous fibre path reconstruction to reduce the weight of a control lower arm. A cyclic multi load topology optimization framework was developed considering four representative loading conditions: pothole braking, reverse braking, outer cornering, and inner cornering. The method of moving asymptotes was employed to obtain the optimal material layout. Fibre paths were extracted from manufacturing G-code and reverse modelled into an ABAQUS finite element model using the embedded element method to represent interactions between the PETG matrix and carbon fibres. The results demonstrate feasibility for practical automotive structural applications.
This study investigates the structural failure mechanism of a lifting lug, fractured during actual industrial operations, through strength calculations and finite element analysis (FEA). Strength evaluation based on ASME BTH-1 and AISC standards revealed that the yield strength was exceeded at both the side and upper regions of the lug hole, with particularly high local stresses at the hole's side surface. FEA results showed that the highest stress occurred at the contact interface between the hole and the pin; however, as this stress was below the tensile strength, fatigue failure was considered the more likely cause. Fatigue analysis indicated a predicted service life of approximately 37 cycles, far below the target life. These findings suggest that the failure was primarily because of the loss of durability from repeated lifting, and not sudden overloads.
Double-walled vacuum insulated storage vessels incorporate a vacuum layer between the outer and inner vessels to provide thermal insulation, and support structures of minimal cross-sectional area to limit heat penetration and suppress boil-off gas generation within the inner vessel. However, sudden braking during vehicle transportation can introduce external dynamic loads that induce sloshing of the free surface of the cryogenic fluid, potentially damaging the internal support structures. In this study, the sloshing behavior of liquid nitrogen at a 50% filling ratio within a double-walled vacuum insulated storage vessel was analyzed under dynamic loading conditions, simulating sudden braking. The influence of baffles installed inside the inner vessel on sloshing reduction was evaluated, with a case study investigating the effects of baffle's location and height on the structural integrity of the support structures.
In this study, we optimized an end-of-line (EOL) testing protocol to detect vibration by analyzing the noise and vibration characteristics of hypoid gears in the transfer case of an on-demand all-wheel drive (AWD) vehicle, using data from real-world driving tests. Noise, vibration, and torque were measured under various driving conditions. The EOL inspection conditions were designed based on the actual torque levels observed within the speed range in which gear whining noise was prominent. When these torque conditions were applied to the EOL vibration test, the results showed a higher correlation with vehicle noise, vibration, and harshness (NVH) characteristics than did conventional inspection settings. These findings demonstrate that the reliability of the inspection process was improved.
The elastic-compensation method determines the limit load by simulating the inelastic behavior through iterative adjustment of the elastic modulus. Several procedures for adjusting the elastic modulus and estimating the limit load have been proposed; however, validation has been performed only for specific geometries. In this study, the applicability of the elastic-compensation method is analyzed by comparing results calculated based on different elastic-modulus adjustment and limit-load estimation procedures for pressure-vessel and elbow geometries. Adjusting the elastic modulus for all elements yields inaccurate results for geometries with significant differences in terms of equivalent stress, whereas adjusting only elements with high equivalent stresses results in limit loads similar to those obtained from limit analysis. The method using the maximum limit load calculated from all iterations yields the accurate limit load.
Real-time monitoring and adaptive control are essential for suppressing abnormal phenomena during machining to maintain product quality. Traditional machine tools rely on external sensors for chatter detection and process monitoring, which increases system complexity and cost. This study proposes an EtherCAT-based machine tool system that utilizes spindle current signals for real-time chatter detection and adaptive control without additional sensors. A suitable current feature was selected through experiments, enabling effective chatter detection. Upon detecting chatter, the spindle speed was adjusted to a stable region to suppress vibration. Additionally, machining load was estimated from the current, and the feed rate was adaptively controlled to maintain constant load conditions. This approach successfully reduced overall machining time and improved productivity. The proposed method demonstrates that internal current data combined with EtherCAT communication can provide a cost-effective and practical solution for intelligent machining systems requiring real-time monitoring and control.
In this study, we propose a friction modeling technique to enhance the accuracy of digital twin simulations for robotic manipulators based on multibody dynamics (MBD). We first analyzed the joint dynamics precisely using RecurDyn and established a real-time digital twin environment on the Unity metaverse platform. Next, we applied sparse identification of nonlinear dynamics (SINDy) to identify and learn friction torque from experimental measurements and integrated these values into the simulation to correct the dynamic behavior. The results of an experimental evaluation of the proposed method showed a significant reduction in torque errors between the simulation and the measurements along with improved trajectory tracking and control stability. By combining high-fidelity MBD analysis with ametaverse-based digital twin, the proposed approach narrows the sim-to-real gap and broadens the practical applicability of robot controllers based on digital twins. The experimental videos are available at https://jjung-yun.github.io/sim2real.
As artificial-intelligence-driven condition-based maintenance technologies for rotating machinery continue to evolve globally, significant challenges persist in obtaining reliable training datasets for machine learning (ML), primarily due to the limited availability of real fault data. To solve this problem, physics-informed computational modeling has emerged as a promising approach for fault data generation. In this study, a flexible multibody dynamics (MBD) simulation model was developed to generate time-series vibration data caused by unbalanced faults in rotating machinery. The generated fault data were compared with and validated against established theoretical and experimental results, demonstrating high physical accuracy and fidelity across various time-series characteristics. These results confirm that the proposed flexible MBD model can accurately simulate the vibrational behavior and dynamic characteristics of unbalanced faults. Furthermore, the study demonstrates the feasibility and applicability of the flexible MBD approach as a tool for generating ML training datasets related to fault phenomena in rotating machinery.
In this study, the effects of thermal aging on the material strength, ductility, and fracture behavior of Mod.9Cr-1Mo (ASME Grade 91) and 9Cr-2W (ASME Grade 92) steels were investigated through tensile and J-R tests. Specimens of aged Gr.91 (73,716 h) and Gr.92 (88,046 h) steels sampled from the reheat steam piping system of a supercritical plant in Korea were used for tensile tests. The test data of service-exposed material specimens were compared with the test results of the virgin Gr.91 steel specimens. In addition, the material properties provided in the elevated temperature design (ETD) rules were compared and analyzed. The material properties of the ETD rules were confirmed to be nonconservative values; hence, additional testing was deemed necessary. A series of J-R tests, which are not codified in the ETD rules, were performed on virgin and aged ferritic-martensitic steels. The fracture toughness properties were determined, and effects of thermal aging on the fracture toughness were investigated.
