• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.8
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• pp.80-85
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• 2021

This study is to evaluate the structural stability of heavy duty structure of the Complex CNC automatic lathe. The analysis conditions were analyzed by applying the weight and load of the part itself and then applying the weight of the upper assembly unit. As a result of the structural analysis, the values of stress and strain are small and safety factor is high, and as a result of the dynamic analysis, there will be no resonance outside the equipment driving area, so there will be no problem in equipment stability.

• Korean Journal of Metals and Materials
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• v.46 no.1
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• pp.1-5
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• 2008

This study has been investigated the deformation behavior of a hot-extruded Mg-Zn-RE (RE: rare earth elements) alloy containing $Mg_{12}$(RE) particles at the elevated temperatures. The particles are intrinsically produced by breaking the eutectic structure of the alloy during the hot-extrusion process. The grain size of the extruded Mg-Zn-RE alloy developed via dynamic recrystallization is around $10{\mu}m$. Under the heat treatment at 200o C up to 48 hr, no change has been observed on the microstructure and mechanical properties due to the pinning effect of the thermally stable particles. Under the tensile test condition in the initial strain-rate range of $1\times10^{-3}s^{-1}$ and the temperature range up to $200^{\circ}C$, the alloy shows yield strength of 270 MPa and elongation to failure around 9% at room temperature and yield strength of 135 MPa at $200^{\circ}C$. Furthermore, although the alloy contains large amount of the second phase particles around 15%, it shows excellent hot-workability possibly due to the presence of the thermally stable interface between the particles and the matrix.

• Steel and Composite Structures
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• v.38 no.2
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• pp.165-176
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• 2021

Existing research on confined concrete filled steel tubular (CCFT) columns has been mainly focused on static or cyclic loading. In this paper, square section CCFT and CFT columns were tested under both static and impact loading, using a 10,000 kN capacity compression test machine and a drop weight testing equipment. Research parameters included bonded and unbonded fiber reinforced polymer (FRP) wraps, with carbon, basalt and glass FRPs (or CFRP, BFRP, and GFRP), respectively. Time history curves for impact force and steel strain observed are discussed in detail. Experimental results show that the failure modes of specimens under impact testing were characterized by local buckling of the steel tube and cracking at the corners, for both CCFT and CFT columns, similar to those under static loading. For both static and impact loading, the FRP wraps could improve the behavior and increase the loading capacity. To analyze the dynamic behavior of the composite columns, a finite element, FE, model was established in LS-DYNA. A simplified method that is compared favorably with test results is also proposed to predict the impact load capacity of square CCFT columns.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.7
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• pp.465-470
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• 2022

This paper studied the high-velocity impact behavior characteristics of metal materials by crosschecking the high-velocity impact analysis with the high-velocity impact experiment results of aluminul 6061. The coefficients of the Huh-Kang material model and the Johnson-Cook fracture model were calculated through quasi-static using MTS-810 and dynamic experimenting using the Hopkinson bar equipment for high-velocity impact analysis. The penetration velocity and shape were predicted through high-velocity impact analysis using the LS-DYNA. The resultes were compared with the experiment results using a high-velocit experiment equipment. It is intended to be used the containment evaluation research for aircraft gas turbine engine blade.

• Textile Coloration and Finishing
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• v.34 no.2
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• pp.127-133
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• 2022

With the increasing interest in the expression of individuality and appearance of modern people, it is time to conduct research and development of novel hair coloring from various angles. Therefore, taking into account the order of discoloration of hair pigments, we selected a creative and novel emulsion as a novel material for hair coloring, rather than a cosmetic material such as hot water extract using natural products dealt with in previous studies, commercially available hair manicure, and oxidation hair dye for hair. Thus, the change in tensile strength and elongation of hair samples by color was studied. As a result of the study, hair with green emulsion paint had a significantly higher maximum load, maximum stress, maximum elongation and breaking load, breaking stress, breaking elongation values are shown. Maximum in terms of modulus, green emulsion applied hair and the control group were higher in the 0-15s strain and 15-145s sections, respectively, and the tangential modulus value was much higher in the control group than the experimental group hairs in all the 0-145s sections. This study, which analyzes the dynamic changes of hair samples that extend the daily color gamut, will greatly contribute to the development of innovative hair coloring materials in the research and production of hair beauty works, and it is judged that it will also contribute to the development of the beauty industry.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.326-342
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• 2022

Comparative analyses of a shield building subjected to a large commercial aircraft impact between decoupling method and coupling method are performed in this paper. The decoupling method is applying impact force time-history curves on impact area of the shield building to study impact damage effects on structure. The coupling method is using a model including aircraft and shield building to perform simulation of the entire impact process. Impact force time-history curves of the fuselage, wing and engine and their total impact force time-history curve are obtained by the entire aircraft normally impacting the rigid wall. Taking aircraft structure and impact progress into account some loading areas are determined to perform some comparative analyses between decoupling method and coupling method, the calculation results including displacement, plastic strain of concrete and stress of steel plate in impact area are given. If the loading area is determined unreasonably, it will be difficult to assess impact damage of impact area even though the accurate impact force of each part of aircraft obtained already. The coupling method presented at last in this paper can more reasonably evaluate the dynamic response of the shield building than the decoupling methods used in the current nuclear engineering design.

• Smart Structures and Systems
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• v.30 no.3
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

• Structural Monitoring and Maintenance
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• v.8 no.4
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• Earthquakes and Structures
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• v.23 no.1
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• pp.101-113
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• 2022

A new type of fabricated subway station construction technology can effectively solve these problems. For a new type of metro structure form, it is necessary to clarify its mechanical properties, especially the seismic performance. A soil-structure elastoplastic finite element model is established to perform three-dimensional nonlinear dynamic time-history analysis based on the first fabricated station structure-Yuanjiadian station of Changchun Metro Line 2, China. Firstly, the nonlinear seismic response characteristics of the fabricated and cast-in-place subway stations under different seismic wave excitations are compared and analyzed. Then, a comprehensive analysis of several important parameters that may affect the seismic response of fabricated subway stations is given. The results show that the maximum plastic strain, the interlayer deformation, and the internal force of fabricated station structures are smaller than that of cast-in-place structure, which indicates that the fabricated station structure has good deformation coordination capability and mechanical properties. The seismic responses of fabricated stations were mainly affected by the soil-structure stiffness ratio, the soil inertia effect, and earthquake load conditions rarely mentioned in cast-in-place stations. The critical parameters have little effect on the interlayer deformation but significantly affect the joints' opening distance and contact stress, which can be used as the evaluation index of the seismic performance of fabricated station structures. The presented results can better understand the seismic responses and guide the seismic design of the fabricated station.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.814-822
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• 2022

The deployment of sensors for Structural Health Monitoring requires a complicated network arrangement, ground truthing, and calibration for validating sensor performance periodically. Any conventional sensor on a structural element is also subjected to static and dynamic vertical loadings in conjunction with other environmental factors, such as brightness, noise, temperature, and humidity. A structural model with strain gauges was built and tested to get realistic sensory information. This paper investigates different deep learning architectures and algorithms, including unsupervised, autoencoder, and supervised methods, to benchmark blind drift calibration methods using deep learning. It involves a fully connected neural network (FCNN), a long short-term memory (LSTM), and a gated recurrent unit (GRU) to address the blind drift calibration problem (i.e., performing calibrations of installed sensors when ground truth is not available). The results show that the supervised methods perform much better than unsupervised methods, such as an autoencoder, when ground truths are available. Furthermore, taking advantage of time-series information, the GRU model generates the most precise predictions to remove the drift overall.