• Geomechanics and Engineering
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• v.33 no.1
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• pp.11-18
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• 2023

This study explores a new energy partitioning approach to determine the fracture toughness of 3-D printed pristine/recycled high density polyethylene (HDPE) blends employing the essential work of fracture (EWF) concept. The traditional EWF approach conducts a uniaxial tensile test with double-edge notched tensile (DENT) specimens and measures the total energy defined by the area under a load-displacement curve until failure. The approach assumes that the entire total energy contributes to the fracture process only. This assumption is generally true for extruded polymers that fracture occurs in a material body. In contrast to the traditional extrusion manufacturing process, the current 3-D printing technique employs fused deposition modeling (FDM) that produces layer-by-layer structured specimens. This type of specimen tends to include separation energy even after the complete failure of specimens when the fracture test is conducted. The separation is not relevant to the fracture process, and the raw experimental data are likely to possess random variation or noise during fracture testing. Therefore, the current EWF approach may not be suitable for the fracture characterization of 3-D printed specimens. This paper proposed a new energy partitioning approach to exclude the irrelevant energy of the specimens caused by their intrinsic structural issues. The approach determined the energy partitioning location based on experimental data and observations. Results prove that the new approach provided more consistent results with a higher coefficient of correlation.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.4
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• pp.11-22
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• 2023

In this study, a structural concrete square beam was developed using the centrifugal molding technique. In order to secure the bending stiffness of the cross section, the hollow rate of the cross section was set to 10% or less. Instead of using the current poor mixture of concrete, a special formwork for producing a centrifugal square beam was manufactured, and a concrete mixing ratio with a high slump (150-200) and a design strength of 100 MPa or more was developed and applied. The produced centrifugally formed rectangular beams were subjected to performance tests according to the standard bending and shear test standards for centrifugally formed members. The static load test results for the four specimens exceeded both the nominal bending strength and nominal shear strength, which are design values through structural design, proving the structural reliability of the ultra-high-strength centrifugally formed square beam.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.56 no.4
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• pp.512-519
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• 2023

Pacific oysters Crassostrea gigas are a popular shellfish in the Republic of Korea. However, due to their abundant moisture and nutrient content, oysters are susceptible to microbiological growth and biochemical changes, which lead to quality degradation. Therefore, the present study aimed to investigate the effectiveness of modified atmosphere packaging (MAP) in maintaining the quality of raw oysters during storage. Microbiological and physicochemical parameters such as pH, glycogen content, soluble protein, turbidity, and volatile basic nitrogen (VBN) were analyzed for oysters stored under various gas compositions and storage periods. The results showed that there was no significant increase in viable cell count in MAP oysters after six days in MAP oysters. Moreover, the physicochemical quality of non-MAP oysters deteriorated rapidly, whereas the quality of MAP oysters were maintained during storage. This study suggests that MAP can be an effective technique for maintaining the freshness of raw oysters during distribution and storage, and may also be useful for extending the shelf-life and maintaining the quality of other seafood products.

• Journal of the Korean Society of Propulsion Engineers
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• v.26 no.5
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• pp.44-51
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• 2022

In this study, 5-axis machining was proposed as a method for manufacturing a nozzle with a curved shape, and flow analysis and structural analysis were used for structural validation of the manufactured geometry. The program used for CFD obtained the internal temperature and pressure distribution of the nozzle using STAR-CCM+ and used it as the boundary condition for structural analysis. For structural analysis, the commercial program NASTRAN was used, and stress was calculated using the von-mises technique. Based on the maximum stress value generated, the safety margin was 0.78 and the safety margin of the bearing stress was 46.8. In addition, the creep life was calculated as 9.97 x 1012 hours using the Larson-Miller parametric method and applying the maximum stress value of 187 MPa and the exhaust gas perfectly mixed temperature of 463 K.

• Composites Research
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• v.19 no.1
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• pp.22-28
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• 2006

In manufacturing process of composite cylinders with metal liner, the autofrettage process which induces compressive residual stress on the liner to improve cycling life can be applied. In this study, a finite element analysis technique is presented, which can predict accurately the compressive residual stress on the liner induced by autofrettage and stress behavior after. Material and geometrical non-linearity is considered in the finite element analysis, and the Von-Mises stress of a liner is introduced as a key parameter that determines pressure cycling life of composite cylinders. Presented methodology is verified through fatigue test of liner material and pressure cycling test of composite cylinders.

• The Journal of Bigdata
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• v.7 no.1
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• pp.99-111
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• 2022

A simulation technique can be used to analyze performance measures and support decision makings in manufacturing systems considering operational uncertainty and complexity. The simulation requires an input modeling procedure to reflect the target system's characteristics. However, data collection to build a simulation is quite limited when a target system includes manual productions with a lot of operational time such as construction equipment assembly lines. This study proposes a procedure for simulation input modeling using video data when it is difficult to collect enough input data to fit a probability distribution. We conducted a video-data analysis and specify input distributions for the simulation. Based on the proposed procedure, simulation experiments were conducted to evaluate key performance measures of the target system. We also expect that the proposed procedure may help simulation-based decision makings when obtaining input data for a simulation modeling is quite challenging.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.139-147
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• 2023

The metal bush assembling process is a process of inserting and compressing a metal bush that serves to reduce the occurrence of noise and stable compression in the rotating section. In the metal bush assembly process, the head diameter defect and placement defect of the metal bush occur due to metal bush omission, non-pressing, and poor press-fitting. Among these causes of defects, it is intended to prevent defects due to omission of the metal bush by using signals from sensors attached to the facility. In particular, a metal bush omission is predicted through various data mining techniques using left load cell value, right load cell value, current, and voltage as independent variables. In the case of metal bush omission defect, it is difficult to get defect data, resulting in data imbalance. Data imbalance refers to a case where there is a large difference in the number of data belonging to each class, which can be a problem when performing classification prediction. In order to solve the problem caused by data imbalance, oversampling and composite sampling techniques were applied in this study. In addition, simulated annealing was applied for optimization of parameters related to sampling and hyper-parameters of data mining techniques used for bush omission prediction. In this study, the metal bush omission was predicted using the actual data of M manufacturing company, and the classification performance was examined. All applied techniques showed excellent results, and in particular, the proposed methods, the method of mixing Random Forest and SA, and the method of mixing MLP and SA, showed better results.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.369-377
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• 2023

In the industrial manufacturing sector, quality control is pivotal for minimizing defect rates; inadequate management can result in additional costs and production delays. This study underscores the significance of detecting texture defects in manufactured goods and proposes a more precise defect detection technique. While the DFR(Deep Feature Reconstruction) model adopted an approach based on feature map amalgamation and reconstruction, it had inherent limitations. Consequently, we incorporated a new loss function using statistical methodologies, integrated a skip connection structure, and conducted parameter tuning to overcome constraints. When this enhanced model was applied to the texture category of the MVTec-AD dataset, it recorded a 2.3% higher Defect Segmentation AUC compared to previous methods, and the overall defect detection performance was improved. These findings attest to the significant contribution of the proposed method in defect detection through the reconstruction of feature map combinations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.348-369
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• 2024

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.

• Advances in materials Research
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• v.13 no.4
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• pp.299-319
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• 2024

Due to higher strength-to-weight ratio of composite laminates, they find uses in many weight-sensitive applications like aerospace, automobile and marine structures. From a reliability point of view, accurate prediction of failure of these structures is important. Due to the complexities in the manufacturing processes of composite laminates, there is a variation in the material properties and geometric parameters. Hence stochastic aspects are important while designing the composite laminates. Many existing works of composite laminate failure analysis are based on the deterministic approach but it is important to consider the randomness in the material properties, geometry and loading to predict accurate failure loads. In this paper the statistics of the ultimate failure load of the [0/θ]_s laminated composite plate (LCP) containing the edge crack and voids subjected to the tensile loading are presented in terms of the mean and coefficient of variance (COV). The objective is to better the efficacy of laminate failure by predicting the statistics of the ultimate failure load of LCP with random material, geometric and loading parameters. The stochastic analysis is done by using the extended finite element method (XFEM) combined with the second-order perturbation technique (SOPT). The ultimate failure load of the LCP is obtained by ply-by-ply failure analysis using the ply discount method combined with the Tsai-Wu failure criterion. The aim is to know the effect of the stacking sequence, crack length, crack angle, location of voids and number of voids on the mean and corresponding COV of the ultimate failure load of LCP is investigated. The results of the ultimate failure load obtained by the present method are in good agreement with the existing experimental and numerical results. It is observed that [0/θ]_s LCPs are very sensitive to the randomness in the crack length, applied load, transverse tensile strength of the laminate and modulus of elasticity of the material, so precise control of these parameters is important. The novelty of the present study is, the stochastic implementation in XFEM for the failure prediction of LCPs containing crack and voids.