• Advances in materials Research
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• v.11 no.4
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• pp.375-390
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• 2022

Carbon Fiber Reinforced Polymer (CFRP) composite provides outstanding mechanical capabilities and is therefore popular in the automotive and aerospace industries. Drilling is a common final production technique for composite laminates however, drilling high-strength composite laminates is extremely complex and challenging. The delamination of composites during the drilling at the entry and exit of the hole has a severe impact on the results of the holes surface and the material properties. The major goal of this research is to investigate contemporary industry solutions for drilling CFRP composites: enhanced edge geometries of cutting tools. This study examined the occurrence of delamination at the entry and exit of the hole during the drilling. For each of the 80°, 90°, and 118°point angle uncoated Brad point, Dagger, and Twist solid carbide drills, Taguchi design of experiments were undertaken. Cutting parameters included three variable cutting speeds (100-125-150 m/min) and feed rates (0.1-0.2-0.3 mm/rev). Brad point drills induced less delamination than dagger and twist drills, according to the research, and the best cutting parameters were found to be a combination of maximum cutting speed, minimum feed rate, and low drill point angle (V:150 m/min, f: 0.1 mm/rev, θ: 80°). The feed rate was determined to be the most efficient factor in preventing hole entry and exit delamination using analysis of variance (ANOVA). Regression analysis was used to create first-degree mathematical models for each cutting tool's entrance and exit delamination components. The results of optimization, mathematical modelling, and experimental tests are thought to be reasonably coherent based on the information obtained.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.233-239
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• 2022

The injection molding process is a process in which thermoplastic resin is heated and made into a fluid state, injected under pressure into the cavity of a mold, and then cooled in the mold to produce a product identical to the shape of the cavity of the mold. It is a process that enables mass production and complex shapes, and various factors such as resin temperature, mold temperature, injection speed, and pressure affect product quality. In the data collected at the manufacturing site, there is a lot of data related to good products, but there is little data related to defective products, resulting in serious data imbalance. In order to efficiently solve this data imbalance, undersampling, oversampling, and composite sampling are usally applied. In this study, oversampling techniques such as random oversampling (ROS), minority class oversampling (SMOTE), ADASYN(Adaptive Synthetic Sampling), etc., which amplify data of the minority class by the majority class, and complex sampling using both undersampling and oversampling, are applied. For composite sampling, SMOTE+ENN and SMOTE+Tomek were used. Artificial neural network techniques is used to predict product quality. Especially, MLP and RNN are applied as artificial neural network techniques, and optimization of various parameters for MLP and RNN is required. In this study, we proposed an SA technique that optimizes the choice of the sampling method, the ratio of minority classes for sampling method, the batch size and the number of hidden layer units for parameters of MLP and RNN. The existing sampling methods and the proposed SA method were compared using accuracy, precision, recall, and F1 Score to prove the superiority of the proposed method.

• Journal of Powder Materials
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• v.29 no.6
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• pp.459-467
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• 2022

Soft magnetic powder materials are used throughout industries such as motors and power converters. When manufacturing Fe-based soft magnetic composites, the size and shape of the soft magnetic powder and the microstructure in the powder are closely related to the magnetic properties. In this study, Fe-Si-Al-P alloy powders were manufactured using various manufacturing process parameter sets, and the process parameters of the vacuum induction melt gas atomization process were set as melt temperature, atomization gas pressure, and gas flow rate. Process variable data that records are converted into 6 types of data for each powder recovery section. Process variable data that recorded minute changes were converted into 6 types of data and used as input variables. As output variables, a total of 6 types were designated by measuring the particle size, flowability, apparent density, and sphericity of the manufactured powders according to the process variable conditions. The sensitivity of the input and output variables was analyzed through the Pearson correlation coefficient, and a total of 6 powder characteristics were analyzed by artificial neural network model. The prediction results were compared with the results through linear regression analysis and response surface methodology, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.251-258
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• 2023

It is necessary to predict peak load accurately in order to supply electric power and operate the power system stably. Especially, it is more important to predict peak load accurately in winter and summer because peak load is higher than other seasons. If peak load is predicted to be higher than actual peak load, the start-up costs of power plants would increase. It causes economic loss to the company. On the other hand, if the peak load is predicted to be lower than the actual peak load, blackout may occur due to a lack of power plants capable of generating electricity. Economic losses and blackouts can be prevented by minimizing the prediction error of the peak load. In this paper, the latest deep learning model such as TCN is used to minimize the prediction error of peak load. Even if the same deep learning model is used, there is a difference in performance depending on the hyper-parameters. So, I propose methods for optimizing hyper-parameters of TCN for predicting the peak load. Data from 2006 to 2021 were input into the model and trained, and prediction error was tested using data in 2022. It was confirmed that the performance of the deep learning model optimized by the methods proposed in this study is superior to other deep learning models.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.17-25
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• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.

• Korean Chemical Engineering Research
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• v.61 no.2
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• pp.278-286
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• 2023

The present work evaluated the production of biohydrogen under mesophilic and thermophilic conditions through dark fermentation of palm oil mill effluent (POME) in batch mode using the design of experiment methodology. Response surface methodology (RSM) was applied to investigate the influence of the two significant parameters, POME concentration as substrate (5, 12.5, and 20 g/l), and volumetric substrate to inoculum ratio (1:1, 1:1.5, and 1:2, v/v.%), with inoculum concentration of 14.3 g VSS/l. All the experiments were analyzed at 37 ℃ and 55 ℃ at an incubation time of 24 h. The highest chemical oxygen demand (COD) removal, hydrogen content (H₂%), and hydrogen yield (HY) at a substrate concentration of 12.5 g COD/l and S:I ratio of 1:1.5 in mesophilic and thermophilic conditions were obtained (27.3, 24.2%), (57.92, 66.24%), and (6.43, 12.27 ml H₂/g COD_rem), respectively. The results show that thermophilic temperature in terms of COD removal was more effective for higher COD concentrations than for lower concentrations. Optimum parameters projected by RSM with S:I ratio of 1:1.6 and POME concentration of 14.3 g COD/l showed higher results in both temperatures. It is recognized how RSM and optimization processes can predict and affect the process performance under different operational conditions.

• Journal of Powder Materials
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• v.30 no.2
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• pp.146-155
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• 2023

The Ti-6Al-4V lattice structure is widely used in the aerospace industry owing to its high specific strength, specific stiffness, and energy absorption. The quality, performance, and surface roughness of the additively manufactured parts are significantly dependent on various process parameters. Therefore, it is important to study process parameter optimization for relative density and surface roughness control. Here, the part density and surface roughness are examined according to the hatching space, laser power, and scan rotation during laser-powder bed fusion (LPBF), and the optimal process parameters for LPBF are investigated. It has high density and low surface roughness in the specific process parameter ranges of hatching space (0.06-0.12 mm), laser power (225-325 W), and scan rotation (15°). In addition, to investigate the compressive behavior of the lattice structure, a finite element analysis is performed based on the homogenization method. Finite element analysis using the homogenization method indicates that the number of elements decreases from 437,710 to 27 and the analysis time decreases from 3,360 to 9 s. In addition, to verify the reliability of this method, stress-strain data from the compression test and analysis are compared.

• 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.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.4
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• pp.382-393
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• 2024

The Microplotter system with a fluid dispensing method, sprays fluid based on ultrasonic pumping through piezoelectric devices. This technique can possible for various materials with a wide range of viscosities to be printed in microscale. In this paper, we introduces dispenser printing technology as well as aim to understand and apply various processes using the equipment. In addition, we will explain how to optimize the equipment by adjusting parameters such as spray intensity, tip height during printing, and patterning speed. By utilizing Microplotter's advantage of being compatible with a wide range of fluids, including metal nanoparticles, carbon nanotubes, DNA, and proteins, it is expected to be used in various fields such as printed electronics, biotechnology, and chemical engineering.

• Advances in nano research
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• v.16 no.4
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• pp.341-352
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• 2024

This study investigates the heat and mass transfer characteristics of a MoS₂ nanoparticle suspension in ethylene glycol over a porous stretching sheet. MoS₂ nanoparticles are known for their exceptional thermal and chemical stability which makes it convenient for enhancing the energy and mass transport properties of base fluids. Ethylene glycol, a common coolant in various industrial applications is utilized as the suspending medium due to its superior heat transfer properties. The effects of variable thermal conductivity, variable mass diffusivity, thermal radiation and thermophoresis which are crucial parameters in affecting the transport phenomena of nanofluids are taken into consideration. The governing partial differential equations representing the conservation of momentum, energy, and concentration are reduced to a set of nonlinear ordinary differential equations using appropriate similarity transformations. R software and MATLAB-bvp5c are used to compute the solutions. The impact of key parameters, including the nanoparticle volume fraction, magnetic field, Prandtl number, and thermophoresis parameter on the flow, heat and mass transfer rates is systematically examined. The study reveals that the presence of MoS₂ nanoparticles curbs the friction between the fluid and the solid boundary. Moreover, the variable thermal conductivity controls the rate of heat transfer and variable mass diffusivity regulates the rate of mass transfer. The numerical and statistical results computed are mutually justified via tables. The results obtained from this investigation provide valuable insights into the design and optimization of systems involving nanofluid-based heat and mass transfer processes, such as solar collectors, chemical reactors, and heat exchangers. Furthermore, the findings contribute to a deeper understanding of stretching sheet systems, such as in manufacturing processes involving continuous casting or polymer film production. The incorporation of MoS₂-C₂H₆O₂ nanofluids can potentially optimize temperature distribution and fluid dynamics.