• Transactions of Materials Processing
• /
• v.24 no.4
• /
• pp.264-271
• /
• 2015

In the current study, a method was proposed to quantitatively predict the wear and fatigue life of a shearing die in order to determine an effective replacement period for the die. The shearing die model of a retainer manufacturing process was used for the proposed method of quantitative life prediction. The retainer is produced through shearing steps, such as piercing and notching. The shearing die of the retainer is carefully controlled because the dimensional accuracy of the retainer is critical. The fatigue life for the shearing die was predicted using ANSYS considering S-N curves of STD11 and Gerber’s equation. The wear life for the shearing die was predicted using DEFORM-3D considering the Archard’s wear model. Experimental shearing of the retainer was conducted to verify the effectiveness of the proposed method for predicting die life. The fatigue failure of the shearing die was macroscopically measured. The wear depth was measured using a 3D coordinate measuring machine. The results showed that the wear and fatigue life in the FE analysis agree well with the experimental results.

• The Journal of Korean Academy of Prosthodontics
• /
• v.37 no.4
• /
• pp.465-473
• /
• 1999

Bond strength of luting cements to dentin is a critical consideration for success of complete cast crowns. This study was performed to evaluate the relationship between surface characteristics of teeth prepared for complete cast crowns and retention of cemented restorations. Eighty artificial crowns were cast for standardized complete crown tooth preparations accomplished with the use of a special device on recently extracted human teeth. Coarse diamond(#102R, Shofu) and superfine finishing diamond(#SF102R, Shofu) burs of similar shape were used. Crowns in each group were randomly subdivided into few subgroups of 10 for luting cements selected for this study: zinc phosphate cement (FLECK' S), polycarboxylate cement (Poly-F), rein-forced glass ionomer cement (Fuji PLUS). and adhesive resin cement (Panavia 21). Retention was evaluated by measuring the tensile load required to dislodge the artificial crown from tooth preparations with an Instron testing machine, and analysed by one-way ANOVA and Student's t-test. The obtained results were as follows ; 1. When tooth preparation was done with coarse diamond bur, retentive force was diminished in order of Panavia 21 Fuji PLUS, FLECK'S, and Poly-F. Retentive forces showed the significant difference between Fuji PLUS group and FLECK'S group(p<0.001). 2. When tooth preparation was done with superfine diamond bur, retentive force was diminished in order of Fuji PLUS, Panavia 21, FLECK'S, and Poly-F. Retentive forces showed the significant difference between Panavia 21 group and FLECK'S group(p<0.001). 3. Retentive force in coarse tooth surfaces was significantly higher than that in superfine tooth surface with all luting cements(p<0.001), and cement residues were almost retained with-in the cast crown in all groups.

• Journal of Biosystems Engineering
• /
• v.31 no.1 s.114
• /
• pp.59-64
• /
• 2006

Application of herbicide for rice cropping is inevitable but notorious for its side effect of environmental pollution. Precision fanning will be one of important tools for the least input and sustainable fanning and could be achieved by implementation of the variable rating technology. If a device to detect weeds in rice field is available, herbicide could be applied only to the places where it is needed by the manner of the variable rating technology. The study was carried out to develop an algorithm of image processing to detect weeds in rice field using a machine vision system of multi-spectral digital images. A series of multi-spectral rice field picture of 560, 680 and 800 nm of center wavelengths were acquired from the 27th day to the 39th day after transplanting in the ineffective tillering stage of a rice growing period. A discrimination model to distinguish pixels of weeds from those of rice plant and weed image was developed. The model was proved as having accuracies of 83.6% and 58.9% for identifying the rice plant and the weed, respectively. The model was used in the algorithm to differentiate weed images from mingled images of rice plant and weed in a frame of rice field picture. The developed algorithm was tested with the acquired rice field pictures and resulted that 82.7%, 11.9% and 5.4% of weeds in the pictures were noted as the correctly detected, the undetected and the misclassified as rice, respectively, and 81.9% and 18.0% of rice plants in the pictures were marked as the correctly detected and the misclassified as weed, respectively.

• Computers and Concrete
• /
• v.34 no.2
• /
• pp.247-265
• /
• 2024

Measuring the fracture toughness of concrete in laboratory settings is challenging due to various factors, such as complex sample preparation procedures, the requirement for precise instruments, potential sample failure, and the brittleness of the samples. Therefore, there is an urgent need to develop innovative and more effective tools to overcome these limitations. Supervised learning methods offer promising solutions. This study introduces seven machine learning algorithms for predicting concrete's effective fracture toughness (K-eff). The models were trained using 560 datasets obtained from the central straight notched Brazilian disc (CSNBD) test. The concrete samples used in the experiments contained micro silica and powdered stone, which are commonly used additives in the construction industry. The study considered six input parameters that affect concrete's K-eff, including concrete type, sample diameter, sample thickness, crack length, force, and angle of initial crack. All the algorithms demonstrated high accuracy on both the training and testing datasets, with R² values ranging from 0.9456 to 0.9999 and root mean squared error (RMSE) values ranging from 0.000004 to 0.009287. After evaluating their performance, the gated recurrent unit (GRU) algorithm showed the highest predictive accuracy. The ranking of the applied models, from highest to lowest performance in predicting the K-eff of concrete, was as follows: GRU, LSTM, RNN, SFL, ELM, LSSVM, and GEP. In conclusion, it is recommended to use supervised learning models, specifically GRU, for precise estimation of concrete's K-eff. This approach allows engineers to save significant time and costs associated with the CSNBD test. This research contributes to the field by introducing a reliable tool for accurately predicting the K-eff of concrete, enabling efficient decision-making in various engineering applications.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.6 no.4
• /
• pp.130-137
• /
• 1997

The demand for wire-cut EDM is increasing rapidly in the die and tool making industry. In this study machining characteristics such as machining rate, surface roughness, hand drum form and hardness of machined material are investigated experimentally under the conditions varing pulse on time, pulse off time, peak voltage, wire tension after fixing other conditions in SKD 11 and brass and brass workpiece. It was found that various operating conditions had significant influences on machining characteristics. But the hardness of workpiece was uneffected by operating conditions. Also it was obtained experimentally that brass workpeice had better machinability than SKD 11 one.dition according to the current(Ip) in an electric spark machine : 1) Electrode is utilized Cu and Graphite. 2) Work piece is used the material of carbon steel. The condition of experiment is : 1) Current is varied 0.7(A) to 50(A) and the time of electric discharging to work piece in each time is 30(min) to 60(min). 2) After the upper side of work piece was measured in radius(5$\mu$m) of stylus analyzed the surface roughness to ade the table and graph of Rmax by yielding data. 3) Electro wear ratio is : \circled1Cooper was measured ex-machining and post-machining by the electronic balance. \circled2The ex-machining of graphite measured by it, the post-machining was found the data from volume $\times$specific gravity and analyzed to made its table and graph on ground the data. 4) In order to keep the accuracy of voltage affected to the work piece was equipped with the A.V. R and the memory scope was sticked to the electric spark machine. 5) In order to preserve the precision of current, to get rid of the noise occured by internal resistance of electric spark machine and to force injecting for the discharge fluid , it made the fixed table for a work piece to minimize the work error by means of one's failure during the electric discharging.

• Journal of Biosystems Engineering
• /
• v.41 no.3
• /
• pp.177-183
• /
• 2016

Purpose: Mee (Madhuca longifolia) is an economically important tree growing throughout Sri Lanka. Its importance is mainly attributed to its oil with high nutritional and medicinal values. However, an inefficient extraction method limits its use. This study revealed the possibility of extracting oil from mee seeds by using a screw-type oil expeller. Methods: A popular screw-type oil expeller was used in the experiment. Extract bar clearance and speeds of the main spiral shaft were altered to increase the oil expelling efficiency of the machine. The quality of refined oil at the optimum oil yield was determined by measuring the refractive index, saponification value, iodine value, unsaponifiable matter, free fatty acid, and specific gravity. Results: An optimum yield of 35% oil was obtained when the machine capacity was 30 kg/h and energy consumption was 0.13 kWh/kg. This optimum machine condition was observed at an extract bar clearance of 0.5 mm and a main spiral shaft speed of 90 rpm. The refractive index, saponification value, iodine value, unsaponifiable matter, free fatty acid, and specific gravity of the oil were 1.4, 203, 59, 3.5%, 0.2%, and 0.907 g/cm3 respectively. Color of the mee oil was closer to yellow, which is revealed by the lightness value (L) of 24.93 and positive value (b) of 11.81. Conclusion: The screw-type oil expeller can be used for economically extracting mee oil on a commercial scale.

• Journal of Korean Society on Water Environment
• /
• v.35 no.5
• /
• pp.425-431
• /
• 2019

Recently, people have been attracting attention to the good quality of water resources as well as water welfare. to improve the quality of life. This study is a papers on the prediction of benthic macroinvertebrate index (BMI), which is a aquatic ecological health, using the machine learning based CFS (Correlation-based Feature Selection) method and the random forest model to compare the measured and predicted values of the BMI. The data collected from the Han River's branch for 10 years are extracted and utilized in 1312 data. Through the utilized data, Pearson correlation analysis showed a lack of correlation between single factor and BMI. The CFS method for multiple regression analysis was introduced. This study calculated 10 factors(water temperature, DO, electrical conductivity, turbidity, BOD, $NH_3-N$, T-N, $PO_4-P$, T-P, Average flow rate) that are considered to be related to the BMI. The random forest model was used based on the ten factors. In order to prove the validity of the model, $R^2$, %Difference, NSE (Nash-Sutcliffe Efficiency) and RMSE (Root Mean Square Error) were used. Each factor was 0.9438, -0.997, and 0,992, and accuracy rate was 71.6% level. As a result, These results can suggest the future direction of water resource management and Pre-review function for water ecological prediction.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.164-164
• /
• 2023

This study investigates the importance of flood-influencing factors on the accuracy of flood risk mapping using the integration of remote sensing-based and machine learning techniques. Here, the Extreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms integrated with GIS-based techniques were considered to develop and generate flood risk maps. For the study area of NAPA County in the United States, rainfall data from the 12 stations, Sentinel-1 SAR, and Sentinel-2 optical images were applied to extract 13 flood-influencing factors including altitude, aspect, slope, topographic wetness index, normalized difference vegetation index, stream power index, sediment transport index, land use/land cover, terrain roughness index, distance from the river, soil, rainfall, and geology. These 13 raster maps were used as input data for the XGBoost and RF algorithms for modeling flood-prone areas using ArcGIS, Python, and R. As results, it indicates that XGBoost showed better performance than RF in modeling flood-prone areas with an ROC of 97.45%, Kappa of 93.65%, and accuracy score of 96.83% compared to RF's 82.21%, 70.54%, and 88%, respectively. In conclusion, XGBoost is more efficient than RF for flood risk mapping and can be potentially utilized for flood mitigation strategies. It should be noted that all flood influencing factors had a positive effect, but altitude, slope, and rainfall were the most influential features in modeling flood risk maps using XGBoost.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.727-730
• /
• 2023

The operation of electric automatic windows is used in harsh environments, and the energy density decreases as charging and discharging are repeated, and as soundness deteriorates due to damage to the internal separator, the vehicle's mileage decreases and the charging speed slows down, so about 5 to 10 Batteries that have been used for about a year are classified as waste batteries, and for this reason, as the risk of battery fire and explosion increases, it is essential to diagnose batteries and estimate SOH. Estimation of current battery SOH is a very important content, and it evaluates the state of the battery by measuring the time, temperature, and voltage required while repeatedly charging and discharging the battery. There are disadvantages. In this paper, measurement of discharge capacity (C-rate) using a waste battery of a Tesla car in order to predict SOH estimation of a lithium-ion battery. A Support Vector Machine (SVM), one of the machine models, was applied using the data measured from the waste battery.

• Restorative Dentistry and Endodontics
• /
• v.27 no.2
• /
• pp.150-157
• /
• 2002

One of the latest concepts in bonding are "total etch", in which both enamel and dentin are etched with an acid to remove the smear layers, and "wet dentin" in which the dentin is not dry but left moist before application of the bonding primer Ideally the application of a bonding agent to tooth structure should be insensitive to minor contamination from oral fluids. Clinically, contaminations such as saliva, gingival fluid, blood and handpiece lubricant are often encountered by dentists during cavity preparation. The aim of this study was to evaluate the effect of contamination by hemostatic agents on shear bond strength of compomer restorations. One hundred and ten extracted human maxillary and mandibular molar teeth were collected. The teeth were removed soft tissue remnant and debris and stored in physiologic solution until they were used. Small flat area on dentin of the buccal surface were wet ground serially with 400, 800 and 1200 abrasive papers on automatic polishing machine. The teeth were randomly divided into 11 groups. Each group was conditioned as follows : Group 1: Dentin surface was not etched and not contaminated by hemostatic agents. Group 2: Dentin surface was not etched but was contaminated by Astringedent$^{\circledR}$(Ultradent product Inc., Utah, U.S.A.) Group 3: Dentin surface was not etched but was contaminated by Bosmin$^{\circledR}$(Jeil Pharm, Korea.). Group 4: Dentin surface was not etched but was contaminated by Epri-dent$^{\circledR}$(Epr Industries, NJ, U.S.A.). Group 5: Dentin surface was etched and not contaminated by hemostatic agents. Group 6: Dentin sorface was etched and contaminated by Astringedent$^{\circledR}$. Group 7 : Dentin surface was etched and contaminated by Bosmin$^{\circledR}$. Group 8: Dentin surface was etched and contaminated by Epri-dent$^{\circledR}$. Group 9: Dentin surface was contaminated by Astringedent$^{\circledR}$. The contaminated surface was rinsed by water and dried by compressed air. Group 10: Dentin surface was contaminated by Bosmin$^{\circledR}$. The contaminated surface was rinsed by water and dried by compressed air. Group 11 : Dentin surface was contaminated by Epri-dent$^{\circledR}$. The contaminated surface was rinsed by water and dried by compressed air. After surface conditioning, F2000$^{\circledR}$ was applicated on the conditoned dentin surface The teeth were thermocycled in distilled water at 5$^{\circ}C$ and 55$^{\circ}C$ for 1,000 cycles. The samples were placed on the binder with the bonded compomer-dentin interface parallel to the knife-edge shearing rod of the Universal Testing Machine(Zwick Z020, Zwick Co., Germany) running at a cross head speed or 1.0 mm/min. Group 2 showed significant decrease in shear bond strength compared with group 1 and group 6 showed significant decrease in shear bond strength compared with group 5. There were no significant differences in shear bond strength between group 5 and group 9, 10 and 11.