• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.6
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• pp.1458-1464
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• 2007

Today, domestic and foreign manufacturing industries have to cope with obsolescence of manufacturing equipment because the shifting market trends drive the rapid changes in the production process resulting in stressful operation. Quality control process for manufacturing and production involves a familiar step - when the production process is completed, every item is subjected to various routine tests to determine that it meets the minimum quality standards. Typically, when a faulty product is found, the production line has to be stopped and the current batch is marked for further inspected and exhaustive testing. In this research, we propose a quality monitoring system for productivity enhancement. Our approach aims to reduces the operational down time in the production line of a car-component factory. The proposed monitoring system for productivity enhancement is designed to collect the data through testing at each phase of the assembly line and uses predictive methods on the collected data to achieve early detection of faults in the production process and minimize the number of products in a faulty batch thus minimizing the losses incurred from defective products. More importantly, this system aims to forecast and proactively detect faults and activate warnings when they are detected thus minimizing items in the defective batch, reducing the damage to manufacturing equipment and ultimately reducing the operational downtime or the delay in the resumption of normal factory operation.

• The KIPS Transactions:PartB
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• v.14B no.6
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• pp.407-412
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• 2007

The performance of ultrasonic testing systems for industrial or medical purpose largely depends on the performance of ultrasonic transducers. Generally, the information about an ultrasonic transducer performance characteristics are expressed by the ultrasonic R/F signal back from a reflector and its frequency characteristics in the data sheet provided by manufacturers. In case of focused ultrasonic transducers, the two pieces of information can, however, hardly assure that the focused ultrasonic transducer would produce well-focused C-scan images. Therefore, we propose the measured size of focal spot and the reconstructed shape of effective focal zone in the focused sound field as novel measures for the performance evaluation of the focused ultrasonic transducers. The process of getting the both measures of the transducers is conducted by the implemented software including sound field scanning and virtual 3D reconstruction functions which requires the echo of a point reflector. The proposed method could, otherwise impossible in the existing method, effectively and simply distinguish superior ones among many transducers made in the same specification and be also used to detect the performance degradation due to the aging of the transducers. Eventually, the quality of performance of the ultrasonic testing systems for industrial or medical purpose is secured.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1675-1683
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• 2018

This paper is a study on modeling of an NFC antenna to be inserted into a steel pallet for conveying selected good products through a vision system to a threaded screw from CNC equipment. The CNC equipment used here incorporates the information communication technology (ICT) corresponding to the Internet of Things (IoT), and the smart factory system technology that produces information by exchanging information freely in two directions by connecting the POP corresponding to the service Internet is evolved Equipment. Therefore, it is possible to collect position information on the threaded workpiece by applying NFC antenna designed considering iron pallet used for material management so as to grasp estimated completion time and actual production amount according to production instruction from existing analog type equipment to POP monitoring system And investigated its characteristics.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.217-223
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• 2019

A study in predicting defects of spot welding in real time in automotive field is essential for cost reduction and high quality production. Welding quality is determined by shear strength and the size of the nugget, and results depend on different independent variables. In order to develop the real-time prediction system, multiple regression analyses were conducted and the two dependent variables were obtained with sufficient statistical results with three independent variables, however, the quality prediction by the regression formula could not ensure accuracy. In this study, a multi-layer neural network circuit was constructed. The neural network by 10 dynamic resistance variables was constructed with three hidden layers to obtain execution functions and weighting matrix. In this case, the neural network was established with three independent variables based on regression analysis, as there could be difficulties in real-time control due to too many input variables. As a result, all test data were divided into poor, partial, and modalities. Therefore, a real-time welding quality determination system by three independent variables obtained by multiple regression analysis was completed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.10
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• pp.3626-3632
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• 2010

Some design changes were made to enhance the uniformity of temperature distribution inside the chamber of semiconductor test equipment. The design changes include the installation of adjustable airflow controller inside the chamber, the alignment of the centers of heater and match plate, the change in the size and the shape of holes in match plate base, and the addition of new holes of 2 mm diameter in order to allow airflow directly to the temperature sensors. In order to verify their effects, the temperature distributions inside the chambers were measured using 32 RTD sensors before and after the design changes. The temperature distributions were in the ranges of 87.1 to $91.5^{\circ}C$ ($90{\pm}2.9^{\circ}C$) and 89.5 to $90.8^{\circ}C$ ($90{\pm}0.8^{\circ}C$) before and after the design changes, respectively. The above temperature distribution after design changes was maintained for longer than 15 minutes, which satisfied the target temperature range of $90{\pm}1^{\circ}C$ for longer than 10 minutes.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.125-140
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• 2018

As one of the processes in the manufacturing industry, quality inspection inspects the intermediate products or final products to separate the good-quality goods that meet the quality management standard and the defective goods that do not. The manual inspection of quality in a mass production system may result in low consistency and efficiency. Therefore, the quality inspection of mass-produced products involves automatic checking and classifying by the machines in many processes. Although there are many preceding studies on improving or optimizing the process using the data generated in the production process, there have been many constraints with regard to actual implementation due to the technical limitations of processing a large volume of data in real time. The recent research studies on big data have improved the data processing technology and enabled collecting, processing, and analyzing process data in real time. This paper aims to propose the process and details of applying big data for quality inspection and examine the applicability of the proposed method to the dairy industry. We review the previous studies and propose a big data analysis procedure that is applicable to the manufacturing sector. To assess the feasibility of the proposed method, we applied two methods to one of the quality inspection processes in the dairy industry: convolutional neural network and random forest. We collected, processed, and analyzed the images of caps and straws in real time, and then determined whether the products were defective or not. The result confirmed that there was a drastic increase in classification accuracy compared to the quality inspection performed in the past.

• Applied Biological Chemistry
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• v.12
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• pp.83-88
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• 1969

In order to inhibit sprouting of garlics during the storage period and the extend their storage life, garlics were irradiated by gamma-rays $(Co^{60})$ and kept at room temperature. The effects of gammaradiation on sprouting inhibition, chemical contents during storage, and spoilage molds were investigated and the results are as follows. 1. Garlics obtained from Muan showed the shorter dormancy period and sprouted earlier than those from Dangjin. 2. Regarding the radioresistance, some differences were found between garlics from Muan and Dangjin. 3. The sprout of garlics from both areas was completely inhibited at 10 Krad and remarkable effect on sprouting was observed even with 5 Krad in case of garlics from Dangjin. 4. The rates of loss in moisture and weight were lower in irradiated lots than control. 5. Change of sugar content was not observed with ${\gamma}-irradiation$ under 300 Krad. 6. Spores of Aspergillus niger and Aspergillus alliaceus were little germinated at a level of 300 Krad.

• Applied Chemistry for Engineering
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• v.9 no.4
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• pp.529-535
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• 1998

This study was made to investigate the effect of variation in pulping process conditions such as wastepaper blending treatments, temperature, and chemical blending treatments on the deinked pulp. Design of experiments was used to select the major factors which significantly influenced on the deinked pulp. As one of the statistical analysis technique, analysis of variance and multiple comparison technique was used to find the best process condition and the predicted values and confidence intervals for brightness and strength were obtained. In the condition of $Na_2SiO_3$ 2.0%, $H_2O_2$ 0.5% and wastepaper blending treatments (KONP : AONP : OMG = 40 : 30 : 30 wt %) the highest brightness of 50.5% was predicted with 90% confidence interval (49.0, 52.0). On a concentration of $H_2O_2$ 0.5%, the highest tensile index of $35.7N{\cdot}m/g$ was predicted with 90% confidence interval (34.6, 36.6) at the pulping temperature of $50^{\circ}C$. On a concentration of surfactant 0.1%, the highest burst index of $0.129kPa{\cdot}m^2/g$ was predicted with 90% confidence interval (0.125, 0.133) at the pulping temperature of $50^{\circ}C$.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.230-238
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• 2014

In this paper, we propose the possible way of accurate analysis and examination of ITO sensor to discriminate whether mutual capacitance ITO sensor is defective by using mutual capacitance of data in each node which consists of electrodes inside of ITO sensor. We have analyzed the structure characteristic of mutual capacitance ITO sensor which is used as an input device for not only small size electronics like mobile phone and tablets but also big size electronics and designed the circuit to inspect ITO sensor using touch screen panel IC. Set a variable related with mutual capacitance of charge and discharge and Implement to find and analyze accurate position when defect is made through the data from each node of ITO sensor. First, we can set a yield effective range through the first experiment data of mutual capacitance ITO sensor and by using the data of each node of ITO sensor which is the result from the second experiment, we can verify accuracy and effectiveness of effective range from the first experiment as a sample which is used in this experiment.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.107-114
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• 2022

In order to classify 3D film images as right or wrong, it is necessary to detect the pattern in a 3D film image. However, if the contrast of the pixels in the 3D film image is low, it is not easy to classify as the right and wrong 3D film images because the pattern in the image might not be clear. In this paper, we propose a method of classifying 3D film images as right or wrong by comparing the width at a specific frequency of each histogram after obtaining the histogram. Since, it is classified using the width of the histogram, the analysis process is not complicated. From the experiment, the histograms of right and wrong 3D film images were distinctly different, and the proposed algorithm reflects these features, and showed that all 3D film images were accurately classified at a specific frequency of the histogram. The performance of the proposed algorithm was verified to be the best through the comparison test with the other methods such as image subtraction, otsu thresholding, canny edge detection, morphological geodesic active contour, and support vector machines, and it was shown that excellent classification accuracy could be obtained without detecting the patterns in 3D film images.