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인공신경망을 활용한 사출성형품의 질량과 치수 예측에 관한 연구
양동철,이준한,김종선,Yang, Dong-Cheol,Lee, Jun-Han,Kim, Jong-Sun 한국금형공학회 2020 한국금형공학회지 Vol.14 No.3
This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network (ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The input parameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length, the average deviation of ANN was 0.043 mm.
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전이학습 기반 사출 성형품 burr 이미지 검출 시스템 개발
양동철,김종선,Yang, Dong-Cheol,Kim, Jong-Sun 한국금형공학회 2021 한국금형공학회지 Vol.15 No.3
An artificial neural network model based on a deep learning algorithm is known to be more accurate than humans in image classification, but there is still a limit in the sense that there needs to be a lot of training data that can be called big data. Therefore, various techniques are being studied to build an artificial neural network model with high precision, even with small data. The transfer learning technique is assessed as an excellent alternative. As a result, the purpose of this study is to develop an artificial neural network system that can classify burr images of light guide plate products with 99% accuracy using transfer learning technique. Specifically, for the light guide plate product, 150 images of the normal product and the burr were taken at various angles, heights, positions, etc., respectively. Then, after the preprocessing of images such as thresholding and image augmentation, for a total of 3,300 images were generated. 2,970 images were separated for training, while the remaining 330 images were separated for model accuracy testing. For the transfer learning, a base model was developed using the NASNet-Large model that pre-trained 14 million ImageNet data. According to the final model accuracy test, the 99% accuracy in the image classification for training and test images was confirmed. Consequently, based on the results of this study, it is expected to help develop an integrated AI production management system by training not only the burr but also various defective images.
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인공신경망 구조에 따른 사출 성형폼 품질의 예측성능 차이에 대한 비교 연구
양동철,이준한,김종선,Yang, Dong-Cheol,Lee, Jun-Han,Kim, Jong-Sun 한국금형공학회 2021 한국금형공학회지 Vol.15 No.1
The quality of products produced by injection molding process is greatly influenced by the process variables set on the injection molding machine during manufacturing. It is very difficult to predict the quality of injection molded product considering the stochastic nature of manufacturing process, because the process variables complexly affect the quality of the injection molded product. In the present study we predicted the quality of injection molded product using Artificial Neural Network (ANN) method specifically from Multiple Input Single Output (MISO) and Multiple Input Multiple Output (MIMO) perspectives. In order to train the ANN model a systematic plan was prepared based on a combination of orthogonal sampling and random sampling methods to represent various and robust patterns with small number of experiments. According to the plan the injection molding experiments were conducted to generate data that was separated into training, validation and test data groups to optimize the parameters of the ANN model and evaluate predicting performance of 4 structures (MISO1-2, MIMO1-2). Based on the predicting performance test, it was confirmed that as the number of output variables were decreased, the predicting performance was improved. The results indicated that it is effective to use single output model when we need to predict the quality of injection molded product with high accuracy.
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배양된 인간 영양배엽세포주에성 ATP와 Adenosine이 세포 성장과 아폽토시스에 미치는 영향
박인양 ( Park In Yang ),이지현 ( Lee Ji Hyeon ),이권행 ( Lee Gwon Haeng ),양동은 ( Yang Dong Eun ),문희봉 ( Mun Hui Bong ),김사진 ( Kim Sa Jin ),신종철 ( Sin Jong Cheol ) 대한산부인과학회 2003 Obstetrics & Gynecology Science Vol.46 No.5
Background : Although nucleotides -like Adenosine Triphosphate (ATP) and its derivatives Adenosine, were known to induce growth inhibition and apoptosis in diverse cell lines, little is known about their effects on trophoblast. Objective : To elucidate the effects of extracellular ATP and adenosine on trophoblast cell growth and to delineate if apoptosis is involved in this mechanism. Materials and Methods : We used TL cell line, derived from human term placenta. The cells were cultured for 24, 48, and 72 hours after being treated with ATP and adenosine, each. Also, cell growth according to different concentrations of ATP and adenosine was evaluated. To test whether apoptosis was induced by each nucleotide, DNA fragmentation and nuclear condensation by Hoechst 33258 stain and P53 protein expression were evaluated. Results : CEll growth was inhibited by ATP and adenosine in time and dose-dependent manner. Furthermore, the growth inhibitory effect of adenosine was stronger than ATP, whereas sings of DAN fragmentation and nuclear condensation were observed in ATP treated cells, but not in adenosine treated ones. Conclusion : Our results shows that ATP and adenosine exert inhibitory effect on growth in TL cell line. These findings suggest that pathological production of ATP or its metabolites, adenosine, may lead to a pathologic status such as preeclampsia or intrauterine growth restriction.
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