• Title/Summary/Keyword: residual prediction

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A Study on the Prediction of Welding Residual Stresses and the Selection of Optimal Welding Condition using Neural Network (신경회로망을 이용한 용접잔류응력 예측 및 최적의 용접조건 선정에 관한 연구)

  • 차용훈;이연신;성백섭
    • Journal of the Korean Society of Safety
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    • v.16 no.4
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    • pp.58-64
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    • 2001
  • In this study, it is developed that the system for effective prediction of residual stresses by the back-propagation algorithm using the neural network. To achieve This goal, the series experiment were carried out and measured the residual stresses using the sectional method. Using the experimental results, the optional control algorithms using a neural network should be developed in order to reduce the effect of the external disturbances during GMA welding processes. Then the results obtained from this study were compared between the measured and calculated results, weld guality might be controlled by the neural network based on backpropagation algorithm. This system can no only help to understand the interaction between the process parameters and residual stress, but also improve the quantity control for welded structures.

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Performance Analysis of Coding According to the Interpolation filter in Inter layer Intra Prediction of H.264/SVC (H.264/SVC의 계층간 화면내 예측에서 보간법에 따른 부호화 성능 분석)

  • Gil, Dae-Nam;Cheong, Cha-Keon
    • Proceedings of the IEEK Conference
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    • 2009.05a
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    • pp.225-227
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    • 2009
  • International standard specification, H.264/SVC improved from H.264/AVC, is set up so as to promote free use of huge multimedia data in various channel environments.;H.264/AVC is a international standard speicification for video compression, adopted and commercialized as standard for DMB broadcasting by JVT of ISO/IEC MPEG and ITU-T VCEG. SVC standard uses 'intra/inter prediction' in AVC as well as 'inter-layer intra prediction', 'inter-layer motion prediction' and 'inter-layer residual prediction' to improve efficiency of encoding. Among prediction technologies, 'inter-layer intra prediction' is to use co-located block of up sampled sublevels as a prediction signal. At this time, application of interpolation is one of the most important factors to determine encoding efficiency. SVC's currently using poly-phase FIR filter of 4-tap and 2-tap respectively to luma components. This paper is written for the purpose of analyzing encoding performance according to the interpolation. For this purpose, we applied poly-phase FIR filter of '2-tap', '4-tap' and '6-tap' respectively to luma components and then measured bit-rate, PNSR and running time of interpolation filter. We're expecting that the analysis results of this paper will be utilized for effective application of interpolation filter. SVC standard uses 'intra/inter prediction' in AVC as well as 'inter-layer intra prediction', 'inter-layer motion prediction' and 'inter-layer residual prediction' to improve efficiency of encoding.

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A Study on Effects of the Residual Stresses Around Cold Working Hole of the Aircraft Structure (항공기 구조물의 체결용 HOLE을 COLD WORKING 할때 생성되는 잔류응력의 영향연구)

  • 강수준;최청호
    • Journal of the Korea Institute of Military Science and Technology
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    • v.2 no.1
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    • pp.101-109
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    • 1999
  • The objective of this research is to study effects of the residual stresses on the crack growth and the life of the structure, caused by cold working around the hole of the aircraft structure which will be jointed by rivets and bolts, etc. The compensated Morrow's equation, by experimental data from the materials AL7075-T6 and AL2024-T3, is suggested to calculate the values of the fatigue life prediction of the structure. Also, the compensated Forman's equation, by experimental data from a material AL7075-T6, is suggested to calculate the values of the crack growth prediction of the structure. It is founded that the calculated values from the suggested equations are almost close to the known values of the fatigue life prediction and the crack growth prediction. It is shown that this paper, associated with an initial research on the effects of residual stresses around hole, gives a direction to study the problem at the aircraft maintenance field.

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A Study of Predicting Method of Residual Stress Using Artificial Neural Network in $CO_2$ Arc Welding (인공신경회로망을 이용한 탄산가스 아크 용접의 잔류응력 예측에 관한 연구)

  • 조용준;이세헌;엄기원
    • Journal of Welding and Joining
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    • v.13 no.3
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    • pp.77-88
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    • 1995
  • A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermomechanical analysis has been performed for the CO$_{2}$ arc welding using the finite element method. The first part of numerical analysis performs a three-dimensional transient heat transfer analysis, and the second part then uses the results of the first part and performs a three-dimensional transient thermo-elastic-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method are used to train a backpropagation neural network to predict the residual stress. Architecturally, the fully interconnected network consists of an input layer for the voltage and current, a hidden layer to accommodate the ailure mechanism mapping, and an output layer for the residual stress. The trained network is then applied to the prediction of residual stress in the four specimens. It is concluded that the accuracy of the neural network predicting method is fully comparable with the accuracy achieved by the traditional predicting method.

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A Study of Predicting Method of Residual Stress Using Artificial Neural Network in $CO_2$Arc welding

  • Cho, Y.;Rhee, S.;Kim, J.H.
    • International Journal of Korean Welding Society
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    • v.1 no.2
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    • pp.51-60
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    • 2001
  • A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermo-mechanical analysis has been performed for the $CO_2$ arc welding using the finite element method. The first part of numerical analysis performs a three-dimensional transient heat transfer analysis, and the second part then uses the results of the first part and performs a three-dimensional transient thermo-elastic-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method are used to train a back propagation neural network to predict the residual stress. Architecturally, the fully interconnected network consists of an input layer for the voltage and current, a hidden layer to accommodate the failure mechanism mapping, and an output layer for the residual stress. The trained network is then applied to the prediction of residual stress in the four specimens. It is concluded that the accuracy of the neural network predicting method is fully comparable with the accuracy achieved by the traditional predicting method.

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Prediction of Axial Residual Stress in Drawn High-Carbon Wire Resulting Due to Increase in Surface Temperature (고탄소강 다단 신선 와이어의 표면 온도 상승에 의한 축방향 잔류응력 예측)

  • Kim, Dae-Woon;Lee, Sang-Kon;Kim, Byung-Min;Jung, Jin-Young;Ban, Deok-Young
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.34 no.10
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    • pp.1479-1485
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    • 2010
  • In recent times, due to wire drawing of high carbon steel at a high speed to ensure a high productivity and high strength, axial residual stress are generated because of rapid increase in surface temperature. In the process, the temperatures of the wires increased because of the deformation of the wires and the friction between the die and wire. In particular, in the case of the wire drawing at a high speed, friction leads to a large temperature gradient so that considerable axial residual stress is generated on the surface. In this study, the relationship between axial residual stress and increase in the surface temperature was investigated, and a prediction model of uniform temperature was proposed. Then, a prediction model for residual stress was developed. The proposed model was verified by measuring the residual stress by X-ray diffraction on drawn wires.

Prediction Equations for FVC and FEV1 among Korean Children Aged 12 Years (체중 잔차를 이용한 12세 아동의 정상 폐기능 예측식)

  • Kang, Jong-Won;Sung, Joo-Hon;Cho, Soo-Hun;Ju, Yeong-Su
    • Journal of Preventive Medicine and Public Health
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    • v.32 no.1
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    • pp.60-64
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    • 1999
  • Objectives. Changes in lung function are frequently used as biological markers to assess the health effects of criteria air pollutants. We tried to formulate the prediction models of pulmonary functions based on height, weight, age and gender, especially for children aged 12 years who are commonly selected for the study of health effects of the air pollution. Methods. The target pulmonary function parameters were forced vital capacity(FVC) and forced expiratory volume in one second(FEV1). Two hundreds and fifity-eight male and 301 female 12-year old children were included in the analysis after excluding unsatisfactory tests to the criteria recommended by American Thoracic Sosiety and excluding more or less than 20% predicted value by previous prediction equations. The weight prediction equation using height as a independent variable was calculated, and then the difference of observed weight and predicted weight (i.e. residual) was used as the independent variable of pulmonary function prediction equations with height. Results. The prediction equations of FVC and FEV1 for male are FVC(ml) = $50.84{\times}height(cm)+7.06{\times}weight$ residual 4838.86, FEV1(ml) = $43.57{\times}height(cm)+3.16{\times}weight$ residual - 4156.66, respectively. The prediction equations of FVC and FEV1 for female are FVC(ml) = $42.57{\times}height(cm)+12.50{\times}weight$ residual - 3862.39, FEV1(ml) = $36.29{\times}height(cm)+7.74{\times}weight$ residual - 3200.94, respectively.

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PREDICTION OF RESIDUAL STRESS FOR DISSIMILAR METALS WELDING AT NUCLEAR POWER PLANTS USING FUZZY NEURAL NETWORK MODELS

  • Na, Man-Gyun;Kim, Jin-Weon;Lim, Dong-Hyuk
    • Nuclear Engineering and Technology
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    • v.39 no.4
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    • pp.337-348
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    • 2007
  • A fuzzy neural network model is presented to predict residual stress for dissimilar metal welding under various welding conditions. The fuzzy neural network model, which consists of a fuzzy inference system and a neuronal training system, is optimized by a hybrid learning method that combines a genetic algorithm to optimize the membership function parameters and a least squares method to solve the consequent parameters. The data of finite element analysis are divided into four data groups, which are split according to two end-section constraints and two prediction paths. Four fuzzy neural network models were therefore applied to the numerical data obtained from the finite element analysis for the two end-section constraints and the two prediction paths. The fuzzy neural network models were trained with the aid of a data set prepared for training (training data), optimized by means of an optimization data set and verified by means of a test data set that was different (independent) from the training data and the optimization data. The accuracy of fuzzy neural network models is known to be sufficiently accurate for use in an integrity evaluation by predicting the residual stress of dissimilar metal welding zones.

A Study on the Prediction of Thermally-Induced Residual Stress and Birefringence in Quenched Polystyrene Plate Including Free Volume Theory (자유 체적이론을 고려한 급냉 폴리스티렌판에 발생하는 잔류응력과 복굴절 형성에 관한 연구)

  • Kim, Jong-Sun;Yoon, Kyung-Hwan
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.27 no.1
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    • pp.77-87
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    • 2003
  • The residual stress and birefringence in injection-molded plastic parts can be divided into the flow-induced residual stress and birefringence produced in flowing stage, the thermally-induced residual stress and birefringence produced in cooling stage. However, the physics involved in the generation of the thermally-induced residual stress and birefringence still remains to be understood. Because polymer experiences viscoelastic history near the glass-transition temperature it is hard to model the entire process. Volume relaxation phenomenon was included to predict the final thermally-induced residual stress and birefringence in quenched plastic parts more accurately. The present study focused on comparing the predicted values far thermally-induced residual stress and birefringence with and without volume relaxation behavior (or free volume theory) under free and constrained quenching conditions. As a result, tile residual stress remained as a tensile stress at the center and as a compressible stress near the surface for the free quenching cases. In contract the residual stress remained as a compressible stress at the center and as a tensile stress near the surface fur the constrained quenching cases. The residual birefringence remained as minus values at the center and as plus values near the surface for the free quenching cases. Interestingly the residual birefringence showed minus values in entire zone for the constrained quenching cases. In the prediction of birefringence only the case including free volume theory showed the correct result for the distribution of birefringence in thickness direction.

Prediction of Error due to Eccentricity of Hole in Hole-Drilling Method Using Neural Network

  • Kim, Cheol;Yang, Won-Ho
    • Journal of Mechanical Science and Technology
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    • v.16 no.11
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    • pp.1359-1366
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    • 2002
  • The measurement of residual stresses by the hole-drilling method has been used to evaluate residual stresses in structural members. In this method, eccentricity can usually occur between the hole center and rosette gage center. In this study, we obtained the magnitude of the error due to eccentricity of a hole through the finite element analysis. To predict the magnitude of the error due to eccentricity of a hole in the biaxial residual stress field, it could be learned through the back propagation neural network. The prediction results of the error using the trained neural network showed good agreement with FE analyzed results.