• Title/Summary/Keyword: 인공신경망

Search Result 2,060, Processing Time 0.031 seconds

A Study on the Prediction of Optimized Injection Molding Condition using Artificial Neural Network (ANN) (인공신경망을 활용한 최적 사출성형조건 예측에 관한 연구)

  • Yang, D.C.;Lee, J.H.;Yoon, K.H.;Kim, J.S.
    • Transactions of Materials Processing
    • /
    • v.29 no.4
    • /
    • pp.218-228
    • /
    • 2020
  • The prediction of final mass and optimized process conditions of injection molded products using Artificial Neural Network (ANN) were demonstrated. The ANN was modeled with 10 input parameters and one output parameter (mass). The input parameters, i.e.; melt temperature, mold temperature, injection speed, packing pressure, packing time, cooling time, back pressure, plastification speed, V/P switchover, and suck back were selected. To generate training data for the ANN model, 77 experiments based on the combination of orthogonal sampling and random sampling were performed. The collected training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. Grid search and random search method were used to find the optimized hyper-parameter of the ANN model. After the training of ANN model, optimized process conditions that satisfied the target mass of 41.14 g were predicted. The predicted process conditions were verified through actual injection molding experiments. Through the verification, it was found that the average deviation in the optimized conditions was 0.15±0.07 g. This value confirms that our proposed procedure can successfully predict the optimized process conditions for the target mass of injection molded products.

A Development of Optimal Design Model for Initial Blank Shape Using Artificial Neural Network in Rectangular Case Forming with Large Aspect Ratio (세장비가 큰 사각케이스 성형 공정에서의 인공신경망을 적용한 초기 블랭크 형상 최적설계 모델 개발)

  • Kwak, M.J.;Park, J.W.;Park, K.T.;Kang, B.S.
    • Transactions of Materials Processing
    • /
    • v.29 no.5
    • /
    • pp.272-281
    • /
    • 2020
  • As the thickness of mobile communication devices is getting thinner, the size of the internal parts is also getting smaller. Among them, the battery case requires a high-level deep drawing technique because it has a rectangular shape with a large aspect ratio. In this study, the initial blank shape was optimized to minimize earing in a multi-stage deep drawing process using an artificial neural network(ANN). There has been no reported case of applying artificial neural network technology to the initial blank optimal design for a square case with large aspect ratio. The training data for ANN were obtained though simulation, and the model reliability was verified by performing comparative study with regression model using random sample test and goodness-of-fit test. Finally, the optimal design of the initial blank shape was performed through the verified ANN model.

FVT Signal Processing for Structural Identification of Cable-stayed Bridge (사장교의 구조식별을 위한 가진실험 데이터분석)

  • 이정휘;김정인;윤자걸
    • Transactions of the Korean Society for Noise and Vibration Engineering
    • /
    • v.14 no.10
    • /
    • pp.923-929
    • /
    • 2004
  • In this research, Forced Vibration Test(FVT) on a cable stayed bridge was conducted to examine the validity of the frequency domain pattern recognition method using signal anomaly index and artificial neuralnetwork. 7he considering structure, Samchunpo Bridge, located in Sachun-Shi, Kyungsangnam-Do, is a cable stayed bridge with the 436 meter span. The excitation force was induced by a sudden braking of a fully loaded truck. and vortical acceleration signals were acquired at 14 points. The initial 2-dimensional FE-model was developed from the design documents to prepare the training sets for the artificial neural network, and then the model calibration was performed with the field test data. As a result of the model calibration, we obtained the FFT spectrums from the model simulation, which was similar to those from the vibration test. These tests and the simulation data will be used for the structural identification using arbitrarily added masses to the bridge.

Springback Compensation of Sheet Metal Bending Process Based on DOE & ANN (판재 굽힘 성형에서 실험계획법 및 인공신경망을 이용한 탄성회복 보정)

  • An, Jae-Hong;Ko, Dae-Cheol;Lee, Chan-Joo;Kim, Byung-Min
    • Transactions of the Korean Society of Mechanical Engineers A
    • /
    • v.32 no.11
    • /
    • pp.990-996
    • /
    • 2008
  • Nowadays, the trend to a lightweight design accelerates the use of advanced high strength steel (AHSS) in automotive industry. Springback phenomena is a hot issue in the sheet metal forming, especially bending process using AHSS. Several analytical methods for that have been proposed in recent years. Each of method has their advantages and disadvantages. There are only a few optimal solutions which can minimize the two objectives simultaneously. In this study, an effective method optimized the multi objective value. The method by the design of experiments(DOE) and artificial neural network(ANN) was presented to compensate springback of bending parts. This method was applied to L and V bending process. The effective method could be optimized to multiple object. It was confirmed that the proposed method was more efficient than traditional manual FEA procedure and the trial and error approach for springback compensation.

FVT Signal Processing for Structural Identification of Cable-Stayed Bridge (사장교의 구조식별을 위한 가진실험 데이터분석)

  • 윤자걸;이정휘;김정인
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
    • /
    • 2003.11a
    • /
    • pp.619-623
    • /
    • 2003
  • In this research, Forced Vibration Test(FVT) on a cable stayed bridge was conducted to examine the validity of the frequency domain pattern recognition method using signal anomaly index and artificial neural network. The considering structure, Samchunpo Bridge, located in Sachun-Shi, Kyungsangnam-Do, is a cable stayed bridge with the 436 meter span. The excitation force was induced by a sudden braking of a fully loaded truck, and vertical acceleration signals were acquired at 14 points. The initial 2-dimensional FE-model was developed from the design documents to prepare the training sets for the artificial neural network, and then the model calibration was performed with the field test data. As a result of the model calibration, we obtained the FFT spectrums from the model simulation, which was similar to those from the vibration test. These tests and the simulation data will be used fur the structural identification using arbitrarily added masses to the bridge.

  • PDF

Study on the Prediction of Daily TOC Data by Using Wavelet Transform and Artificial Neural Networks (웨이블렛 변환과 인공신경망을 이용한 일 TOC 자료의 예측에 관한 연구)

  • Gwak, Pil Jeong;Oh, Chang Ryol;Jin, Young Hoon;Park, Sung Chun
    • Journal of Korean Society on Water Environment
    • /
    • v.22 no.5
    • /
    • pp.952-957
    • /
    • 2006
  • The present study applied wavelet transform and artificial neural networks (ANNs) for the prediction of daily TOC data. TOC data were transformed into denoised data by the wavelet transform and the noise-reduced data were used for the prediction model by artificial neural networks. For the application of wavelet transform, Daubechies wavelet of order 10 ('db10') was used as a basis function and decomposed the TOC data up to fifth level with five detail components and one approximation component. ANNs were calibrated with the input data of the segregated TOC data corresponding to the details from second to fifth level and the approximation. Consequently, the ANNs model for the prediction of daily TOC data showed the best result when it had seventeen hidden nodes in its layer.

Study on the Modelling of Algal Dynamics in Lake Paldang Using Artificial Neural Networks (인공신경망을 이용한 팔당호의 조류발생 모델 연구)

  • Park, Hae-Kyung;Kim, Eun-Kyoung
    • Journal of Korean Society on Water Environment
    • /
    • v.29 no.1
    • /
    • pp.19-28
    • /
    • 2013
  • Artificial neural networks were used for time series modelling of algal dynamics of whole year and by season at the Paldang dam station (confluence area). The modelling was based on comprehensive weekly water quality data from 1997 to 2004 at the Paldang dam station. The results of validation of seasonal models showed that the timing and magnitude of the observed chlorophyll a concentration was predicted better, compared with the ANN model for whole year. Internal weightings of the inputs in trained neural networks were obtained by sensitivity analysis for identification of the primary driving mechanisms in the system dynamics. pH, COD, TP determined most the dynamics of chlorophyll a, although these inputs were not the real driving variable for algal growth. Short-term prediction models that perform one or two weeks ahead predictions of chlorophyll a concentration were designed for the application of Harmful Algal Alert System in Lake Paldang. Short-term-ahead ANN models showed the possibilities of application of Harmful Algal Alert System after increasing ANN model's performance.

Effect of Climate Change on Water Quality in Seonakdong River Experimental Catchment (기후변화에 따른 서낙동강 시험유역에서의 수질영향 분석)

  • Kang, Ji Yoon;Kim, Jung Min;Kim, Young Do;Kang, Boo Sik
    • Journal of Korean Society of Water and Wastewater
    • /
    • v.27 no.2
    • /
    • pp.197-206
    • /
    • 2013
  • Recently, climate change causes climatic anomaly such as global warming, the typhoon and severe rain storm etc. and it brings damage frequently. Climate change and global warming are prevalent all over the world in this century and many researchers including hydrologists have studied on the climate change. In this study, Seonakdong river watershed in the Nakdong river basin was selected as a study area. Real-time monitoring system was used to draw the rating curves, which has 0.78 to 0.96 of $R^2$. To predict runoff change in Seonakdong river watershed caused by climate change, the change in hydrologic runoff were predicted using the watershed model, SWAT. As a result, the runoff from the Seonakdong river watershed was increased by up to 45 % in summer. Because of the non-point sources from the farmland and the urban area, the water quality will be affected by the climate change. In this study, the operating plan of the water gates in Seonakdong river will be suggested by considering the characteristics of the watershed runoff due to the climate change. The optimal watergate opening plan will solve the water pollution problems in the reservoir-like river.

Statistical Process Control System for Continuous Flow Processes Using the Kalman Filter and Neural Network′s Modeling (칼만 필터와 뉴럴 네트워크 모델링을 이용한 연속생산공정의 통계적 공정관리 시스템)

  • 권상혁;김광섭;왕지남
    • Journal of the Korean Society for Precision Engineering
    • /
    • v.15 no.3
    • /
    • pp.50-60
    • /
    • 1998
  • This paper is concerned with the design of two residual control charts for real-time monitoring of the continuous flow processes. Two different control charts are designed under the situation that observations are correlated each other. Kalman-Filter based model estimation is employed when the process model is known. A black-box approach, based on Back-Propagation Neural Network, is also applied for the design of control chart when there is no prior information of process model. Performance of the designed control charts and traditional control charts is evaluated. Average run length(ARL) is adopted as a criterion for comparison. Experimental results show that the designed control chart using the Neural Network's modeling has shorter ARL than that of the other control charts when process mean is shifted. This means that the designed control chart detects the out-of-control state of the process faster than the others. The designed control chart using the Kalman-Filter based model estimation also has better performance than traditional control chart when process is out-of-control state.

  • PDF

Pattern Classification of Acoustic Emission Signals During Wood Drying by Artificial Neural Network (인공신경망을 이용한 목재건조 중 발생하는 음향방출 신호 패턴분류)

  • 김기복;강호양;윤동진;최만용
    • Journal of Biosystems Engineering
    • /
    • v.29 no.3
    • /
    • pp.261-266
    • /
    • 2004
  • This study was Performed to classify the acoustic emission(AE) signal due to surface cracking and moisture movement in the flat-sawn boards of oak(Quercus Variablilis) during drying using the principal component analysis(PCA) and artificial neural network(ANN). To reduce the multicollinearity among AE parameters such as peak amplitude, ring-down count event duration, ring-down count divided by event duration, energy, rise time, and peak amplitude divided by rise time and to extract the significant AE parameters, correlation analysis was performed. Over 96 of the variance of AE parameters could be accounted for by the first and second principal components. An ANN analysis was successfully used to classify the Af signals into two patterns. The ANN classifier based on PCA appeared to be a promising tool to classify the AE signals from wood drying.