類神經網路應用 (applications of artificial neural network, ANN)

類神經網路應用 (applications of artificial neural network, ANN)

Keywords : ANN, Artificial neural network, Machine learning, Application, Forging, Wear, Roll forming, Material, Metal forming, Flow stress curve,Forming limit curve ... .

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類神經網路 [WIKI]，屬機器學習之演算法 [WIKI]，透過資料建構數學模型，進行自適性訓練與預測。ANN 由輸入層、隱藏層與輸出層所構成，輸入層為設計變數，輸出層為預測值，隱藏層為建構輸入與輸出層之關聯性，並透過訓練範例訓練及調整網路模型。

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雲端服務 (cloud service)：

• [] Azure Machine Learning Studio, MICROSOFT,   https://azure.microsoft.com/zh-tw/services/machine-learning-studio/
• [] Neural Network Console, Sony, https://dl.sony.com

免費 / 開源軟體 (free / open source)：

• FaNN [Web]
• Stuttgart Neural Network Simulator (SNNS) [Web]

商用軟體：

• MATLAB [Web]
• Neuro Solutions [Web]
• STATISTICA [Web]

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應用 (application)：

• [] 折彎 (bending)：U型折彎彈回預測 [AMPT,2007]。
• [] 成本預估 (cost estimation)：以倒傳遞神經模型進行沖壓模具成本預估 [NCA,2014]
• [] 深引伸 (deep drawing)：初始鈑料輪廓控制點設計 [JIAMT,2010]。
• [] 熱鍛 (hot forging) ：以鍛造溫度與溢料厚度作為輸入參數，預估熱鍛模具之磨耗深度 [AJSE,2012] 。
• [] 連續輥軋 (roll forming)：成形負荷 [AMT,2012]、成形破壞 [IJMF,2013] .
• [] 材料 (material)：預估鎂合金 AZ-31 之塑流曲線 (flow stress curve) 與成形極限曲線 (forming limit curve, FLC) [CMS,2011] 。
• [] 徑向鍛造 (radial forging)：以初始料溫、模具入口角度、進給率、斷面縮減率作為輸入參數，透過 MATLAB 預估徑向鍛造(radial forging)成形負荷 [NCA,2014]。
• [] 專家系統 (expert system)：造紙機專家系統 [KAM,2009]。

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REFERENCE

• [] Photo, PIXABAY, https://goo.gl/JyDhJt
• [] 人工神經網路, WIKI, https://goo.gl/nBdoMu
• [] 機器學習, WIKI, https://goo.gl/tAjKFb
• [] Fast Artificial Neural Network Library (FANN), http://leenissen.dk/fann/wp/
• [] Stuttgart Neural Network Simulator (SNNS), http://www.ra.cs.uni-tuebingen.de/SNNS/
• [] Stuttgart Neural Network Simulator (SNNS), WIKI, https://en.wikipedia.org/wiki/SNNS
• [] Neuro Solutions, http://www.neurosolutions.com/
• [] Neural Network Toolbox, MathWorks, https://www.mathworks.com/products/neural-network.html
• [] STATISCA, WEB, http://statistica.io

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REF. PAPERs

• [] A. Chamekh, S. BenRhaiem, H. Khaterchi, H. BelHadjSalah,R. Hambli (2010) An optimization strategy based on a metamodel applied for the prediction of the initial blank shape in a deep drawing process, International Journal of Advanced Manufacturing Technology, Vol.50, pp.93-100. https://link.springer.com/article/10.1007/s00170-009-2512-y
• [] A. Azari, M. Poursina,D. Poursina (2014) Radial forging force prediction through MR, ANN, and ANFIS models, Neural Computing & Applications, Vol.25, pp.849-858. https://link.springer.com/article/10.1007/s00521-014-1562-8
• [] B. Ozcan,A. Figlali (2014) Artificial neural networks for the cost estimation of stamping dies, Neural Computing & Applications, Vol.25, pp.717-726. https://link.springer.com/article/10.1007%2Fs00521-014-1546-8
• [] H. S. Park,T. V. Anh (2013) Development of two-phase neural network-genetic algorithm hybrid model in modeling damage evolution in roll forming of aluminum sheet, International Journal of Material Forming, Vol.6, pp.423-436. https://link.springer.com/article/10.1007%2Fs12289-012-1096-5
• [] M. R. S. Yazdi, G. S. Bagheri,M. Tahmasebi (2012) Finite Volume Analysis and Neural Network Modeling of Wear During Hot Forging of a Steel Splined Hub, Arabian Journal for Science and Engineering, Vol.37, pp.821-829. https://link.springer.com/article/10.1007%2Fs13369-012-0210-9
• J. G. Wu, Q. Li,Y. Yan (2012) Emulation and prediction of the cold roll forming force, Advanced Manufacturing Technology, Pts 1-4, Vol.472-475, pp.206-213. https://www.scientific.net/AMR.472-475.206
• [] A. Forcellese, F. Gabrielli,M. Simoncini (2011) Prediction of flow curves and forming limit curves of Mg alloy thin sheets using ANN-based models, Computational Materials Science, Vol.50, pp.3184-3197. http://www.sciencedirect.com/science/article/pii/S0927025611003211
• [] X. M. Yu, Y. Zeng,K. S. Zhang (2009) The Analysis and Explore on Paper Making Expert System, 2009 Second International Symposium on Knowledge Acquisition and Modeling: Kam 2009, Vol 3, pp.319-322. http://ieeexplore.ieee.org/document/5362307/
• [] W. J. Liu, Q. Liu, F. Ruan, Z. Y. Liang,H. Y. Qiu (2007) Springback prediction for sheet metal forming based on GA-ANN technology, Journal of Materials Processing Technology, Vol.187, pp.227-231. http://www.sciencedirect.com/science/article/pii/S0924013606010909

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