(1. 中南大學(xué) 材料科學(xué)與工程學(xué)院，長沙 410083;
2. 華中科技大學(xué) 塑性成形模擬及模具技術(shù)國家重點實驗室，
武漢 430074;
3. 云南省機械研究設(shè)計院， 昆明 650031)
摘 要: 基于MATLAB平臺， 將BP神經(jīng)網(wǎng)絡(luò)、遺傳算法和數(shù)值模擬技術(shù)應(yīng)用于鋁型材擠壓模具參數(shù)優(yōu)化設(shè)計。 采用三層BP神經(jīng)網(wǎng)絡(luò)建立型材擠壓模具的數(shù)學(xué)模型， 由正交實驗法安排模擬實驗組合，采用有限元軟件進行擠壓過程的數(shù)值模擬， 并以具有不同工作帶尺寸的擠壓模具中金屬流出模口平面上的Z向質(zhì)點流速均方差作為模型目標(biāo)值，將模擬結(jié)果作為神經(jīng)網(wǎng)絡(luò)的輸入樣本對訓(xùn)練網(wǎng)絡(luò)并建立網(wǎng)絡(luò)知識源， 通過遺傳算法求得模型的全局優(yōu)化解； 最后通過有限元數(shù)值模擬技術(shù)驗證并比較優(yōu)化所得工作帶與經(jīng)驗法確定的工作帶對金屬流動均勻性的影響。數(shù)值模擬結(jié)果表明， 本研究對擠壓模具工作帶的優(yōu)化是有效的。
關(guān)鍵字: 鋁型材； BP人工神經(jīng)網(wǎng)絡(luò)； 遺傳算法； 擠壓模具；工作帶； 有限元模擬
profile extrusion die
WANG Fang3, YANG Li-bin1
（ 1. School of Materials Science and Engineering,
Central South University, Changsha 410083, China;
2. State Key Laboratory of Plastic Forming Simulation and Die Technology,
Huazhong University of Science and Technology, Wuhan 430074, China;
3. Yunnan Mechanical Research and Design Institute, Kunming 650031, China）
Abstract: BP artificial neural network, genetic algorithm and FEM simulation were applied to optimize the design of profile extrusion die on MATLAB foundation. A three-layer neural network was used to set up mathematical model for profile extrusion dies with different bearing lengths. Orthogonal test was arranged for numerical simulation to get Z-velocity at the die land exit which was used as the target value of the model. The neural network is trained by the above Z-velocity values to form knowledge source, and the general optimized solution was attained through genetic algorithm. At last, the optimized bearing of the extrusion die was analyzed by FEM and compared to the design with experiential way. The simulation results show that the optimization of die bearing is effective.
Key words: aluminum profile; BP artificial neural network; genetic algorithm; extrusion die; bearing length; finite element simulation
