【摘要】：通過BP神經(jīng)網(wǎng)絡(luò)與遺傳算法GA對(duì)激光焊接有限元模擬中的熱源模型參數(shù)進(jìn)行優(yōu)化,實(shí)現(xiàn)了對(duì)激光焊接溫度場(chǎng)的精確模擬。選取面體熱源模型對(duì)激光焊接溫度場(chǎng)進(jìn)行了有限元模擬,將模擬中難以確定并且對(duì)結(jié)果影響較大的熱源有效功率系數(shù)、熱能分配系數(shù)和熱源作用半徑作為輸入量,以有限元模擬結(jié)果的誤差作為輸出量對(duì)BP神經(jīng)網(wǎng)絡(luò)進(jìn)行訓(xùn)練,得到具有一定預(yù)測(cè)能力的神經(jīng)網(wǎng)絡(luò),并形成結(jié)合神經(jīng)網(wǎng)絡(luò)和遺傳算法的參數(shù)優(yōu)化方法。結(jié)果表明,經(jīng)過參數(shù)優(yōu)化后的激光焊接有限元模擬具有較高的精度。
[Abstract]:The heat source model parameters in laser welding finite element simulation are optimized by BP neural network and genetic algorithm GA, and the accurate simulation of laser welding temperature field is realized. The surface heat source model is selected to simulate the temperature field of laser welding by finite element method. The effective power coefficient, thermal energy distribution coefficient and the radius of action of heat source, which are difficult to determine in the simulation and have a great influence on the results, are taken as the input quantity. The BP neural network is trained with the error of the finite element simulation result as the output, and the neural network with certain predictive ability is obtained, and a parameter optimization method combining the neural network and genetic algorithm is formed. The results show that the finite element simulation of laser welding with optimized parameters has high accuracy.
【作者單位】： 武漢理工大學(xué)現(xiàn)代汽車零部件技術(shù)湖北省重點(diǎn)實(shí)驗(yàn)室;武漢理工大學(xué)汽車零部件技術(shù)湖北省協(xié)同創(chuàng)新中心;武漢理工大學(xué)汽車工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(51305317) 中國(guó)汽車產(chǎn)業(yè)創(chuàng)新發(fā)展聯(lián)合基金(U1564202) 湖北省自然科學(xué)基金重點(diǎn)項(xiàng)目(ZRS2014000009)
【分類號(hào)】：TG456.7

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