(1. 燕山大學(xué) 先進(jìn)鍛壓成形技術(shù)與科學(xué)教育部重點(diǎn)實(shí)驗(yàn)室，秦皇島 066004；
2. 燕山大學(xué) 河北省特種運(yùn)載裝備重點(diǎn)實(shí)驗(yàn)室，秦皇島 066004)
摘 要: 為有效預(yù)測AA7075-T6板材變形破裂問題，設(shè)計(jì)10種不同應(yīng)力狀態(tài)的板材拉伸試樣，通過方程組法獲取BP神經(jīng)網(wǎng)絡(luò)的樣本數(shù)據(jù)，建立基于神經(jīng)網(wǎng)絡(luò)與遺傳算法(BP+GA法)的韌性斷裂準(zhǔn)則參數(shù)預(yù)測模型，并依據(jù)方程組法最優(yōu)試樣組合方案以及優(yōu)化后的斷裂參數(shù)，繪制AA7075-T6板材成形極限曲線。通過缺口試樣誤差評估比較方程組法和BP+GA法的斷裂預(yù)測精度，并應(yīng)用半球形剛模脹形試驗(yàn)對方程組法和BP+GA法兩種斷裂參數(shù)標(biāo)定方法繪制的成形極限曲線(FLC)進(jìn)行驗(yàn)證。結(jié)果表明：方程組法篩選后的最佳試樣組合方案接近于BP+GA法搜索得到的全局最優(yōu)解；通過BP+GA法繪制的AA7075-T6板材理論成形極限曲線為成形極限實(shí)測數(shù)據(jù)點(diǎn)集的下輪廓，預(yù)測結(jié)果趨近安全；而缺少平面應(yīng)變至雙向等拉區(qū)域的試驗(yàn)樣本導(dǎo)致理論FLC產(chǎn)生較大差距，從而反映了Lou-Huh準(zhǔn)則參數(shù)求解對測試試樣應(yīng)力狀態(tài)具有較高的敏感性。研究結(jié)果為高強(qiáng)鋁板斷裂理論參數(shù)分析和成形極限預(yù)測提供了借鑒和數(shù)據(jù)依據(jù)。
關(guān)鍵字: 韌性斷裂準(zhǔn)則；BP神經(jīng)網(wǎng)絡(luò)；遺傳算法；高強(qiáng)鋁板；成形極限預(yù)測
（1. Key Laboratory of Advanced Forging and Stamping Technology and Science, Ministry of Education, Yanshan University, Qinhuangdao 066004, China;
2. Hebei Key Laboratory of Special Delivery Equipment, Yanshan University, Qinhuangdao 066004, China）
Abstract:In order to effectively predict AA7075-T6 sheet deformation problem, the 10 sheet tensile specimens for different stress states were designed, according to the equations method to obtain sample data of BP neural network. A fracture parameter prediction model of ductile fracture criterion based on neural network and genetic algorithm (BP+GA) was established. The forming limit curves of AA7075-T6 sheet were drawn based on the optimal specimen option by the equations method and the fracture parameters optimized by the BP+GA method. The fracture prediction accuracies of equations method and BP+GA method were compared by evaluating the fracture prediction error of notched specimen, and the forming limit curves drawn by the equations method and the BP+GA method were verified by punch-stretch test. The results show that the optimal specimen option selected by the equations method is close to the global optimal solution obtained by the BP+GA method. The theoretical forming limit curve (FLC) of AA7075-T6 sheet drawn by BP+GA method is the lower profile of the experimental data point set, and the predicted result is safe. However, the lack of tensile specimens from plane strain to biaxial-equal tension stress regions results in a large gap between the theoretical FLC and test data, which reflects that the parameter calculation for Lou-Huh criterion has a high sensitivity to the stress state of the test specimen. The research result provides reference and data basis for fracture parameters analysis and forming limit prediction of high strength aluminum sheet.
Key words: ductile fracture criterion; BP neural network; genetic algorithm; high strength aluminum sheet; forming limit prediction
