Forming process optimization of double-sharp-edged aluminum alloy fender based on SVR model
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摘要: 为了实现双锐棱设计在轻量化材料的设计和应用，以铝合金材料的某车型双锐棱翼子板为研究对象，针对翼子板拉延成形过程中棱线部位滑移线不易控制等问题，采用基于支持向量机回归（SVR）的代理模型，并利用粒子群算法（PSO）建立优化模型寻求最优工艺参数。选取压边力B.H.F.、拉延筋系数ƒ1、拉延筋系数ƒ2、摩擦系数μ为优化参数，以主、副棱线最大滑移量为优化目标，建立了SVR模型并利用PSO建立多目标优化模型寻求最优工艺参数。获得最优工艺参数组合为：压边力为B.H.F.=1281.43 kN，拉延筋系数ƒ1=0.193，摩擦系数μ=0.150，拉延筋系数ƒ2=0.205，将优化后的工艺参数进行仿真求解得出主棱线滑移量为1.92 mm，副棱线滑移量1.31 mm，满足成形仿真判断标准。最后利用优化后的工艺参数以及仿真结果指导现场试模，得到了成形质量良好，无明显棱线滑移的合格零件。研究表明，采用SVR与PSO相结合的方法可以快速、有效地优化铝合金双锐棱翼子板成形工艺方案。Abstract: In order to realize the design and application of double-sharp edge design in lightweight materials, a double-sharp edge fender of a certain vehicle made of aluminum alloy was taken as the research object. To solve the problem the skid line of sharp edges could not be controlled easily, a surrogate model based on support vector machine regression (SVR) was used, and an optimization model was established by particle swarm optimization (PSO) to seek the optimal process parameters. The blank holder force B.H.F., drawbead coefficient ƒ1, drawbead coefficient ƒ2, and friction coefficient μ were selected as the optimization parameters, and the maximum slippage of the primary and secondary ridges was taken as the optimization objective, the SVR model was established, and the PSO was used to establish a multi-objective optimization to seek the optimal process parameters. The optimal combination of process parameters was obtained as follows: blank holder force B.H.F.=1281.43 kN, drawbead coefficient ƒ1=0.193, friction coefficient μ=0.150, drawbead coefficient ƒ2=0.205, the optimized process parameters were simulated and obtained. The slippage of the main ridgeline was 1.92 mm, and the slippage of the auxiliary ridgeline was 1.31 mm, which met the judgment standard of forming simulation. Finally, the optimized process parameters and simulation results were used to guide the die tryout, and the qualified parts with good forming quality and no obvious ridgeline skid line were obtained. The research shows that the combination of SVR and PSO can quickly and effectively optimize the aluminum alloy double-sharp edge fender forming process plan.
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表 1 AL6014-T4铝合金的材料参数
材料 AL6014-T4 板料厚度 0.9 弹性模量E/GPa 70 屈服强度${\sigma }_{s}/{\rm{MPa} }$ 103.5 泊松比$\mu $ 0.3 抗拉强度${ {\sigma } }_{ {b} }/{\rm{ {M}{P}{a} } }$ 214.1 硬化指数n$ {n} $ 0.259 材料密度ρ/(g/cm3) 2.7 
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表 2 拉丁超立方实验设计及结果
样本点 压边力/
kN拉延筋
系数1摩擦
系数拉延筋
系数2主棱线
滑移/
mm副棱线
滑移/
mm1 1 298 0.241 0.137 0.172 3.6 1 2 1 076 0.175 0.115 0.136 14.74 16.38 3 1 254 0.158 0.142 0.148 10.108 8.665 4 1 151 0.271 0.122 0.185 3.056 1.673 5 1 038 0.182 0.129 0.104 12.12 12.63 … … … … … … … 59 1 133 0.217 0.125 0.130 3.157 2.159 60 1 170 0.204 0.105 0.193 6.345 7.665
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表 3 SVR模型测试结果
序号 主棱线滑移/mm 副棱线滑移量/mm CAE
仿真值SVR
预测值CAE
仿真值SVR
预测值1 2.400 0 3.656 7 4.3 3.999 2 2 3.056 0 2.671 8 1.673 1.426 1 3 3.300 0 3.624 1 0.700 0 2.276 3 4 23.800 0 22.189 5 26.3 24.577 9 5 13.500 0 12.483 3 14.9 12.786 4 6 2.221 0 4.626 7 4.378 4.574 04 7 3.011 0 2.063 3 2.190 0 2.851 6 8 10.108 0 9.860 5 8.665 11.456 5 9 19.500 0 19.774 5 21.8 21.881 2 10 7.700 0 9.224 0 9.3 8.723
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表 4 优化前后铝合金翼子板棱线滑移量对比
序号 优化后参数 预测的结果 压边力/kN 拉延
筋1摩擦
系数拉延
筋2主棱线
滑移/mm副棱线
滑移/mm1 1 284 0.193 0.150 0.205 0.73 0.26 2 1 278 0.193 0.150 0.204 0.74 0.29 3 1 300 0.189 0.150 0.214 0.77 0.29
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