| 引用本文: | 贺亚芳,吴会平,李细锋,毛祺栋.高强钢控制臂件冲压工艺优化及稳健性分析[J].材料科学与工艺,2015,23(3):39-43.DOI:10.11951/j.issn.1005-0299.20150308. |
| HE Yafang,WU Huiping,LI Xifeng,MAO Qidong.Stamping process optimization and robustness analyses for high strength steel control arm[J].Materials Science and Technology,2015,23(3):39-43.DOI:10.11951/j.issn.1005-0299.20150308. |
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| 摘要: |
| 为获得高强钢控制臂件冲压工艺参数最优解,提高冲压工艺稳健性,基于6sigma稳健设计理论,采用数值模拟方法分析预成形模具间隙、局部翻边模间隙、预成形压边力、摩擦系数、板料轮廓尺寸等对拐角处材料减薄率的影响规律；将坯料位置、料片厚度、摩擦系数、塑性应变比、屈服强度、抗拉强度、板料尺寸等作为噪声变量输入,分析工艺的稳健性；根据敏感性分析结果,选用局部翻边模间隙、预成形模具间隙为变量,3σ水平设为目标,成形过程能力Cp值为1.0,进行优化计算；最后采用优化后的局部翻边模间隙、预成形模具间隙值,其他噪音变量不变,再次进行稳健性分析.研究发现,影响控制臂局部过度减薄甚至开裂的主要因素为预成形模具间隙及局部翻边模具间隙.根据模拟结果试模,零件的壁厚分布与模拟结果相比,最大误差小于6%.通过对关键参数的敏感性分析以及考虑噪声因素的稳健性分析,优化工艺参数后,成形质量水平提高,成形结果可靠. |
| 关键词: 控制臂 sigma 敏感性分析 噪声 稳健性分析 工艺优化 |
| DOI：10.11951/j.issn.1005-0299.20150308 |
| 分类号:TG3 |
| 基金项目: |
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| Stamping process optimization and robustness analyses for high strength steel control arm |
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HE Yafang1,WU Huiping2,LI Xifeng2,MAO Qidong1
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(1.Shanghai Huizhong Automotive Manufacturing Co.,Ltd., Shanghai, 201805, China; 2.Institute of Forming Technology & Equipment, Shanghai Jiao Tong University, Shanghai, 200030, China)
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| Abstract: |
| To optimize stamping process parameters and improve the stamping process robustness for high strength steel control arm, the 6sigma robust design theory based numerical simulation is utilized to reveal the effects of preforming die gap, local flanging die gap, preforming blankholder force, friction coefficient, blank outline dimension, etc., on thickness thinning ratio at the corner. The noise variables, such as blank position, blank thickness, friction coefficient, plastic strain ratio, yield stress, ultimate tensile stress, blank size, etc., are input, and the process robustness is simulated. Based on the sensitivity analysis results, the optimization calculation is performed by setting the local flanging die gap and performing die gap as variables, and 3sigma quality level and Cp=1 as an object. Finally, the robustness analysis is carried on again by optimized local flanging die gap and preforming die gap. It′s found that the preforming die gap and local flanging die gap are the primary factors that affect excessive thinning and crack of control arm. The maximum error of thickness distribution between simulation and trial is less than 6%. By sensitivity analysis of key parameters and robustness analysis of noise parameters, the high and reliable quality level is achieved. |
| Key words: control arm sigma sensitivity analysis noise robustness analysis process optimization |
