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作 者:贺焕利 王科银 王宇峰 HE Huan-li;WANG Ke-yin;WANG Yu-feng(School of Automotive Engineering,Hubei University of Automotive Technology,Hubei Shiyan 442002,China;Hubei Key Laboratory of Automotive Power Transmission and Electronic Control,Hubei University of Automotive Technology,Hubei Shiyan 442002,China)
机构地区:[1]湖北汽车工业学院汽车工程学院,湖北十堰442002 [2]湖北汽车工业学院汽车动力传动与电子控制湖北省重点实验室,湖北十堰442002
出 处:《精密成形工程》2022年第5期48-54,共7页Journal of Netshape Forming Engineering
基 金:湖北省重点实验室开放基金(ZDK1202003)。
摘 要:目的提高某汽车同步器齿环精锻成形的材料利用率、成形精度及降低成形载荷。方法构建以飞边体积为优化目标、以坯料尺寸和初始温度为优化变量、以未出现欠填充和折叠缺陷为约束条件的优化模型,并将克里金模型和遗传算法相结合,提出全局优化算法。结果使用构建的优化模型和优化算法,经过100代优化得到了飞边较小且无欠填充、无折叠缺陷的同步器齿环热精锻工艺参数,并使用实验验证了该优化工艺参数的合理性。优化和验证实验结果表明,坯料内径为62 mm、坯料高度为17 mm、成形温度为700℃时,热精锻成形的齿环没有折叠和欠填充缺陷,且飞边体积减少了大约10%、成形载荷下降了23%。结论对镍黄铜同步器齿环的优化和实验验证表明,使用克里金模型和遗传算法相结合的策略是解决此类小样本黑盒问题的有效手段。In order to improve the material utilization,improve the forming accuracy and reduce the forming load of the hot precision forging of an automobile synchronizer gear ring,an optimization model is established with the flash volume as the optimization objective,the billet size and initial temperature as the optimization variables,and no underfill and folding defects as the constraints.In addition,a global optimization method based on Kriging agent model and genetic algorithm is proposed.Using the constructed optimization model and algorithm,through 100 generations of evolutionary optimization,the hot precision forging process parameters of synchronizer gear ring with small flash and no underfill and folding defects are obtained,and the rationality of the optimized process parameters is verified by experiments.The optimization and verification experimental results show that when the blank inner diameter is 62 mm,the blank height is 17 mm and the forming temperature is 700℃,the gear ring formed by hot precision forging has no folding,underfilling defects,the flash volume is reduced by about 10%and the forming load is reduced by 23%.The optimization and experimental verification of nickel brass synchronizer ring show that the strategy based on Kriging model prediction and genetic algorithm optimization is an effective means to solve this kind of small sample black box problem.
分 类 号:TG316[金属学及工艺—金属压力加工]
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