摘要
针对汽车变速器带轮轴锻件多齿形、阶梯轴、带法兰等难成形的特点,在工艺分析基础上指出终锻是保证其成形精度和质量的关键。通过数值分析获得坯料在终锻工序中的应变场和模具应力场,并预测了终锻缺陷。基于响应面法、数值模拟和实验对终锻工艺参数进行了研究,通过线性加权和法建立了终锻成形质量的评价函数,得到了响应变量关于3个自变量的回归预测方程。研究结果表明,坯料温度对终锻成形质量影响最大,当优化工艺参数为坯料温度1070℃、摩擦系数0.22、凸模速度33 mm·s^-1时,终锻成形质量最好。实验验证了优化工艺参数下锻件齿形饱满,符合产品要求,为复杂带轮轴类锻件的热模锻成形提供了理论和工艺指导。
Aiming at the difficult-to-form characteristic of continuously variable transmission pulley shaft forgings such as multi-tooth,stepped shafts and flanges,based on process analysis,the final forging was pointed to be the key to ensure its forming accuracy and quality.The strain field and die stress field of the billet in the final forging process were obtained by numerical analysis,and the final forging defects were predicted.Based on the response surface method,numerical simulation and experiments,the process parameters of the final forging were studied.The evaluation function of the final forging forming quality was established by the linear weighted sum method.The regression prediction equations of the response variables with the three independent variables were obtained.The research results show that the blank temperature has the greatest influence on the final forging forming quality.When the optimized process parameters are blank temperature of 1070℃,friction coefficient of 0.22 and punch speed of 33 mm·s-1,the final forging forming quality is the best.The experiments verify that the forgings with the optimized process parameters have full tooth profile,which meets the product requirements and provides theoretical and technological guidance for hot die forging forming of complex pulley shaft forgings.
作者
陈鑫
王匀
张太良
张扣宝
CHEN Xin;WANG Yun;ZHANG Tai-liang;ZHANG Kou-bao(School of Mechanical Engineering,Jiangsu University,Zhenjiang 212013,China;Jiangsu Winner Machinery Co.,Ltd.,Xinghua 225714,China)
出处
《塑性工程学报》
CAS
CSCD
北大核心
2020年第12期30-36,共7页
Journal of Plasticity Engineering
基金
国家自然科学基金资助项目(51575245)
泰州市重大科技成果转化项目(SCG201905)。
关键词
带轮轴
热模锻
响应面法
数值模拟
缺陷预测
pulley shaft
hot die forging
response surface method
numerical simulation
defect prediction