基于灰色系统理论的筒形件变薄拉深工艺优化与预测
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摘要
为探究减薄率、凹模锥角与拉深速度3个工艺参数对筒形件变薄拉深成形质量的影响,以变薄拉深筒形件的力学性能(抗拉强度、屈服强度与布氏硬度)为目标函数,设计多组正交试验方案。基于灰色关联度,计算各个目标函数的关联系数与关联度,对工艺参数进行多目标优化,并得到最优参数组合;利用灰色系统理论建立GM (0,N)灰色预测模型对目标函数进行预测。结果表明:当减薄率为45%、凹模锥角为15°、拉深速度为10 mm·s~(-1)时,变薄拉深后筒形件的抗拉强度为679 MPa,屈服强度为623 MPa,布氏硬度为221 N·mm~(-2);工艺参数对变薄拉深筒形件力学性能的影响顺序为:减薄率>凹模锥角>拉深速度;利用GM (0,N)灰色预测模型对筒形件力学性能能实现较为精确的快速预测,预测误差较小,建模简单快速。
To investigate the influence of three process parameters such as thinning rate,die cone angle and ironing speed on the quality of ironing of cylindrical parts,the mechanical properties( tensile strength,yield strength and Brinell hardness) of ironing cylindrical part were taken as the objective functions,a series of orthogonal test schemes were designed. Based on the grey correlation degree,the correlation coefficient and correlation degree of each objective function were calculated,the process parameters were multi-objective optimized,and the optimal combination of parameters was obtained. Using the grey system theory,the GM( 0,N) grey prediction model was established to predict the objective function. The results show that when the thinning rate is 45%,the die cone angle is 15° and the ironing speed is 10 mm·s~(-1),the tensile strength of ironing cylindrical parts is 679 MPa,yield strength is 623 MPa and Brinell hardness is221 N·mm~(-2). The influence order of the process parameters on mechanical properties of ironing cylindrical parts is: thinning rate > die cone angle > ironing speed. The GM( 0,N) grey prediction model can predict the mechanical properties of cylindrical parts accurately and quickly,the prediction error is small and the modeling is simple and fast.
To investigate the influence of three process parameters such as thinning rate,die cone angle and ironing speed on the quality of ironing of cylindrical parts,the mechanical properties( tensile strength,yield strength and Brinell hardness) of ironing cylindrical part were taken as the objective functions,a series of orthogonal test schemes were designed. Based on the grey correlation degree,the correlation coefficient and correlation degree of each objective function were calculated,the process parameters were multi-objective optimized,and the optimal combination of parameters was obtained. Using the grey system theory,the GM( 0,N) grey prediction model was established to predict the objective function. The results show that when the thinning rate is 45%,the die cone angle is 15° and the ironing speed is 10 mm·s~(-1),the tensile strength of ironing cylindrical parts is 679 MPa,yield strength is 623 MPa and Brinell hardness is221 N·mm~(-2). The influence order of the process parameters on mechanical properties of ironing cylindrical parts is: thinning rate > die cone angle > ironing speed. The GM( 0,N) grey prediction model can predict the mechanical properties of cylindrical parts accurately and quickly,the prediction error is small and the modeling is simple and fast.
引文
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[11]GB/T 228.1-2010,金属材料.拉伸试验.第1部分:室温试验方法[S].GB/T 228.1-2010,Metallic materials.Tensile testing.Part 1:Method of test at room temperature[S].
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[13]陈国华,陈珑凯,张华文.安全事故指标多变量灰色预测方法及应用[J].工业安全与环保,2014,40(6):47-50.CHEN Guohua,CHEN Longkai,ZHANG Huawen.The method of multivariable gray model for the forecast of safety accident indicators and its application[J].Industrial Safety and Environmental Protection,2014,40(6):47-50.
[2]许江平,柳玉起,章志兵,等.变薄拉深过程模拟的有限元动力显式算法[J].锻压技术,2008,33(5);38-43.XU Jiangping,LIU Yuqi,ZHANG Zhibing,et al.Finite element dynamic explicit method in ironing process simulation[J].Forging&Stamping Technology,2008,33(5):38-43.
[3]白建雄,陈先朝,肖小亭,等.304不锈钢壳级进模变薄拉深工艺及数值模拟[J].锻压技术,2015,40(1);33-38.BAI Jianxiong,CHEN Xianchao,XIAO Xiaoting,et al.Ironing process and numerical simulation of progressive die for stainless steel 304 shell[J].Forging&Stamping Technology,2015,40(1):33-38.
[4]王俊彪,贾建军.多道次变薄拉深的模拟与优化设计[J].西北工业大学学报,1997,15(3):348-354.WANG Junbiao,JIA Jianjun.The simulation and optimization multi-step lroning process[J].Journal of Northwestern Polytechnic University,1997,15(3):348-354.
[5]SINGH S K,GUPTA A K,MAHESH K.A study on the extent of ironing of EDD steel at elevated temperature[J].CIRP Journal of Manufacturing Science and Technology,2010,3(1):73-79.
[6]ADAMOVIC D,MANDIC V,JURKOVIC Z,et al.An experimental modelling and numerical FE analysis of steel-strip ironing process[J].Tehnicˇki Vjesnik,2010,17(4):435-444.
[7]邓聚龙.灰理论基础[M].武汉:华中科技大学出版社,2002.DENG Julong.Basis of grey theory[D].Wuhan:Huazhong University of Science&Technology Press,2002.
[8]薛克敏,吴超,郭威武,等.基于灰色系统理论的隔热件成形优化[J].塑性工程学报,2018,25(3):30-34.XUE Kemin,WU Chao,GUO Weiwu,et al.Forming optimization of insulation parts based on grey system theory[J].Journal of Plasticity Engineering,2018,25(3):30-34.
[9]陈龙,黄璞,王炯,等.基于正交试验和灰色系统理论的拼焊板前纵梁成形优化[J].塑性工程学报,2012,19(4):1-5.CHEN Long,HUANG Pu,WANG Jiong,et al.Optimization of tailor-welded front longitudinal forming based on orthogonal experiment and grey system theory[J].Journal of Plasticity Engineering,2012,19(4):1-5.
[10]席奇豪,樊文欣,吕伟,等.基于灰色关联度的连杆衬套强力旋压参数优化[J].锻压技术,2016,41(7):114-117.XI Qihao,FAN Wenxin,LWei,et al.Optimization on power spinning parameters for connecting rod bushing based on grey relational degree[J].Forging&Stamping Technology,2016,41(7):114-117.
[11]GB/T 228.1-2010,金属材料.拉伸试验.第1部分:室温试验方法[S].GB/T 228.1-2010,Metallic materials.Tensile testing.Part 1:Method of test at room temperature[S].
[12]GB/T 231.4-2009,金属材料.布氏硬度试验.第4部分:硬度值表[S].GB/T 231.4-2009,Metallic materials.Brinell hardness test.Part 4:Tables of hardness values[S].
[13]陈国华,陈珑凯,张华文.安全事故指标多变量灰色预测方法及应用[J].工业安全与环保,2014,40(6):47-50.CHEN Guohua,CHEN Longkai,ZHANG Huawen.The method of multivariable gray model for the forecast of safety accident indicators and its application[J].Industrial Safety and Environmental Protection,2014,40(6):47-50.