CN113095677A - 一种基于加工质量逆向推导的加工过程定量控制方法 - Google Patents
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Abstract
本发明公开了一种基于加工质量逆向推导的加工过程定量控制方法,该方法包括如下步骤:S1、设计并开展单因素实验确定对加工质量影响较大的加工参数及其最佳取值范围;S2、基于S1的结果,设计并开展基于响应曲面法的加工参数正交实验,获取实验结果;S3、将加工质量与加工参数之间的正向模型与遗传算法结合,开发基于加工质量逆向推导加工参数的算法并实现对加工过程的定量控制;S4、开展工艺实验,验证并修正上述算法。本发明提出的基于加工质量逆向推导的加工过程定量控制方法,用以解决了需要多次重复加工才可达到要求加工质量的问题,提升了加工效率和可控性,降低了加工和时间成本。
Description
技术领域
本发明涉及精密超精密加工领域,尤其是涉及精密超精密加工领域的加工过程定量控制方法。
背景技术
随着科学技术的发展,航空航天、天文设备和大型激光设备等高端行业对零件的加工精度和加工效率要求越来越高,因此也对零件加工过程的可预测性和可控性提出了更高要求。为提高加工过程的可预测性和可控性,许多学者针对不同加工方法研究并建立了零件加工质量和加工参数之间的数学关系模型,用于对零件加工后的质量进行预测。如Zhang Z Z(Zhang Z Z,Yao P,Wang J,et al.Nanomechanical characterization of RB-SiC ceramics based on nanoindentation and modelling ofthe ground surfaceroughness.CERAM.INT.46(2020)6243-6253.)建立并验证了预测超精密磨削过程中加工表面粗糙度的理论模型。Wu J(Wu J,Cheng J,Gao C C,et al.Research on predictingmodel of surface roughness in small-scale grinding of brittle materialsconsidering grinding tool topography.INT J MECH SCI.166(2020)DOI:10.1016/j.ijmecsci.2019.105263.)提出了一种磨削后表面粗糙度的预测模型。Qi J(Qi J,ZhangD,Li S,et al.Modeling and prediction of surface roughness in belt polishingbased on artificial neural network.P I MECH ENG B-J ENG.232(2018)2154-2163.)提出了一种缎带抛光中工件抛光后的表面粗糙度预测模型。
现有的多数研究主要关注加工质量和加工参数之间的正向模型,即根据给定的加工参数来预测加工质量。如公式所示:T=f(x1,x2,……,xn)。其中T代表与加工质量相关的参数(一般为表面粗糙度),xi(i=1,2,…n)代表加工参数。
但上述模型在应用中存在一定缺陷,原因在于:对于实际加工过程,多数情况是需要根据需求的加工质量值来求解出对应的加工参数值,此过程无法通过上述模型直接实现,而只能通过不断更换不同的加工参数组合进行预测,而后选取其中最接近需求加工质量的加工参数组合,一定程度上影响了加工过程的定量控制。
针对此,本发明提出基于加工质量逆向推导加工参数的方法,用于根据需求的加工质量来反求加工参数值,实现对加工过程的定量控制。
发明内容
本发明要解决的技术问题是现有的加工质量与加工参数间的数学关系模型无法根据给定的加工质量,直接定量推导出加工参数值,需要重复试验,影响了加工过程的定量控制。本发明提供了解决上述问题的基于加工质量逆向推导的加工过程定量控制方法。
本发明通过下述步骤实现:
基于加工质量逆向推导的加工过程定量控制方法,该方法包括如下步骤:
S1、设计并开展单因素实验确定对加工质量影响较大的加工参数及其最佳取值范围;
S2、基于S1的结果,设计并开展基于响应曲面法的加工参数正交实验,获得实验结果,并将其用于构建加工质量和加工参数之间的正向模型,并对正向模型进行方差分析;
S3、将加工质量和加工参数之间的正向模型与遗传算法结合,开发基于加工质量逆向推导加工参数的算法,实现对加工过程的定量控制;
S4、开展工艺试验,验证并修正上述算法。
其中,所述步骤S2具体包括:
从S1中确定关键加工参数及其最佳取值范围后,在Design-Expert软件中设计关键加工参数的正交实验组。开展相应的实验后,利用实验数据拟合出加工质量与加工参数间的正向模型方程的形式:
T为加工质量对应的参数,x1,x2…xn为加工参数,βi为常系数。
所述步骤S4,本文所开发的基于加工质量逆向推导加工参数的算法具体步骤如下:
①设置遗传算法的参数,包括种群大小、遗传代数、交叉概率和突变概率等。在关键加工参数最佳取值范围内随机生成n组加工参数组合,将其定义为第一代人口;
②基于S2中得到的加工质量与加工参数间的正向模型建立适应度函数。随后分别评估上述n组加工参数的适应度;
③根据适应度值对上述n组加工参数进行排序,并将适应度最好的前a%组的关键加工参数组保留,并对其他(1-a)%组的关键加工参数进行选择,交叉和变异,以获得新的组合,被保留下来的加工参数组和变异后的加工参数组形成新一代种群,记录此时的遗传数为(t+1),t从0开始计数;
④比较遗传数与①中设置的遗传代数。若遗传数<遗传代数,返回②。否则,输出适应度最高的关键加工参数组合。
附图说明
此处所说明的附图用来提供对本发明实施例的进一步理解,构成本申请的一部分,并不构成对本发明实施例的限定。在附图中:
图1为验证本发明所提方法的实验装置及加工原理
图2为本发明中加工质量与加工参数的正向模型的建立过程
图3为验证本发明所提方法的实验结果数据
图4为验证本发明所提方法的步骤2中所建立的加工质量与加工参数的正向模型的实验结果
图5为本发明所开发的基于加工质量逆向推导加工参数的算法流程
图6为验证本发明所提方法的实验结果
具体实施方式
以下将以黄铜材料的复合磁流体抛光过程为例,结合实施例和附图,对本发明作进一步的详细说明,本发明的示意性实施方式及其说明仅仅用于解释本发明,并不作为对本发明的限定。在本例中表征加工质量的参数为聚碳酸酯材料抛光后的表面粗糙度,加工参数为抛光工艺参数,包括主轴转速、抛光间隙和抛光时间。
本实施例中所采用的实验装置模型如图1,主要由磁体夹具1,永磁体2,磁流体3,工件4,抛光盘5组成。永磁体2以偏心距r固定在磁体夹具1下方,永磁体2随磁体夹具1旋转。磁体夹具1沿图示方向可以上下调节抛光间距。抛光盘5固定。工件4在磁流体3下方,运动路径为往复直线运动。
根据上述章节,利用本发明实现对黄铜的复合磁流体抛光过程的定量控制方法包含以下步骤:
S1、根据之前对黄铜材料的抛光工艺参数的研究,进行单因素实验法可得抛光效果主要受主轴转速n,抛光间隙h和抛光时间t的影响。将以上参数设置为关键加工参数,并得到其最佳取值范围分别为200~1000rpm,1~2mm和30~120min。
S2、图2所示为本发明中加工质量与加工参数的正向模型的建立过程。具体包括:基于S1的结果,采用Design-Expert软件设计并开展了61组正交设计实验,实验结果如图3,将上述结果导入Design-Expert软件中建立加工质量Q与加工参数n、h、t间的正向模型,即:
Q=(160703+97714.3h+55.6943n-1610.61t+3.91987h*n-104.625h*t+0.0812614n*t+47414h2-0.0500214n2+8.02352t2)×10-6。
在Design-Expert软件中对拟合的方程进行方差分析证明该模型的理论精度。
对正向模型进行试验验证,随机生成10组加工参数组合,对零件进行抛光加工并记录抛光后的加工质量,实验值与根据上述正向模型计算的模拟值的比较结果如图4所示。
S3、将加工质量和加工参数之间的正向模型与遗传算法结合,开发基于加工质量逆向推导加工参数的算法,算法的具体步骤为:
①设置遗传算法的参数:种群大小为100,遗传代数为50,交叉概率为0.7,突变概率为0.01。在加工参数最佳取值范围内随机生成100组加工参数组合,将其定义为第一代人口。
②构建适应度函数。先设定变量G1为需求的表面粗糙度值Y与利用正向模型预测的表面粗糙度值T的差值。
G1=Y-T
设定变量G2为向量P和C之间距离,向量P(ht,nt,tt)为加工参数的目标值,向量C(hi,ni,ti)是关键加工参数的初始值,其值为(1.560075)。N是标准化因子,是关键加工参数最大值与最小值差的倒数,其值为(1 1/800 1/900)。
适应度函数R定义为
R=W1G1+W2G2
其中,经过多次尝试权重系数W1和W2分别设置为100和1。
③随机给定3个表面粗糙度目标值,通过本发明所提的逆推算法得到对应的3组关键加工参数。使用3组参数对工件进行抛光加工,记录对应表面粗糙度的实际值并计算与目标值的相对误差,平均相对误差为7.64%,如图6。证明了逆向模型的准确性。
如上所述,使用本发明所提的方法,可以直接根据所需的加工质量值反推与之对应的加工参数值,实现对加工过程的定量控制,对精密和超精密加工过程具有重要意义和使用价值。
Claims (2)
1.一种基于加工质量逆向推导加工过程定量控制方法,其特征在于,该方法包括以下步骤:
S1、设计并开展单因素实验确定对加工质量影响较大的加工参数及其最佳取值范围;
S2、基于S1的结果,设计并开展基于响应曲面法的加工参数正交实验,获取实验结果;
S3、将加工质量与加工参数之间的正向模型与遗传算法结合,开发基于加工质量逆向推导加工参数算法并实现对加工过程的定量控制;
S4、开展工艺实验,验证并修正基于加工质量逆向推导加工参数算法。
2.根据权利1所要求的一种基于加工质量逆向推导加工过程定量控制方法,其特征在于:基于加工质量逆向推导加工参数算法包含如下步骤:
①设置遗传算法的参数,包括种群大小、遗传代数、交叉概率和突变概率等;在关键加工参数最佳取值范围内随机生成n组加工参数组合,将其定义为第一代人口;
②基于S2中得到的加工质量与加工参数间的正向模型建立适应度函数;随后分别评估上述n组加工参数的适应度;
③根据适应度值对上述n组加工参数进行排序,并将适应度最好的前a%组的关键加工参数组保留,并对其他(1-a)%组的关键加工参数进行选择,交叉和变异,以获得新的组合,被保留下来的加工参数组和变异后的加工参数组形成新一代种群,记录此时的遗传数为(t+1),t从0开始计数;
④比较遗传数与①中设置的遗传代数;若遗传数<遗传代数,返回②;否则,输出适应度最高的关键加工参数组合。
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CN109828532A (zh) * | 2019-01-29 | 2019-05-31 | 贵州大学 | 一种基于ga-gbrt的表面粗糙度预测方法及工艺参数优化方法 |
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US20200184131A1 (en) * | 2018-06-27 | 2020-06-11 | Dalian University Of Technology | A method for prediction of key performance parameter of an aero-engine transition state acceleration process based on space reconstruction |
CN109828532A (zh) * | 2019-01-29 | 2019-05-31 | 贵州大学 | 一种基于ga-gbrt的表面粗糙度预测方法及工艺参数优化方法 |
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