U形件成形工艺参数优化研究
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摘要
板料成形过程中普遍存在回弹问题，特别是在弯曲和浅拉深过程中回弹更为严重，对零件的尺寸精度和生产效率造成极大的影响，因此对回弹控制进行深入研究有重要意义。冲压界以往对于拉延成形件的回弹控制问题研究的不多，工程实际中通常基于经验和反复试验来减小或消除回弹的影响。上世纪90年代以来，随着拉延成形中起皱和拉裂问题逐步得到解决，回弹控制问题逐渐上升为研究重点，另外，板料冲压CAE技术的不断完善也为回弹控制研究提供了必要基础。
本文工作主要包括以下几个方面的内容：
1．综合考察及评价了多个工艺参数对板料冲压回弹量的影响。以NUMISHEET’93标准考题中U形板料作为研究对象，通过正交表安排试验，对板料的冲压进行数值模拟获得冲压回弹量，取得试验数据。从而研究了不同工艺参数对板料回弹的影响的程度，得出结论：除变压边力外的其它工艺参数如摩擦系数、凹模圆角等参数对回弹量控制同样是起到不可忽视的作用，同时可以得到使板料回弹量最小的参数组合。
2．建立了基于神经网络的从冲压工艺参数到冲压回弹量的非线性映射关系。利用正交试验获得的数据作为神经网络的训练样本，得到输入为工艺参数、输出为冲压回弹量的神经网络模型，并通过检验样本检验了ANN(Artificial Neural Network)模型的准确性，为参数优化及回弹预测做好准备。
3．利用神经网络模型的非线性映射关系，得到板料回弹量随工艺参数变化的平滑曲线，弥补了目前普遍采用的折线图的不足。
4．基于神经网络和正交试验的工艺参数优化。在工艺参数取值范围内，采用ANN模型代替CAE软件数值模拟试验，并结合正交试验法，对工艺参数进一步优化使得板料回弹量更小。论文工作表明：将神经网络与正交试验、数值模拟三者结合用于板料冲压参数优化可以明显缩短优化工艺参数的时间，提高工艺设计效率，同时在数值模拟试验次数一定的条件下，能获得比单纯使用正交试验和数值模拟方法更为精确的结果。
5．研究了ANN在回弹量预测方面的应用。利用神经网络的非线性映射关系，在训练样本的取值范围内，可以给定一组工艺参数得到一个回弹量，而不必重新利用CAE软件计算，为工艺制定节省时间。
本文工作的特色在于对U形件回弹作了多工艺参数综合影响分析，得出摩擦力、凹模圆角等工艺参数对于回弹量的控制与变压边力具有同等重要的作用的分析结果，有益于对冲压件回弹问题的深入探讨；将神经网络技术和正交试验法、数值模拟法相结合用于冲压工艺参数的优化，在保证分析精度一定的前提下，明显节省了工艺制定的时间，提高了工艺设计的工作效率。
Generally speaking, springback problems have always existed in sheet metal forming process. Especially during the course of sheet metal bending and low-drawing, this phenomenon is obviously serious which greatly affect precision of parts and manufacture efficiency. So it is necessary to do further research to control the springback. There is not much research work of springback control formerly, however, and experience as well as trial-and-error method is used to reduce or eliminate the springback in engineering practice. But since the 90th of last century, with the gradual solution of wrinkling and cracking in the course of drawing, springback problem has daily risen to an important research project. In addition, ceaseless perfection of CAE simulation technology of sheet-metal pressing also provides a necessary foundation for research of springback control.
The principal contents studied in this thesis are as follows:
1. Several process parameters influencing springback are comprehensively studied. Taking U-shape sheet metal in NUMISHEET'93 into account, we arrange the combination of process parameters in orthogonal experiment method and get numerical simulation of sheet metal stamping. Then, the orthogonal table is obtained. In addition, how much the different process parameters influence springback is studied, and the parameter combination is obtained to minimize the springback quantity. Then a useful conclusion can be reached: several other parameters besides variable blank holder force, such as friction, also influence springback strongly.
2. Nonlinear relationship between stamping process parameters and quantity of springback is obtained through the neural network, which accuracy is testified by the test samples.
3. Nonlinear relationship of ANN model is used to get the smooth curve of process parameters to springback quantity, so as to compensate the deficiency of the widely used polygonal chart.
4. Optimization of process parameters based on the neural network and orthogonal test is carried out. Within the range process parameters, ANN model, as the substitute of CAE numerical simulation test, combined with orthogonal test is introduced to further optimize the process parameters to make the sheet mental less. The work in paper shows: the combination of network, orthogonal test and numerical simulation may obviously lessen the time of optimizing process parameters and improve the process design efficiency. At the same time ,on condition that the time of
numerical simulation is available, the more precise conclusion can be obtained.
Less springback quantity is obtained via using both the ANN model and orthogonal experiment, comparing with only usage of the orthogonal test method.
5. Springback quantity prediction is studied. Aided with Nonlinear relationship of ANN model, within the specimen of experiment, a certain springback quantity can be attained from the given suit of parameters without reusing the CAE software, thus time of process program is saved.
本文工作主要包括以下几个方面的内容：
1．综合考察及评价了多个工艺参数对板料冲压回弹量的影响。以NUMISHEET’93标准考题中U形板料作为研究对象，通过正交表安排试验，对板料的冲压进行数值模拟获得冲压回弹量，取得试验数据。从而研究了不同工艺参数对板料回弹的影响的程度，得出结论：除变压边力外的其它工艺参数如摩擦系数、凹模圆角等参数对回弹量控制同样是起到不可忽视的作用，同时可以得到使板料回弹量最小的参数组合。
2．建立了基于神经网络的从冲压工艺参数到冲压回弹量的非线性映射关系。利用正交试验获得的数据作为神经网络的训练样本，得到输入为工艺参数、输出为冲压回弹量的神经网络模型，并通过检验样本检验了ANN(Artificial Neural Network)模型的准确性，为参数优化及回弹预测做好准备。
3．利用神经网络模型的非线性映射关系，得到板料回弹量随工艺参数变化的平滑曲线，弥补了目前普遍采用的折线图的不足。
4．基于神经网络和正交试验的工艺参数优化。在工艺参数取值范围内，采用ANN模型代替CAE软件数值模拟试验，并结合正交试验法，对工艺参数进一步优化使得板料回弹量更小。论文工作表明：将神经网络与正交试验、数值模拟三者结合用于板料冲压参数优化可以明显缩短优化工艺参数的时间，提高工艺设计效率，同时在数值模拟试验次数一定的条件下，能获得比单纯使用正交试验和数值模拟方法更为精确的结果。
5．研究了ANN在回弹量预测方面的应用。利用神经网络的非线性映射关系，在训练样本的取值范围内，可以给定一组工艺参数得到一个回弹量，而不必重新利用CAE软件计算，为工艺制定节省时间。
本文工作的特色在于对U形件回弹作了多工艺参数综合影响分析，得出摩擦力、凹模圆角等工艺参数对于回弹量的控制与变压边力具有同等重要的作用的分析结果，有益于对冲压件回弹问题的深入探讨；将神经网络技术和正交试验法、数值模拟法相结合用于冲压工艺参数的优化，在保证分析精度一定的前提下，明显节省了工艺制定的时间，提高了工艺设计的工作效率。
Generally speaking, springback problems have always existed in sheet metal forming process. Especially during the course of sheet metal bending and low-drawing, this phenomenon is obviously serious which greatly affect precision of parts and manufacture efficiency. So it is necessary to do further research to control the springback. There is not much research work of springback control formerly, however, and experience as well as trial-and-error method is used to reduce or eliminate the springback in engineering practice. But since the 90th of last century, with the gradual solution of wrinkling and cracking in the course of drawing, springback problem has daily risen to an important research project. In addition, ceaseless perfection of CAE simulation technology of sheet-metal pressing also provides a necessary foundation for research of springback control.
The principal contents studied in this thesis are as follows:
1. Several process parameters influencing springback are comprehensively studied. Taking U-shape sheet metal in NUMISHEET'93 into account, we arrange the combination of process parameters in orthogonal experiment method and get numerical simulation of sheet metal stamping. Then, the orthogonal table is obtained. In addition, how much the different process parameters influence springback is studied, and the parameter combination is obtained to minimize the springback quantity. Then a useful conclusion can be reached: several other parameters besides variable blank holder force, such as friction, also influence springback strongly.
2. Nonlinear relationship between stamping process parameters and quantity of springback is obtained through the neural network, which accuracy is testified by the test samples.
3. Nonlinear relationship of ANN model is used to get the smooth curve of process parameters to springback quantity, so as to compensate the deficiency of the widely used polygonal chart.
4. Optimization of process parameters based on the neural network and orthogonal test is carried out. Within the range process parameters, ANN model, as the substitute of CAE numerical simulation test, combined with orthogonal test is introduced to further optimize the process parameters to make the sheet mental less. The work in paper shows: the combination of network, orthogonal test and numerical simulation may obviously lessen the time of optimizing process parameters and improve the process design efficiency. At the same time ,on condition that the time of
numerical simulation is available, the more precise conclusion can be obtained.
Less springback quantity is obtained via using both the ANN model and orthogonal experiment, comparing with only usage of the orthogonal test method.
5. Springback quantity prediction is studied. Aided with Nonlinear relationship of ANN model, within the specimen of experiment, a certain springback quantity can be attained from the given suit of parameters without reusing the CAE software, thus time of process program is saved.
引文
1．张立力等 数值模拟参数和工艺参数对板材成形回弹影响的研究 锻压技术2002年第6期
2. Y.C. Liu, Springback Reduction in U-Channels— "Double-Bend" Technique, J. Applied Metalworking Vol. 3, No2, 1984
3. Y. Umehara, Technologies for the more precise press-forming of automobile parts, Journal of Materials Processing Technology 22(1990) 239-256
4. Y. Tozawa, Forming technology for raising the accuracy of sheet-formed products, Journal of Materials Processing Technology 22(1990)343-351
5. I-Nan Chou, Chinghua Hung, Finite element analysis and optimization on springback reduction, International Journal of Machine Tools & Manufacture 39(1999)517-536
6．朱东波，李涤尘等，板料成形回弹问题研究新进展，塑性工程学报，2000(1)
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8．汪晨，板料成形回弹的数值模拟及考虑回弹反向补偿的模具设计方法研究：[博士学位论文]，上海：上海交通大学，1999
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19. Daw-Kwei Leu, Simplified approach for evaluating bendability and springback in plastic bending of anisotropic sheet metals, Journal of Materials Processing Technology 66(1997)9-17
20. F. Pourboghrat, K. Chuang, O. Richmond, Hybrid membrane/shell method for rapid estimation of springback in anisotropic sheet metals, Journal of Applied Mechanics, Vol. 65, No. 3, 671-684, 1998
21. W. Johnson, T.X. Yu, On the Range of Applicability of Results for the Springback of an Elastic/Perfectly Plastic Rectangular Plate after subjecting it to Biaxial Pure Bending, Int. J. Mech. Sci., Vol. 23, No. 10, 631-637, 1981
22. A. El-Megharbel, A. El-Domiaty, M. Shaker, Springback and residual stresses after stretch bending of workhardening sheet metal, Journal of Materials Processing Technology 24(1990)191-200
23. Jang-Kyo Kim, P.F. Thomson, Friction and stress distribution in coated steel sheet during forming, Journal of Materials Processing Technology 21 (1990) 29-42
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2. Y.C. Liu, Springback Reduction in U-Channels— "Double-Bend" Technique, J. Applied Metalworking Vol. 3, No2, 1984
3. Y. Umehara, Technologies for the more precise press-forming of automobile parts, Journal of Materials Processing Technology 22(1990) 239-256
4. Y. Tozawa, Forming technology for raising the accuracy of sheet-formed products, Journal of Materials Processing Technology 22(1990)343-351
5. I-Nan Chou, Chinghua Hung, Finite element analysis and optimization on springback reduction, International Journal of Machine Tools & Manufacture 39(1999)517-536
6．朱东波，李涤尘等，板料成形回弹问题研究新进展，塑性工程学报，2000(1)
7．齐恬，板材成形回弹数值模拟和模具补偿方法的研究：[硕士学位论文]，机械科学研究院，2001
8．汪晨，板料成形回弹的数值模拟及考虑回弹反向补偿的模具设计方法研究：[博士学位论文]，上海：上海交通大学，1999
9. S.K. Kwok, W.B. Lee, The development of a machine vision system for adaptive bending of sheet metals, Journal of Materials Processing Technology 48(1995)43-49
10．张赣，控制U形件回弹的一种方法，锻压技术，1996年第2期
11．陈菁，李湘生，纪胜如等，校正弯曲回弹分析及校正力确定，金属成形工艺，1997(4)
12. L.C. Zhang, G. Lu, S.C. Leong, V-shaped sheet forming by deformable punches, Journal of Materials Processing Technology 63(1997)134-139
13．黄春风，弯曲弹性问题及其工艺控制方法，模具技术，1998(6)
14．官英平，板料校正弯曲回弹量计算，锻压技术，1998(6)
15．夏华，U形弯曲的回弹控制及模具结构，金属成形工艺，1998(2)
16．王东，风窗下梁成形工艺及模具设计，模具技术，1998(2)
17. D. Schmockel. M. Beth, Springback reduction in draw-bending process of sheet metals, Annals of the CIRP Vol. 42/1/1993
18. M. Sunseri, J. Cao, A.P. Karafillis, etc. Accommodation of springback error in channel forming using active binder force control:numerical simulations and experiments, Journal of Engineering Materials and Technology, Transactions of the ASME 118 3 Jul 1996 ASME p426-435.
19. Daw-Kwei Leu, Simplified approach for evaluating bendability and springback in plastic bending of anisotropic sheet metals, Journal of Materials Processing Technology 66(1997)9-17
20. F. Pourboghrat, K. Chuang, O. Richmond, Hybrid membrane/shell method for rapid estimation of springback in anisotropic sheet metals, Journal of Applied Mechanics, Vol. 65, No. 3, 671-684, 1998
21. W. Johnson, T.X. Yu, On the Range of Applicability of Results for the Springback of an Elastic/Perfectly Plastic Rectangular Plate after subjecting it to Biaxial Pure Bending, Int. J. Mech. Sci., Vol. 23, No. 10, 631-637, 1981
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