基于JSTAMP/NV的汽车横梁回弹分析

为测试JSTAMP／NV的回弹仿真精度,以2005年国际板材成型数值模拟会议（Numisheet’2005）提出的预测汽车横梁回弹的基准考题为例,采用Y—u材料模型,基于JSTAMP／NV仿真冲压拉深成型和回弹的过程．仿真结果与试验结果吻合良好,表明利用JSTAMP／NV能有效预测汽车横梁的回弹．     

The effect of non-linear recovery on springback prediction

Numerical prediction of springback remains one of the major problems in sheet metal forming. The quality of springback prediction for a sheet metal forming process depends on a precise estimation of the stress distribution all over the metal sheet. In particular, the decrease of the unloading elastic modulus (non-linear recovery) with increasing plastic prestrain during stress reversals, observed by several authors, was commonly not taken into account in FE analysis. A model is proposed which takes the elastic modulus evolution with plastic prestrain and the unloading stress into account. Numerical simulations show that this model coupled with a non-linear kinematic hardening model can accurately predict the springback amount.     

自适应模糊神经网络在板料弯曲回弹预测中的应用

回弹是板料冲压成形中影响工件质量的重要因素，因为它是一个多变量相互作用的高度非线性问题，至今在解析和数值方法中未能找到一个很有效的解决途径。该文提出利用自适应模糊神经网络(ANFIS)对非线性问题的良好逼近能力，采用基于有限元方法获得训练样本，经训练后得到具有回弹预测能力的ANFIS模型。实验验证了该方法的有效性。     

Internal pressure and counterpunch action design in Y-shaped tube hydroforming processes: A multi-objective optimisation approach

In sheet metal forming most of the problems are multi-objective problems, generally characterised by conflicting objectives. A classical approach to investigate such kind of problems is focused on a combination of multiple objectives into a unique objective function to be optimised. Actually, in metal forming processes optimisation two main phases have to be developed in order to reach an optimal solution: the former is the modelling phase (definition of the design variables and objective function) and the latter concerns the computational aspect (numerical simulations or experiment to be developed). In this paper, an integration between numerical simulations, response surface methodology and Pareto optimal solution search techniques was applied in order to design a complex Y shaped tube hydroforming. In particular, the calibration of internal fluid pressure and counterpunch action was investigated with the aim to achieve three different quality objectives: minimization of thinning, reduction of underfilling and accuracy of the final fillet radius at the bulge zone corner.     

Computer-aided engineering for sheet metal forming: definition of a springback quality function

Computer-aided engineering methods are extensively applied to sheet metal forming integrated design. The adoption of a new class of materials, the advanced high strength steels, has increased the occurrence of springback, and consequently the request for tools oriented to springback reduction and optimization. This paper presents an approximated formulation to compute the springback field after stamping through the finite element analysis of the process. This can be found assuming that the residual field of nodal forces after stamping produces a springback shape referable to a linear combination of n modes of vibration of the nominal shape of the component. The aim of this formulation is not that of substituting the finite element analysis of the springback but rather to make use of the coefficients of the linear combination, so to define a global quality function for springback. In this way, Robust Design methods or other current optimization procedures to improve the stamping process as for structural defects (such wrinkling, necking and flatness) can be applied also for the reduction of springback. The meaning of these coefficients will be shown through three test cases and the consistency of the formulation will be discussed according to the number of modes of vibration included in the computation.     

Reliability assessment for sheet metal forming operations

Methodology developed for reliability calculations of structures is applied to estimate reliability of sheet metal forming operations. Sheet forming operations are one of the most common technological processes but still the tool and process design is a difficult engineering problem. Product defects are often encountered in the industrial practice. Material breakage, wrinkling, shape defects due to springback are most frequent defects in sheet metal forming operations. Numerical simulation allows us to evaluate product manufacturability and predict the defects at early stages of the design process. In the paper the so-called forming limit diagrams (FLD) are used as a criterion of material breakage in the manufacturing process. A zone of a FLD where good results are guaranteed with sufficient probability is considered as safe zone. Sheet forming operations are characterized with a significant scatter of the results. This can be caused by differences that can occur in forming of each part. Small differences in the contact conditions, for instance, can lead to significant changes in the deformation state of the sheet. In reliability-like approach we try to quantify intuitive terms of probability of failure/success of forming operations given some uncertainty of parameters characterizing a forming process like friction parameters or blankholding force. Since the employment of the gradient-based reliability techniques is very much limited due to the some degree of numerical noise introduced by the explicit dynamic algorithm used to perform sheet stamping simulation the method of adaptive Monte Carlo simulations were chosen for reliability assessment.     

A cognitively based methodology for evaluating human performance in the computer-aided design task domain

This article describes a methodology for evaluating human performance in the computer aided design (CAD) task environment. The methodology is based primarily on cognitive theoretic frameworks that are consistent with processes presumed to underlie human design activities. The motivation for its development stems from rapid software and hardware advances in CAD systems and our relative lack of understanding of how these enhancements affect human design performance for (1) fundamentally different types of tasks and (2) different levels of complexity for a particular task. This methodology is currently being applied to computer aided architectural design, an area where artificial intelligence (AI), enhanced geometric modelling and other system features are being debated in terms of their usefulness in aiding the human's design activities.     

Integration of feedforward neural network and finite element in the draw-bend springback prediction

To achieve accurate results, current nonlinear elastic recovery applications of finite element (FE) analysis have become more complicated for sheet metal springback prediction. In this paper, an alternative modelling method able to facilitate nonlinear recovery was developed for springback prediction. The nonlinear elastic recovery was processed using back-propagation networks in an artificial neural network (ANN). This approach is able to perform pattern recognition and create direct mapping of the elastically-driven change after plastic deformation. The FE program for the sheet metal springback experiment was carried out with the integration of ANN. The results obtained at the end of the FE analyses were found to have improved in comparison to the measured data.     

Flexible manufacturing of sheet metal parts based on digitized-die

Digitized-die forming (DDF) is a flexible manufacturing technology through which a variety of three-dimensional sheet metal parts can be produced in a DDF system. It eliminates the need to design and produce the conventional die. The central component of DDF system is a pair of matrices of punches, the punches are controlled by computer and the desired shape of die is constructed by changing the heights of punches. Based on the flexibility of DDF, new forming processes are designed that cannot be realized in conventional stamping. In varying deformation path DDF, a sheet part is manufactured along an optimal forming path, and large deformation can be achieved for the material with poor formability. In sectional DDF, a sheet part is formed section by section, and this technique makes it possible to manufacture large-size parts in a small DDF press. A closed-loop forming system was built by combining DDF with rapid 3D-shape measurement system. It is used to compensate for material springback and improve dimensional accuracy of the formed part. And a DDF system with multi-point blankholder control system was developed to control the material flow, thereby to prevent sheet parts from wrinkling and tearing. The DDF integrated system is described, and the detailed forming procedures are explained in the paper. Typical examples are presented showing the applicability of the DDF technology.     

The use of plasmodes as a supplement to simulations: A simple example evaluating individual admixture estimation methodologies

With the advent of powerful computers, simulation studies are becoming an important tool in statistical methodology research. However, computer simulations of a specific process are only as good as our understanding of the underlying mechanisms. An attractive supplement to simulations is the use of plasmode datasets. Plasmodes are data sets that are generated by natural biologic processes, under experimental conditions that allow some aspect of the truth to be known. The benefit of the plasmode approach is that the data are generated through completely natural processes, thus circumventing the common concern of the realism and accuracy of computer simulated data. The estimation of admixture, or the proportion of an individual’s genome that originates from different founding populations, is a particularly difficult research endeavor that is well suited to the use of plasmodes. Current methods have been tested with simulations of complex populations where the underlying mechanisms such as the rate and distribution of recombination are not well understood. To demonstrate the utility of this method, data derived from mouse crosses is used to evaluate the effectiveness of several admixture estimation methodologies. Each cross shares a common founding population so that the ancestry proportion for each individual is known, allowing for the comparison of true and estimated individual admixture values. Analysis shows that the different estimation methodologies (Structure, AdmixMap and FRAPPE) examined all perform well with simple datasets. However, the performance of the estimation methodologies varied greatly when applied to a plasmode consisting of three founding populations. The results of these examples illustrate the utility of plasmodes in the evaluation of statistical genetics methodologies.     

Robust design of sheet metal forming process based on adaptive importance sampling

Optimization methods have been widely applied in sheet metal forming area. However, the existence of variations during manufacturing processes significantly may influence final product quality, rendering non-robust optimal solutions. In this paper, experiments were conducted to investigate how a stochastic behavior of noise factors affected stamping quality. Robust design models for sheet metal forming process integrated adaptive importance sampling with response surface method, in order to minimize impact of the variations and achieve reliable process parameters. Support Vector Machine with nonlinear capability in both pattern recognition and regression was adopted to map the relation between input process parameters and part quality. A cup drawing example was employed to verify the feasibility of the proposed method. Comparisons were conducted between different optimization models to demonstrate robustness of the adaptive importance sampling method. Final results showed that the stamping part quality was optimized under the specified constraint requirements.     

Deep Learning in Sheet Metal Bending With a Novel Theory-Guided Deep Neural Network

Sheet metal forming technologies have been intensively studied for decades to meet the increasing demand for lightweight metal components.To surmount the springback occurring in sheet metal forming processes,numerous studies have been performed to develop compensation methods.However,for most existing methods,the development cycle is still considerably time-consumptive and demands high computational or capital cost.In this paper,a novel theory-guided regularization method for training of deep neural networks(DNNs),implanted in a learning system,is introduced to learn the intrinsic relationship between the workpiece shape after springback and the required process parameter,e.g.,loading stroke,in sheet metal bending processes.By directly bridging the workpiece shape to the process parameter,issues concerning springback in the process design would be circumvented.The novel regularization method utilizes the well-recognized theories in material mechanics,Swift’s law,by penalizing divergence from this law throughout the network training process.The regularization is implemented by a multi-task learning network architecture,with the learning of extra tasks regularized during training.The stress-strain curve describing the material properties and the prior knowledge used to guide learning are stored in the database and the knowledge base,respectively.One can obtain the predicted loading stroke for a new workpiece shape by importing the target geometry through the user interface.In this research,the neural models were found to outperform a traditional machine learning model,support vector regression model,in experiments with different amount of training data.Through a series of studies with varying conditions of training data structure and amount,workpiece material and applied bending processes,the theory-guided DNN has been shown to achieve superior generalization and learning consistency than the data-driven DNNs,especially when only scarce and scattered experiment data are available for training which is often the case in practice.The theory-guided DNN could also be applicable to other sheet metal forming processes.It provides an alternative method for compensating springback with significantly shorter development cycle and less capital cost and computational requirement than traditional compensation methods in sheet metal forming industry.     

Optimization of variable blank holder force trajectory for springback reduction via sequential approximate optimization with radial basis function network

Springback is one of the major defects in sheet metal forming. Variable blank holder force (VBHF) approach is one of the effective ways for the springback reduction. In this paper, the VBHF trajectory is optimized to reduce the springback by a sequential approximate optimization (SAO) with radial basis function (RBF) network. The U-shaped forming in NUMISHEET’93 is employed to determine an optimum VBHF trajectory, for example. In this paper, the bending moment is taken as the objective function. The tearing of sheet during the forming is considered as the design constraint, and the forming limit diagram (FLD) is employed to evaluate the design constraint quantitatively. It has been found from numerical results that the optimal VBHF trajectory can drastically reduce the springback in comparison with various VBHF trajectories. Through the theoretical examination and numerical simulation, the springback reduction of metal forming by the VBHF trajectory is discussed.     

Expert system for simulation of metal sheet stamping

Stamping processes are frequently used in the automotive industry. In an attempt to reduce developing times and costs, conventional design and manufacturing procedures are being changed. Finite element simulations have proved to be a good help in the design and analysis of these processes. The main problem of these simulations is that they are quite difficult to perform and that there are several non-trivial questions that the user has to answer before achieving a good model. In this work, a new application has been designed. It combines the usability of a custom application with the power of such a tool as the finite element method. To design this application, a lot of questions regarding FEM simulation of stamping processes have been analyzed. The result is a methodology to automate simulations of stamping processes where the user does not need to have deep knowledge of the finite element software. Such methodology has been successfully employed in a Spanish manufacturing industry of automotive components.     

汽车薄钢板不均匀拉伸试验和仿真研究

研究汽车覆盖件拉伸优化问题,针对汽车覆盖件容易出现起皱,传统研究方法没有考虑其成形过程中与模具接触,预测精度不高的现状。根据起皱预测理论,研究单面接触对起皱的影响。提出了高强度薄钢板的对角拉伸试验仿真。首先建立了标准的YBT(Yoshida Buckling Test)仿真模型,在有限元软件LS_DYNA上验证了仿真模型的正确性。进一步考虑冲压过程中板料与模具的接触状况,建立了带有单面接触的YBT仿真模型。结果表明,标准YBT试验中的起皱的高度较大,起皱原因是压应力导致的失稳。单面接触情况下压应力的释放得到抑制,起皱高度明显减小,明确了起皱的原因是卸载回弹,为设计提供了依据。     

径向基函数网络在变压边力反演中的应用

变压边力策略是在冲压件成型中控制回弹的一种有效措施。综合运用径向基函数（RBF）神经网络和三维回弹模拟技术开展了由冲压件成形质量反求变压边力的研究, 同时研究了径向基函数神经网络的动态结构设计问题,提出一种基于泛化的径向基函数神经网络的动态结构设计方法DYNSDRBF,编制了相应的计算程序。DYNSDRBF方法在变压边力反演神经网络设计中的应用结果表明,运用该方法设计的神经网络具有较好的计算精度,可有效地提高冲压件的成形质量。     

基于模式化知识和人工神经网络的智能设计系统框架研究

阐述了将神经网络和模式识别引入智能设计系统的模型－集成转换模型,在该模型中,对智能系统与设计的实现系统的结合进行了探讨,提出了数值编译器与反编译器、图形／编码转换器等将神经网络引入设计系统来的工具和方法。     

Using support vector machines for the computationally efficient identification of acceptable design parameters in computer-aided engineering applications

This paper addresses the problem of estimating continuous boundaries between acceptable and unacceptable engineering design parameters in complex engineering applications. In particular, a procedure is proposed to reduce the computational cost of finding and representing the boundary. The proposed methodology combines a low-discrepancy sequence (Sobol) and a support vector machine (SVM) in an active learning procedure able to efficiently and accurately estimate the boundary surface. The paper describes the approach and methodological choices resulting in the desired level of boundary surface refinement and the new algorithm is applied to both two highly-nonlinear test functions and a real-world train stability design problem. It is expected that the new method will provide designers with a tool for the evaluation of the acceptability of designs, particularly for engineering systems whose behaviour can only be determined through complex simulations.     

一种基于改进傅里叶变换的回弹补偿算法研究*

针对回弹规律比较复杂的冲压件中高精度的要求,提出了一种基于改进傅里叶变换的回弹闭环控制系统模型。首先,从产品模型出发利用有限元分析、成形工艺参数优化等方法进行初始模具设计;然后对模具及冲压产品进行测量,评价回弹误差;最后通过两个迭代循环来基本消除回弹误差,完成最后模具修正补偿。实验证明,利用该模型可以有效地完成对模具形状修正,实现了复杂冲压件的回弹补偿。     

Response surface methodology for design of sheet forming parameters to control springback effects

This paper deals with the optimization of tools geometry in sheet metal forming in order to reduce the springback effects after forming. A response surface method (RSM) based on diffuse approximation is used; this technique has been proved more efficient than classical gradient based methods since it requires fewer iterations and convergence is guaranteed especially for nonlinear problems. A new improved Inverse Approach for the stamping simulation based on DKTRF shell element is presented. In the new version, the strains and stresses due to bending and unbending effects are calculated analytically from the final workpiece, especially on the die entrance radii for curvature changes. The bending/unbending moments and the final shape are used to calculate springback using a second incremental Approach based on the Updated Lagrangian Formulation. The benchmark on the “U” bending problem of NUMISHEET’93 has been used to validate the method, good results on the elimination of springback have been obtained. The final results are validated using STAMPACK^® and ABAQUS^® commercial codes.