多工位高速锻造工艺智能集成优化技术研究
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摘要
多工位高速锻造是一种先进的近净成形工艺，由于该工艺在技术、经济效益上的优越性，当前在制造业中得到了广泛应用。多工位高速锻造的成形工位多，各工位之间关联密切，对工艺和模具的设计要求也较高。不合理的预成形工位模具设计往往会造成工位载荷分配不合理，成形载荷过大以及折叠、充填不饱满等成形缺陷。长期以来，为了得到合理的设计方案，设计人员往往要反复对设计方案进行试模或者数值模拟，在试模结果的基础上根据经验修改原有的设计方案。这种传统的试错优化模式，主要依靠设计人员的主观经验和直观判断，具有很大的随意性和局限性，很难获得最优的设计方案，造成资源和时间的浪费，无法适应现代生产的要求。随着社会经济的快速发展和企业竞争的日益加剧，对多工位高速锻造的产品开发提出了高效率、高质量和低成本的要求，迫切需要将先进的优化理论与方法引入多工位高速锻造工艺优化过程中，将设计人员从繁重的反复试凑工作中解脱出来。因此，将智能技术、数值模拟技术与最优化技术引入多工位高速锻造工艺与模具的优化中是一个必然的趋势。本文与瑞士哈特贝尔公司(HATEBUR)合作开展了研究工作，并取得了以下研究成果：
针对多工位高速锻造优化问题所具有的各工位之间关联密切、约束与目标众多、响应值与设计变量无显式关系，并且全过程模拟时间长，复杂零件不能二维简化的特点，提出了基于近似替代模型的组合优化策略。首先采用正交试验设计和方差分析对设计变量进行显著性分析，去除不重要的设计变量，降低优化问题的规模；之后应用拉丁超立方采样方法获取样本点，构建设计变量和响应值(优化目标、约束条件)的近似替代模型；最后，基于近似替代模型，应用惩罚策略构造评价函数，将复杂的多约束非线性工程优化问题转化为求解评价函数的单目标优化问题，应用遗传算法寻找全局最优解。在优化计算过程中，利用近似替代模型快速预测响应值，而无需进行数值模拟，从而显著节省了优化时间。
对各种试验设计方法和近似模型建模方法进行了研究和比较。针对多工位高速锻造工艺优化，提出采用拉丁超立方采样和Kriging模型的组合建立数值变量型响应的近似模型，采用BP神经网络建立语言变量型响应的近似模型。分别采用正交试验设计和拉丁超立方采样2种试验设计方法以及二次响应面模型、最小二乘响应面模型、BP神经网络模型和Kriging模型4种近似建模方法建立实际案例的设计变量与响应值之间关系的近似替代模型，并对近似模型的预测精度进行了比较。
以实现“设计改进－响应反馈”过程的智能集成和自动化为目标，研究了CAD/CAE智能集成的关键技术，从而减少繁琐的人机交互操作，为优化提供一个智能化的集成平台。其中包括模具几何模型根据设计变量自动修改，智能建立有限元分析模型以及从数值模拟结果自动提取响应值。提出了基于优化案例模型和型腔参数化模型模板的知识集成模式。通过优化案例模型，实现设计变量自动对应到同一或不同工位的模具几何模型上的形状参数，并将优化信息和几何模型进行集成；基于型腔参数化模板，应用模具几何模型自动获取技术实现设计变量到模具几何模型的自动映射，并保证了模具之间的约束与装配关系。提出了基于有限元分析模板的CAE模型智能建模技术。实现CAE分析模型的智能建模和修改，自动完成多工位高速锻造过程的连续模拟。提出了基于知识的CAE分析结果智能反馈技术。应用知识提取和模型智能重构，对数值模拟结果中的数据进行智能地分析并自动加以转化，实现了模拟结果到响应值(优化目标、约束条件)的自动提取与反馈，从而实现了“设计改进－响应反馈”过程的智能集成和自动化。
在基于近似替代模型的组合优化策略和CAD/CAE智能集成技术研究的基础上，本文基于UG NX和DEFORM软件平台，利用Visual C++.net及MATLAB、EXCEL等软件开发了多工位高速锻造工艺智能优化系统。将基于近似替代模型的优化流程和相关知识集成到多工位高速锻造工艺智能优化系统中，在优化过程中，系统提供有效的智能支持，引导设计者完成多工位高速锻造工艺优化任务。通过对轴承套圈的多工位高速锻造工艺优化实例分析，验证了该系统的实用性和可靠性。
基于智能优化系统，应用多工位高速锻造智能集成优化技术分别对二维热锻案例（轴承套圈锻件和齿坯锻件）和三维冷锻案例（小齿轮锻件）进行了优化，优化效果明显。与初始设计方案相比，有效地降低了成形载荷，减少了成形缺陷，从而验证了本文提出的多工位高速锻造工艺智能集成优化技术的有效性和正确性。
The high-speed multi-station forging process is a kind of advanced near net-shape forgingprocess. Due to the advantages of technology and economic benefits, it is widely applied inmanufacturing industry. Because it owns a series of forming stations which have closerelationships, the requirement of process design is very high. Improper design of die shapes ofpreform stations often results in too high forming load, unreasonable distribution of load andforming defects, such as folding and underfill. For a long time, in order to obtain the properdesign, designers often have to test or simulate the process design, and then optimize andredesign by experience iteratively. The trial-and-error optimization method depends on thedesign’s subjective experience and intuitive determination. It is random and inefficient, so it isdifficult to get the optimal design. With the rapid development of society and the increasingcompetition of enterprises, it is required to achieve the goals of process design for highefficiency, high quality and low cost. Thus, the application of advanced theory and method isdesired in the process of optimization to free the designers from the heavy work oftrial-and-error process. As a result, the intelligent technology, the numerical simulationtechnology and the optimization technology are necessary to be applied in the optimization formulti-station forging. In cooperation with HATEBUR, the following research results areobtained in the thesis.
A combined optimization strategy based on surrogate model is proposed for theoptimization of high-speed multi-station forging process, which has the characteristics of theclose relationship between forming stations, lots of optimization goals and constraints, noexplicit expression between response and variables, long simulation time of the whole processand the complex parts that can not be simplified into2D. The combined optimization strategyapplies the techniques of orthogonal experiment design and analysis of variance (ANOVA) toevaluate the initial variables at first. According to the significance, the unimportant variables areejected to reduce scale of the optimization problem. After that, the latin hypercube sampling(LHS) method is applied to obtain the samples and the surrogate models are constructed toapproximate the relationships between variables and responds (objective functions andconstraints). Based on these surrogate models, an evaluation function is constructed by penaltymethod. In this way, the complex constrained nonlinear optimization problem can be changedinto a single-objective optimization problem of the evaluation function. Finally, the globaloptimization algorithm is applied to search the optimal solution. In the calculation process, theestablished surrogate models are used to predict the response value quickly instead of the numerical simulation. Therefore, it can reduce the optimization time significantly.
Different DOE methods and surrogate modeling methods for approximate models arestudied and compared. For the optimization of high-speed multi-station forging process, thecombination of LHS and Kriging is proposed for the case of numerical response value, and BPnetwork is used for the case of linguistic response value. Two DOE methods, which includeorthogonal experimental design and LHS, together with four modeling methods, which includeleast square (LS) response surface, moving least square (MLS) response surface, BP neuralnetwork and Kriging model, are applied to establish the approximate relationship modelsbetween variables and response values of an actual case. And the prediction accuracies of thedifferent models are compared.
In order to automatically obtain the response values corresponding to the modification ofdesign, the CAD/CAE intelligent integration technique is proposed in this thesis. It provides anintelligent integration platform for optimization to reduce the complex interactive operation. Itcontains the processes of the automatic modification of tools’ geometry models according tovariables, the intelligent modeling of CAE analysis model and the automatic acquisition andfeedback of the CAE simulation result. The knowledge integration method based on template ofparameterized geometry model and optimization case model is proposed. Through theoptimization case model, variables are corresponding to the parameters of tools’ geometrymodels in one or different forming stations automatically, and the highly integration ofoptimization information and geometry information is realized. Based on the parameterizedgeometry template of cavity, the automatic acquisition technique is applied to obtain the tools’geometry models according to the values of variables, which ensure the correct constraints andassembly relationships between tools. The knowledge based CAE intelligent modeling techniqueis proposed. It achieves the intelligent establishment and automatic modification of CAEanalysis model, so that the simulation of the forging process is carried out by sequence ofstations automatically. The knowledge based intelligent post simulation feedback technology isproposed. Based on knowledge acquisition and intelligent model reconstruction, the usefulinformation is analyzed and transformed from the CAE simulation result. Therefore, theautomatic mapping process from simulation result to response values (objective functions andconstraints) is implemented. Further more, the intelligent integration and automatic process,feedback of response values according to the modification of variables, is implemented.
Based on the studies of surrogate model based combined optimization strategy andCAD/CAE intelligent integration technique, an intelligent optimization system for high-speedmulti-station forging process is developed on the platform of UG NX and DEFORM by usingthe software of Visual C++.net, MATLAB and EXCEL. The surrogate model based optimizationmethod and related knowledge is integrated in the intelligent optimization system, which guidesdesigners to complete the optimization task of high-speed multi-forging process. Meanwhile, theintelligent system provides effective intelligent support in the process of optimization. Theoptimization case study of combring produced by high-speed multi-station forging hasdemonstrated the reliability and practicality of the intelligent optimization system.
Based on the intelligent optimization system, the intelligent integration optimizationtechnology is applied to optimize the2D hot forging cases, which include combring and gearblank, and3D cold forging case of gear. After optimization, the forging process is improvedsignificantly. As compared to the initial design, the maximum forming load is decreased, and theforming quality is improved without defect. The effectivity and rationality of the intelligentintegration optimization technology are proved.
针对多工位高速锻造优化问题所具有的各工位之间关联密切、约束与目标众多、响应值与设计变量无显式关系，并且全过程模拟时间长，复杂零件不能二维简化的特点，提出了基于近似替代模型的组合优化策略。首先采用正交试验设计和方差分析对设计变量进行显著性分析，去除不重要的设计变量，降低优化问题的规模；之后应用拉丁超立方采样方法获取样本点，构建设计变量和响应值(优化目标、约束条件)的近似替代模型；最后，基于近似替代模型，应用惩罚策略构造评价函数，将复杂的多约束非线性工程优化问题转化为求解评价函数的单目标优化问题，应用遗传算法寻找全局最优解。在优化计算过程中，利用近似替代模型快速预测响应值，而无需进行数值模拟，从而显著节省了优化时间。
对各种试验设计方法和近似模型建模方法进行了研究和比较。针对多工位高速锻造工艺优化，提出采用拉丁超立方采样和Kriging模型的组合建立数值变量型响应的近似模型，采用BP神经网络建立语言变量型响应的近似模型。分别采用正交试验设计和拉丁超立方采样2种试验设计方法以及二次响应面模型、最小二乘响应面模型、BP神经网络模型和Kriging模型4种近似建模方法建立实际案例的设计变量与响应值之间关系的近似替代模型，并对近似模型的预测精度进行了比较。
以实现“设计改进－响应反馈”过程的智能集成和自动化为目标，研究了CAD/CAE智能集成的关键技术，从而减少繁琐的人机交互操作，为优化提供一个智能化的集成平台。其中包括模具几何模型根据设计变量自动修改，智能建立有限元分析模型以及从数值模拟结果自动提取响应值。提出了基于优化案例模型和型腔参数化模型模板的知识集成模式。通过优化案例模型，实现设计变量自动对应到同一或不同工位的模具几何模型上的形状参数，并将优化信息和几何模型进行集成；基于型腔参数化模板，应用模具几何模型自动获取技术实现设计变量到模具几何模型的自动映射，并保证了模具之间的约束与装配关系。提出了基于有限元分析模板的CAE模型智能建模技术。实现CAE分析模型的智能建模和修改，自动完成多工位高速锻造过程的连续模拟。提出了基于知识的CAE分析结果智能反馈技术。应用知识提取和模型智能重构，对数值模拟结果中的数据进行智能地分析并自动加以转化，实现了模拟结果到响应值(优化目标、约束条件)的自动提取与反馈，从而实现了“设计改进－响应反馈”过程的智能集成和自动化。
在基于近似替代模型的组合优化策略和CAD/CAE智能集成技术研究的基础上，本文基于UG NX和DEFORM软件平台，利用Visual C++.net及MATLAB、EXCEL等软件开发了多工位高速锻造工艺智能优化系统。将基于近似替代模型的优化流程和相关知识集成到多工位高速锻造工艺智能优化系统中，在优化过程中，系统提供有效的智能支持，引导设计者完成多工位高速锻造工艺优化任务。通过对轴承套圈的多工位高速锻造工艺优化实例分析，验证了该系统的实用性和可靠性。
基于智能优化系统，应用多工位高速锻造智能集成优化技术分别对二维热锻案例（轴承套圈锻件和齿坯锻件）和三维冷锻案例（小齿轮锻件）进行了优化，优化效果明显。与初始设计方案相比，有效地降低了成形载荷，减少了成形缺陷，从而验证了本文提出的多工位高速锻造工艺智能集成优化技术的有效性和正确性。
The high-speed multi-station forging process is a kind of advanced near net-shape forgingprocess. Due to the advantages of technology and economic benefits, it is widely applied inmanufacturing industry. Because it owns a series of forming stations which have closerelationships, the requirement of process design is very high. Improper design of die shapes ofpreform stations often results in too high forming load, unreasonable distribution of load andforming defects, such as folding and underfill. For a long time, in order to obtain the properdesign, designers often have to test or simulate the process design, and then optimize andredesign by experience iteratively. The trial-and-error optimization method depends on thedesign’s subjective experience and intuitive determination. It is random and inefficient, so it isdifficult to get the optimal design. With the rapid development of society and the increasingcompetition of enterprises, it is required to achieve the goals of process design for highefficiency, high quality and low cost. Thus, the application of advanced theory and method isdesired in the process of optimization to free the designers from the heavy work oftrial-and-error process. As a result, the intelligent technology, the numerical simulationtechnology and the optimization technology are necessary to be applied in the optimization formulti-station forging. In cooperation with HATEBUR, the following research results areobtained in the thesis.
A combined optimization strategy based on surrogate model is proposed for theoptimization of high-speed multi-station forging process, which has the characteristics of theclose relationship between forming stations, lots of optimization goals and constraints, noexplicit expression between response and variables, long simulation time of the whole processand the complex parts that can not be simplified into2D. The combined optimization strategyapplies the techniques of orthogonal experiment design and analysis of variance (ANOVA) toevaluate the initial variables at first. According to the significance, the unimportant variables areejected to reduce scale of the optimization problem. After that, the latin hypercube sampling(LHS) method is applied to obtain the samples and the surrogate models are constructed toapproximate the relationships between variables and responds (objective functions andconstraints). Based on these surrogate models, an evaluation function is constructed by penaltymethod. In this way, the complex constrained nonlinear optimization problem can be changedinto a single-objective optimization problem of the evaluation function. Finally, the globaloptimization algorithm is applied to search the optimal solution. In the calculation process, theestablished surrogate models are used to predict the response value quickly instead of the numerical simulation. Therefore, it can reduce the optimization time significantly.
Different DOE methods and surrogate modeling methods for approximate models arestudied and compared. For the optimization of high-speed multi-station forging process, thecombination of LHS and Kriging is proposed for the case of numerical response value, and BPnetwork is used for the case of linguistic response value. Two DOE methods, which includeorthogonal experimental design and LHS, together with four modeling methods, which includeleast square (LS) response surface, moving least square (MLS) response surface, BP neuralnetwork and Kriging model, are applied to establish the approximate relationship modelsbetween variables and response values of an actual case. And the prediction accuracies of thedifferent models are compared.
In order to automatically obtain the response values corresponding to the modification ofdesign, the CAD/CAE intelligent integration technique is proposed in this thesis. It provides anintelligent integration platform for optimization to reduce the complex interactive operation. Itcontains the processes of the automatic modification of tools’ geometry models according tovariables, the intelligent modeling of CAE analysis model and the automatic acquisition andfeedback of the CAE simulation result. The knowledge integration method based on template ofparameterized geometry model and optimization case model is proposed. Through theoptimization case model, variables are corresponding to the parameters of tools’ geometrymodels in one or different forming stations automatically, and the highly integration ofoptimization information and geometry information is realized. Based on the parameterizedgeometry template of cavity, the automatic acquisition technique is applied to obtain the tools’geometry models according to the values of variables, which ensure the correct constraints andassembly relationships between tools. The knowledge based CAE intelligent modeling techniqueis proposed. It achieves the intelligent establishment and automatic modification of CAEanalysis model, so that the simulation of the forging process is carried out by sequence ofstations automatically. The knowledge based intelligent post simulation feedback technology isproposed. Based on knowledge acquisition and intelligent model reconstruction, the usefulinformation is analyzed and transformed from the CAE simulation result. Therefore, theautomatic mapping process from simulation result to response values (objective functions andconstraints) is implemented. Further more, the intelligent integration and automatic process,feedback of response values according to the modification of variables, is implemented.
Based on the studies of surrogate model based combined optimization strategy andCAD/CAE intelligent integration technique, an intelligent optimization system for high-speedmulti-station forging process is developed on the platform of UG NX and DEFORM by usingthe software of Visual C++.net, MATLAB and EXCEL. The surrogate model based optimizationmethod and related knowledge is integrated in the intelligent optimization system, which guidesdesigners to complete the optimization task of high-speed multi-forging process. Meanwhile, theintelligent system provides effective intelligent support in the process of optimization. Theoptimization case study of combring produced by high-speed multi-station forging hasdemonstrated the reliability and practicality of the intelligent optimization system.
Based on the intelligent optimization system, the intelligent integration optimizationtechnology is applied to optimize the2D hot forging cases, which include combring and gearblank, and3D cold forging case of gear. After optimization, the forging process is improvedsignificantly. As compared to the initial design, the maximum forming load is decreased, and theforming quality is improved without defect. The effectivity and rationality of the intelligentintegration optimization technology are proved.
引文
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