Design Method for Forming Tools for Rotary Draw Bending of Bend-in-Bend Geometries

弯中弯几何形状旋转拉弯成型工具的设计方法

• 批准号: 520256321
• 负责人: Professor Dr.-Ing. Bernd Engel
• 金额: --
• 依托单位: Lehrstuhl Ubiquitous Computing
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/520256321?language=en
• 关键词: Design; Method; Forming; Tools; Rotary

Rotary draw bending (RDB) is widely used for production of bent tubes or pipes with small radii. The resulting geometries are generated by the contact surfaces of tools. These contact surfaces of the forming tools are currently designed using finite element (FE) simulations as well as knowledge of specialists. The results of the real forming processes are usually different from calculated geometries because some physical effects are unaccounted for the simulation model. Especially their interactions and transient behavior during forming are neglected. This is more important for bend-in-bend geometries in which a sequence of two bends lined up without a straight intermediate part. For the second bend, the contour of the first defines the geometry of the cavity of the second tool. Even small imprecisions of the first bow lead to bigger geometrical deviations of the product. The goal of this research project is to reach a higher process stability by a different tool design. The method is to monitor sensor data of the process during a large quantity of product cycles and design a model of the forming process using this database. Additional sensors will be integrated into the forming tools and the bending machine to get a virtual image of the process. The goal of the first funding period (1st FÖP) is the digital representation (research focus A) and the data-driven, explainable model of the bending process (research focus C). Target values are the components geometries with regard to shape and position deviations of the bends as well as the component contour in the potential area of wrinkling. The Chair of Forming Technology (UTS) is responsible for the numerical modeling of the bending process, the determination of sensitive process variables that lead to fluctuating quality characteristics of the component, the selection and implementation of suitable sensors in the forming system and the development of automated, time-synchronized measuring peripherals. Furthermore, a method for parameterizing the tools geometries with respect of forming system parameters and real product geometries, is being developed. This forms the basis for the design of improved contact surface designs, using the data-driven process modeling in the 2nd FÖP. The Chair of Ubiquitous Computing (IUC) is developing the digitization of the process in the form of a data pipeline that, with regards to the 2nd FÖP, is able to combine the process data, expert knowledge and simulation models. This particularly requires an adaptation of data-driven and visual models for the RDB, specific feature engineering as well as classification and regression processes which will be based on random forest methods. The results of the work are a collection of comprehensible reference data sets with the data-based representation of the process sequence and first adaptations of explainable models in combination with domain knowledge and the analysis results learned.

旋转拉弯（RDB）广泛用于生产弯管或小半径管道。由此产生的几何形状是由工具的接触表面生成的。目前，成形工具的这些接触表面是使用有限元 (FE) 模拟以及专家的知识来设计的。实际成形过程的结果通常与计算的几何形状不同，因为仿真模型未考虑一些物理效应。特别是它们在成形过程中的相互作用和瞬态行为被忽略。这对于弯中弯几何形状更为重要，在弯中弯几何形状中，两个弯头排列在一起，而没有直的中间部分。对于第二个弯曲，第一个弯曲的轮廓限定了第二个工具的空腔的几何形状。即使第一弓的微小不精确性也会导致产品更大的几何偏差。该研究项目的目标是通过不同的工具设计达到更高的工艺稳定性。该方法是在大量产品周期期间监测过程的传感器数据，并利用该数据库设计成形过程的模型。 额外的传感器将集成到成型工具和折弯机中，以获得过程的虚拟图像。第一个资助期 (1st FÖP) 的目标是数字表示（研究重点 A）和数据驱动的、可解释的弯曲过程模型（研究重点 C）。目标值是关于弯曲的形状和位置偏差的部件几何形状以及潜在起皱区域的部件轮廓。成形技术主席 (UTS) 负责弯曲过程的数值建模、确定导致部件质量特性波动的敏感过程变量、在成形系统中选择和实施合适的传感器以及开发自动化、时间同步的测量外围设备。此外，正在开发一种根据成形系统参数和实际产品几何形状参数化工具几何形状的方法。这构成了使用第二个 FÖP 中的数据驱动过程建模改进接触面设计的基础。普适计算 (IUC) 主席正在以数据管道的形式开发过程数字化，就第二个 FÖP 而言，该数据管道能够将过程数据、专家知识和仿真模型结合起来。这特别需要适应 RDB 的数据驱动和可视化模型、特定的特征工程以及基于随机森林方法的分类和回归过程。工作的结果是一系列可理解的参考数据集，其中包含基于数据的流程序列表示以及可解释模型的首次调整，并结合领域知识和学到的分析结果。