Next Generation Deep Drawing Using Smart Observers, Close-Loop Control, and 3D-Servo-Press

该合作研究项目的目标是：(i)利用3D伺服压力机的灵活性来提高钣金部件的可成形性；（ii）建立科学的理解，以识别非线性3D压边机（BH）运动，导致材料的非线性变形路径，以提高材料的可成形性；（iii）确定适当的传感器/观察者结构，以预测钣金部件的起皱和撕裂故障。（iv）为工业4.0流程实施和效益创建框架。借助根据材料和工艺条件的变化对工艺进行自动调整，可以避免金属板的实时失效，提高产品的尺寸精度和最终性能，并最终降低报废率。该研究将利用两个机构在PtU的成型机和UNH的材料表征和建模方面的优势。人员交流将为参与的研究人员提供特殊的教育和文化机会，并将确保合作的成功。在本研究中，将研究工业4.0组件，即传感器/观察者，控制系统和执行器，以改善钣金成形。将完成的具体任务是：（T1）表征材料行为，包括使用声发射传感器预测撕裂和起皱失效。这项任务还包括理解材料在非线性变形路径下的失效，这在钣金成形过程中很常见。（T2）将建立一种新颖的深拉深工艺，实现非线性bh运动，并配备控制和观察系统。特别设计的工具将允许通过独特的3d伺服压力机的非线性BH运动实现各种非线性变形路径。（T3）进行数值模拟，以确定有益的非线性三维黑洞离线运动。为了改进实时过程控制，还将创建和验证选定观察者的降阶模型。它们允许预测无法通过实验测量的关键工艺参数，例如，基于传感器数据评估的应力和应变值。（T4）控制方案和系统将在产品性能和过程鲁棒性方面进行比较。这些包括前馈和不同的闭环控制方法，以确定所需的执行器轨迹。从这项研究中获得的知识将有利于工业4.0的努力，并改善产品的设计和制造。

The objectives of this collaborative research project are to (i) exploit the flexibility of a 3D-servo-press to improve the formability of sheet metal components, (ii) establish the scientific understanding to identify non-linear 3D blank holder (BH) movements causing non-linear deformation-paths for material formability improvements, (iii) determine adequate sensor/observer structures to predict wrinkling and tearing failures in sheet metal components, and (iv) create a framework for Industry 4.0 process implementation and benefits. With the aid of the automatic adjustment of processes with respect to variations in the material and process conditions in real time failure sheet metal can be avoided, improvements in the dimensional accuracy and final properties of products can be achieved, and ultimately scrap-rates can be reduced. The research will capitalize on the strengths of the two institutions with respect to forming machines at PtU and material characterization and modeling at UNH. Personnel exchanges will provide exceptional educational and cultural opportunities for the researchers involved and will assure the success of the collaboration. In this research, Industry 4.0 components, i.e. sensors/observers, control systems, and actuators, will be investigated to improve sheet metal forming. The specific tasks that will be completed are: (T1) Characterize the material behavior, including the predictions of both tearing and wrinkling failures using an acoustic emission sensor. This task also includes understanding failure in the material when subjected to non-linear deformation-paths which are common in sheet metal forming processes. (T2) A novel deep drawing process enabling non-linear BH-movements will be established and equipped with control and observer systems. Specially designed tooling will allow various non-linear deformation-paths to be achieved through non-linear BH movements of a unique 3D-servo-press. (T3) Conduct numerical simulations to determine beneficial non-linear 3D BH movements offline. For improved real-time process control, reduced-order models for selected observers will also be created and validated. They allow for predicting key process parameters that cannot be measured experimentally, e.g., stress and strain values, based on an evaluation of sensor data. (T4) Control schemes and systems will be compared with respect to product properties and process robustness. These include feed-forward and different closed-loop control approaches to determine the desired actuator trajectories.The knowledge gained from this research will benefit Industry 4.0 efforts and improve product design and manufacturing.