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板材零件的成形性，(Ii)建立科学的认识，以识别引起非线性变形的三维压边机(BH)运动--用于材料成形性改进的路径，(Iii)确定足够的传感器/观察器结构来预测板材零件的起皱和撕裂故障，以及(Iv)为Industry 4.0工艺实施和效益创建一个框架。借助于根据材料和工艺条件的变化实时自动调整工艺，可以避免板材失效，可以改善产品的尺寸精度和最终性能，并最终降低废品率。这项研究将利用这两个机构在PTU的成型机和UNH的材料特性和建模方面的优势。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。在这项研究中，将研究Industry 4.0组件，即传感器/观察器、控制系统和执行器，以改进板材成形。将完成的具体任务包括：(T1)表征材料的行为，包括使用声发射传感器预测撕裂和起皱故障。这项任务还包括了解材料在遭受非线性变形路径时的失效情况，这种路径在板材成形过程中很常见。(T2)将建立一种新的能够实现非线性BH运动的拉深工艺，并配备控制和观察系统。特殊设计的模具将允许通过独特的3D伺服压力机的非线性BH运动来实现各种非线性变形路径。(T3)进行数值模拟，以确定有益的离线非线性3D BH运动。为了改进实时过程控制，还将为选定的观察者创建和验证降阶模型。它们允许根据传感器数据的评估来预测无法通过实验测量的关键工艺参数，例如应力和应变值。(T4)将就产品特性和工艺稳健性对控制方案和系统进行比较。这些方法包括前馈和不同的闭环控制方法，以确定所需的执行机构轨迹。从这项研究中获得的知识将有助于工业4.0的努力，并改进产品设计和制造。

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.