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

• Applicant: Professor Dr.-Ing. Peter Groche
• Subject Area: Primary Shaping and Reshaping Technology, Additive Manufacturing
• Term: from 2017 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 386415239

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.

• DFG Programme: Research Grants
• International Connection: USA
• Cooperation Partners: Professor Brad Kinsey, Ph.D.; Professor Yannis Korkolis, Ph.D.