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

University Of New Hampshire, Durham NH

Investigators

Abstract

Smart factories represent the fourth industrial revolution where, for example, automation and data exchange in cyber-physical systems, with respect to a single process or across an entire manufacturing facility, are exploited to improve processes and product performance. With enhanced understanding of material behavior (including failure), state-of-the-art automation, connected systems, and advances in computational time, such smart factories are attainable. This award seeks to implement smart factory principles to realize a robust, intelligent sheet forming process capable of making real time process adjustments. To achieve this goal the University of New Hampshire (UNH) will collaborate with the Production Technology and Forming Machines (PtU) Institute at the Technische Universität Darmstadt in Germany. This award, which supports the research undertaken by UNH, centers on understanding the fundamental aspects of sheet metal behavior under non-uniform deformation conditions, the prediction of process conditions leading to material failure, and the sensing of failure modes. The resulting modeling predictions and material's knowledge will be integrated into the unique sheet forming capabilities at PtU for evaluation of real-time control capabilities. No NSF funds will support PtU activities. Success will translate into higher processing capabilities (more efficient processes capable of processing a wider range of materials) which is of great significance to US based automotive, aerospace, and energy based industries. Planned personnel exchanges will provide exceptional educational and cultural opportunities for the researchers involved in the project. The objectives of this research 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 deformation trajectories for process improvement, (iii) investigate an acoustic emissions (AE) sensor to predict failure in sheet metal components, and (iv) create a framework for smart factory process implementation and benefits. If processes can automatically be adjusted based on variations in the material, lubrication, process conditions, etc. as the process progress, failure of the material, which is a concern in sheet metal forming due to the thin gauge of the blanks, can be avoided, and improvements in the dimensional accuracy and final properties of product can be achieved. The research will capitalize on the strengths of the two institutions with respect to forming machine at PtU and material characterization and modeling at UNH. Personnel exchanges and regular communications will assure the overall success of the collaboration.