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

智能工厂代表着第四次工业革命，在这场革命中，例如，针对单个流程或整个制造设施，利用网络物理系统中的自动化和数据交换来改进流程和产品性能。随着对材料行为(包括失效)的更好理解、最先进的自动化、互联系统和计算时间的进步，这样的智能工厂是可以实现的。该奖项旨在实施智能工厂原则，以实现能够进行实时工艺调整的稳健、智能的板材成形工艺。为了实现这一目标，新汉普郡大学(UNH)将与德国达姆施塔特理工学院的生产技术与成形机械研究所(PTU)合作。该奖项支持UNH开展的研究，重点是了解非均匀变形条件下板材行为的基本方面，预测导致材料失效的工艺条件，以及检测失效模式。由此产生的建模预测和材料知识将被集成到PTU独特的板材成形能力中，以评估实时控制能力。没有任何NSF资金将支持PTU的活动。成功将转化为更高的加工能力(能够加工更广泛材料的更高效的工艺)，这对美国的汽车、航空航天和基于能源的行业具有重要意义。计划中的人员交流将为参与该项目的研究人员提供特殊的教育和文化机会。这项研究的目标是：(I)利用3D伺服压力机的灵活性来改善板材零件的成形性；(Ii)建立科学的认识，以识别用于工艺改进的非线性变形轨迹；(Iii)研究声发射(AE)传感器以预测板材零件的故障；以及(Iv)创建智能工厂工艺实施和效益的框架。如果随着工艺的进展，可以根据材料、润滑、工艺条件等的变化自动调整工艺，则可以避免由于毛坯的薄规格而引起的板料成形中的材料失效，并且可以实现产品的尺寸精度和最终性能的改善。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。人员交流和定期沟通将确保合作的全面成功。

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.