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

使用智能观察器、闭环控制和 3D 伺服压力机的下一代深拉伸

• 批准号: 1727490
• 负责人: Brad Kinsey
• 金额: $ 36.44万
• 依托单位: University of New Hampshire
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1727490&HistoricalAwards=false
• 关键词: Next; Generation; Deep; Drawing; Using

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.

智能工厂代表着第四次工业革命，例如，在单个过程或整个制造设施中，网络物理系统中的自动化和数据交换被用来改善过程和产品性能。随着对材料行为（包括故障）的深入了解，最先进的自动化，连接的系统以及计算时间的进步，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，以实现能够进行真实的时间过程调整的稳健、智能的板材成形过程。为了实现这一目标，新罕布什尔州大学（UNH）将与德国工业大学达姆施塔特的生产技术和成形机械（PtU）研究所合作。该奖项支持UNH进行的研究，重点是了解非均匀变形条件下金属板材行为的基本方面，预测导致材料失效的工艺条件以及失效模式的检测。由此产生的建模预测和材料的知识将被集成到独特的板成形能力在PtU的实时控制能力的评估。没有NSF基金将支持PtU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工艺），这对美国的汽车，航空航天和能源行业具有重要意义。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。本研究的目标是（i）利用3D伺服压力机的灵活性来提高金属板材部件的可成形性（ii）建立科学的理解，以识别非线性变形轨迹，以改进工艺，（iii）研究声发射（AE）传感器来预测金属板材部件的故障，以及（iv）创建智能工厂工艺实施和效益的框架。如果能够根据加工过程中材料、润滑、加工条件等的变化自动调整加工过程，则能够避免由于坯料的薄规格而引起的金属板成形中的材料失效，并且能够实现产品的尺寸精度和最终性能的改进。该研究将利用这两个机构在PtU成型机和UNH材料表征和建模方面的优势。人员交流和定期沟通将确保合作取得全面成功。

项目成果

Acoustic emission monitoring for necking in sheet metal forming

• DOI: 10.1016/j.jmatprotec.2022.117758
• 发表时间: 2022
• 期刊: Journal of Materials Processing Technology
• 影响因子: 6.3
• 作者: M. Baral;Ali Al-Jewad;A. Breunig;P. Groche;J. Ha;Y. Korkolis;B. Kinsey

Robustness of deep-drawing finite-element simulations to process variations

• DOI: 10.1007/s12289-022-01695-3
• 发表时间: 2022-05
• 期刊: International Journal of Material Forming
• 影响因子: 2.4
• 作者: Kelin Chen;A. Breunig;J. Ha;B. Kinsey;P. Groche;Y. Korkolis

Flange Wrinkling in Deep-Drawing: Experiments, Simulations and a Reduced-Order Model

• DOI: 10.3390/jmmp6040076
• 发表时间: 2022-08-01
• 期刊: JOURNAL OF MANUFACTURING AND MATERIALS PROCESSING
• 影响因子: 3.2
• 作者: Chen, Kelin;Carter, Adrian J.;Korkolis, Yannis P.
• 通讯作者: Korkolis, Yannis P.

Effectiveness of different closed-loop control strategies for deep drawing on single-acting 3D Servo Presses

单动 3D 伺服压力机拉深时不同闭环控制策略的有效性

• DOI: 10.1016/j.cirp.2022.04.072
• 发表时间: 2022
• 期刊: CIRP Annals
• 作者: Groche, Peter;Breunig, Alexander;Chen, Kelin;Molitor, Dirk A.;Ha, Jinjin;Kinsey, Brad L.;Korkolis, Yannis P.
• 通讯作者: Korkolis, Yannis P.