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Dynamical Systems Diagnostics for Intelligent Machine Tools

智能机床动态系统诊断

基本信息

批准号：
2053470
负责人：
Brian Mann
金额：
$ 35.81万
依托单位：
Duke University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2053470&HistoricalAwards=false
关键词：
Dynamical Systems Diagnostics Intelligent Machine

项目摘要

This grant will support research that will contribute to new knowledge related to a manufacturing process, promoting both the progress of science and advancing national prosperity. Subtractive manufacturing processes make three-dimensional parts and objects by removing material in small layers. Machining is one of the more pervasive subtractive processes and includes material removal methods known as milling, drilling, broaching, and turning. Each of these machining processes uses a cutting tool to remove material layers from a bulk material and to leave behind a desired three-dimensional part. These material removal processes are also one of the most widely used approaches to create metal, wood, and plastic parts for the automotive, aerospace, medical device industries. However, the accuracy, surface quality, and productivity of all machining processes are limited by the vibrations caused in the cutting process. Although recent advances have unveiled the fundamentals physics behind these barriers for machining processes, there still exist a large gap between what is possible in the best academic lab and in a production setting. This project seeks to develop diagnostic tools that will enable manufacturers to take advantage of the latest academic knowledge for vibration problems, such as stability limitations, surface finish, and surface location error. Therefore, the results of this grant will benefit the U.S. economy and society. This research involves and impacts several disciplines which include dynamical systems and control, manufacturing, machine learning, and data science. It is expected that this research, along with the complementary educational efforts, will help train the future workforce and broaden the participation of underrepresented groups in STEM disciplines. Tool vibrations impose severe limitations on industrial capability, such as reduced accuracy, a poor surface finish, and increased costs which are linked to instability. Although past research has uncovered the fundamental mechanism that leads to instability, it is still nearly impossible for the U.S. industrial base to apply this knowledge due to the need for repetitive, costly, and manually intensive modal tests and separate cutting force tests. This research will develop two data-driven approaches that will enable the U.S. industrial base to integrate machining dynamics with modern cyber-infrastructure. More specifically, the first research objective develops a new approach to automate the identification of the physical parameters required by predictive machining dynamics models and analysis tools. This will enable modern cyber infrastructure to optimize cutting process parameters and thus allow better decision to be made that now include the limitations imposed by vibrations. The second research objective develops a data-driven approach to discover the governing equations of systems that include time delays. It is expected that this framework will provide a more comprehensive understanding of the important physical mechanisms to include in machining dynamics models; this method could also generate models that modern cyber infrastructure could use to obtain optimal cutting process parameters or monitor the cutting process to diagnose problems from model parameter changes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该补助金将支持有助于与制造过程相关的新知识的研究，促进科学进步和国家繁荣。减法制造工艺通过去除小层材料来制造三维零件和物体。机械加工是一种更普遍的减材工艺，包括被称为铣削、钻孔、拉削和车削的材料去除方法。这些机械加工过程中的每一个都使用切削工具从块状材料中去除材料层并留下所需的三维部件。这些材料去除工艺也是为汽车、航空航天、医疗器械行业制造金属、木材和塑料零件的最广泛使用的方法之一。然而，所有加工过程的精度、表面质量和生产率都受到切削过程中产生的振动的限制。虽然最近的进展已经揭示了这些加工工艺障碍背后的基本物理原理，但在最好的学术实验室和生产环境之间仍然存在很大的差距。该项目旨在开发诊断工具，使制造商能够利用最新的学术知识来解决振动问题，例如稳定性限制，表面光洁度和表面位置误差。因此，这笔赠款的结果将有利于美国的经济和社会。这项研究涉及并影响多个学科，包括动力系统和控制，制造，机器学习和数据科学。预计这项研究，沿着补充教育工作，将有助于培训未来的劳动力，并扩大代表性不足的群体在STEM学科的参与。工具振动对工业能力造成严重限制，例如精度降低、表面光洁度差以及与不稳定性相关的成本增加。尽管过去的研究已经揭示了导致不稳定的基本机制，但由于需要重复、昂贵和手动密集的模态测试和单独的切削力测试，美国工业基地几乎不可能应用这些知识。这项研究将开发两种数据驱动的方法，使美国工业基地能够将加工动态与现代网络基础设施相结合。更具体地说，第一个研究目标开发了一种新的方法来自动识别预测加工动力学模型和分析工具所需的物理参数。这将使现代网络基础设施能够优化切割工艺参数，从而能够做出更好的决策，包括振动带来的限制。第二个研究目标是开发一种数据驱动的方法来发现包含时滞的系统的控制方程。预计该框架将提供一个更全面的理解的重要物理机制，包括在加工动力学模型;这种方法还可以生成模型，现代赛博基础设施可以使用这些模型来获得最佳的切割过程参数，或者监控切割过程，以便根据模型参数的变化来诊断问题。这项奖励反映了美国国家科学基金会的法定使命，并且通过评估，被认为是值得支持的使用基金会的知识价值和更广泛的影响审查标准。