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CPS: Small: Mitigating Uncertainties in Computer Numerical Control (CNC) as a Cloud Service using Data-Driven Transfer Learning

CPS：小型：使用数据驱动的迁移学习减轻计算机数控 (CNC) 作为云服务的不确定性

基本信息

批准号：
1931950
负责人：
Chinedum Okwudire
金额：
$ 50万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931950&HistoricalAwards=false
关键词：
CPS Small Mitigating Uncertainties Computer

项目摘要

Computer numerical control (CNC) is a critical feature of modern manufacturing machines. It provides automated control based on a set of programmed instructions, which traditionally run on a local computer that is physically tethered to the machine. This work envisions a future where manufacturing machines are controlled remotely over the Internet using CNC installed on cloud computers. Among several benefits over traditional CNC, cloud-based CNC holds promise to significantly improve the speed and accuracy of manufacturing machines at low cost. However, a major challenge with cloud-based CNC is that, somewhat like video streaming, it controls manufacturing machines primarily using pre-calculated commands that must be buffered to mitigate Internet transmission delays. For this reason, cloud-based CNC is susceptible to anomalies that result from delayed transmission of information on how the controlled machine is actually behaving. The award supports a scientific investigation into approaches for predicting impending anomalies from data gathered from past experience, and using the predictions to avoid incorrect control actions resulting from inadequate feedback. The U.S. stands to benefit economically from a transition from traditional to cloud-based CNC, since the U.S. is by far the market leader in cloud-based services. The project also will include outreach to U.S. companies, educational curriculum development to increase the U.S. talent pool in manufacturing and data analytics, and activities for middle schoolers in the Detroit area to inspire them to pursue careers in engineering.The objective of the project is to mitigate uncertainties associated with real-time control of manufacturing machines from the cloud using data-driven transfer learning. The knowledge gained will boost the performance of manufacturing machines at low cost by providing the machines with reliable cloud-based CNC. In cloud-based CNC, advanced feedforward control functionalities are transitioned to the cloud while fast feedback loops are retained locally. However, with emphasis on feedforward control, uncertainties in modeling the dynamic behavior of machines could degrade the reliability and performance of cloud-based CNC by causing failures, due to inaccurate control actions. The system will predict failures using measured signals and mitigate them in a redundant, cloud-based CNC architecture by switching control authority from a cloud controller to a back-up local controller in the event of an impending failure. To this end, a data-driven transfer learning framework will predict and minimize uncertainties using data obtained from other machines connected to cloud-based CNC. Such transfer learning leverages data from one source to learn a different, but related, target source. The framework will allow cloud-based CNC to: (i) learn from a combination of condition monitoring signals and past failure data to predict impending failures, (ii) reduce uncertainties by leveraging condition monitoring data to calibrate physical models whose parameters are functions of their inputs, and (iii) plan feasible trajectories for switching from a cloud to a local controller when an impending failure is detected. The project will address the shortcomings of existing transfer learning methods by: (i) predicting failure events from a combination of condition monitoring and past failure data, and (ii) calibration of physics-based models with functional parameters from condition monitoring data. The methods will be evaluated experimentally on a CPS test bed consisting of a 3D printer controlled from the cloud using a cloud-based CNC prototype.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.

计算机数控（CNC）是现代制造机械的一个重要特征。它提供基于一组编程指令的自动控制，这些指令通常在物理上与机器相连的本地计算机上运行。这项工作设想了一个未来，制造机器通过安装在云计算机上的CNC通过互联网远程控制。与传统CNC相比，基于云的CNC有几个优点，其中之一是有望以低成本显著提高制造机器的速度和精度。然而，基于云的CNC的一个主要挑战是，有点像视频流，它主要使用预先计算的命令来控制制造机器，这些命令必须进行缓冲，以减少互联网传输延迟。由于这个原因，基于云的CNC容易受到异常的影响，这些异常是由于被控制机器实际行为的信息传输延迟造成的。该奖项支持对从过去经验中收集的数据预测即将发生的异常的方法进行科学研究，并使用预测来避免由于反馈不足而导致的错误控制行动。美国将从传统CNC向基于云的CNC过渡中获得经济利益，因为美国目前是基于云的服务的市场领导者。该项目还将包括与美国公司的接触，开发教育课程，以增加美国制造业和数据分析方面的人才库，以及为底特律地区的中学生举办活动，鼓励他们从事工程方面的职业。该项目的目标是利用数据驱动的迁移学习，减轻与从云端实时控制制造机器相关的不确定性。所获得的知识将通过为机器提供可靠的基于云的CNC来提高低成本制造机器的性能。在基于云的CNC中，先进的前馈控制功能被转移到云端，而快速反馈回路在本地保留。然而，由于强调前馈控制，机器动态行为建模中的不确定性可能会由于不准确的控制动作而导致故障，从而降低基于云的CNC的可靠性和性能。该系统将使用测量信号预测故障，并在一个冗余的、基于云的CNC架构中减轻故障，在即将发生故障的情况下，通过将控制权限从云控制器切换到备用本地控制器。为此，数据驱动的迁移学习框架将使用从连接到基于云的CNC的其他机器获得的数据来预测和最小化不确定性。这种迁移学习利用来自一个来源的数据来学习不同但相关的目标来源。该框架将允许基于云的CNC:(i)从状态监测信号和过去故障数据的组合中学习，以预测即将发生的故障；（ii）通过利用状态监测数据来校准物理模型（其参数是其输入的函数）来减少不确定性；（iii）在检测到即将发生的故障时，规划从云切换到本地控制器的可行轨迹。该项目将通过以下方式解决现有迁移学习方法的缺点：(i)结合状态监测和过去的故障数据预测故障事件，以及（ii）根据状态监测数据的功能参数校准基于物理的模型。这些方法将在CPS试验台上进行实验评估，该试验台由使用基于云的CNC原型从云控制的3D打印机组成。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。