GOALI: Real-time Performance Prediction of Multi-Stage Manufacturing Systems using Nonlinear Stochastic Differential Equation Models

GOALI：使用非线性随机微分方程模型进行多阶段制造系统的实时性能预测

• 批准号: 0729552
• 负责人: Satish Bukkapatnam
• 金额: $ 24万
• 依托单位: Oklahoma State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0729552&HistoricalAwards=false
• 关键词: GOALI; Real; time; Performance; Prediction

This grant provides funding to pursue physics-based and data driven approaches for real-time prediction of performance outputs of manufacturing systems (e.g., throughputs and yield rates) based on nonlinear stochastic dynamic systems principles. Modern manufacturing enterprises have invested in a variety of sensors and IT infrastructure to increase plant floor systems visibility. This offers an unprecedented opportunity to track performance of a manufacturing system from a dynamic, as opposed to a static sense. Conventional static models are inadequate for predicting performance variables in real-time from these large data sources. Dynamic models, such as those resulting from the proposed research, are necessary. Unlike previous approaches, degradation and repair dynamics that influence the down time distributions in a manufacturing line will be explicitly considered in the proposed approach. Sigmoidal function theory will be used to remove discontinuities in the models. The commonly used stationarity assumptions will be relaxed and model order reduced, to promote fast and accurate real-time performance estimation under nonstationary regimes. The proposed approach will be validated using multiple data sets acquired from actual operations at General Motor's (GOALI partner) automotive assembly lines.If successful, the results of the proposed research will lead to improvements in simulation modeling and real-time performance prediction of real-world manufacturing systems, including in automotive and semiconductor industry. Fast and accurate simulation models as well as performance predictors are becoming increasingly necessary to make the modern industries adapt their operations to demand fluctuations as well as supply and capacity variations. As the domestic industrial manufacturing is moving to ?assemble-to-order? paradigm, the proposed approach can be extremely valuable means to track and predict performance to support decision-making over short- and medium-term horizons.

该赠款提供资金，以追求基于物理和数据驱动的方法，用于实时预测制造系统的性能输出（例如，生产量和产出率）的基础上，非线性随机动态系统的原则。现代制造企业已经投资了各种传感器和IT基础设施，以提高车间系统的可见性。这提供了一个前所未有的机会，从动态跟踪制造系统的性能，而不是静态的感觉。传统的静态模型不足以从这些大数据源实时预测性能变量。动态模型，如那些从拟议的研究，是必要的。与以往的方法不同，退化和修复动态的影响，在生产线的停机时间分布将被明确考虑在所提出的方法。将使用S形函数理论来消除模型中的不连续性。通常使用的平稳性假设将被放松，模型的阶数减少，以促进快速和准确的实时性能估计下的非平稳制度。该方法将使用从通用汽车（GOALI合作伙伴）汽车装配线的实际操作中获得的多个数据集进行验证。如果成功，所提出的研究结果将导致改进包括汽车和半导体行业在内的真实制造系统的仿真建模和实时性能预测。快速准确的仿真模型以及性能预测器变得越来越必要，以使现代工业适应需求波动以及供应和容量变化。作为国内工业制造业正在向？按单点菜？作为一种范例，所提出的方法可以成为跟踪和预测业绩以支持中短期决策的极其宝贵的手段。