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

这笔赠款提供资金，用于开发基于物理和数据驱动的方法，根据非线性随机动态系统原理实时预测制造系统的性能输出(例如，生产能力和成品率)。现代制造企业在各种传感器和IT基础设施方面进行了投资，以提高车间系统的可见性。这提供了一个前所未有的机会，可以从动态而不是静态的角度跟踪制造系统的表现。传统的静态模型不足以从这些大型数据源实时预测性能变量。动态模型是必要的，例如由拟议的研究得出的模型。与以往的方法不同，该方法将显式地考虑影响生产线中停机时间分布的退化和维修动态。S型函数理论将被用来消除模型中的不连续性。放宽常用的平稳性假设，降低模型阶数，以促进非平稳状态下快速、准确的实时性能估计。该方法将使用从通用汽车(Goali Partner)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，建议的研究结果将有助于改进包括汽车和半导体行业在内的现实世界制造系统的仿真建模和实时性能预测。为了使现代工业适应需求波动以及供应和产能变化，快速而准确的模拟模型以及性能预测器变得越来越必要。随着国内工业制造业向按订单组装？在这一模式下，拟议的方法可以是非常有价值的跟踪和预测业绩的手段，以支持短期和中期的决策。

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.