Collaborative Research: Fusion of Siloed Data for Multistage Manufacturing Systems: Integrative Product Quality and Machine Health Management

As products become more intricate, it is very common that modern manufacturing processes which involve multiple stages are required. The output workpiece of one stage is the input workpiece of the next stage. In a multistage manufacturing system, not only does the health condition of a machine interact with incoming and outgoing product quality, but the outgoing product quality also impacts the machine health condition and product quality at downstream machines. Due to the complicated interactions among product quality and machine health conditions across multiple stages, for effective system monitoring and operational control, it is insufficient (even misleading) to consider product quality issues and machine condition degradation issues separately at each individual machine. With the fast progress of sensing and information technology, a large amount of product quality data and machine health condition data at multiple machines in a multistage manufacturing system are easily acquired and accessed. This project establishes a series of data-driven methodologies to achieve efficient monitoring and operation of multistage manufacturing systems through integrative modeling of product quality and machine health data. The developed methodologies are tested and validated in a laboratory testbed and on real production systems with industrial collaborators. This project contributes to workforce training by promoting the interdisciplinary research of manufacturing, computing, sensing, and data analytics and provides unique training opportunities for students through new curriculum development and various outreach activities. An integrated mathematical framework to describe spatial interactions among different machines and the temporal degradation of each machine is investigated. At the core of the framework, a flexible non-homogeneous hidden Markov model is used to describe the machine temporal degradation. The interactions between product quality and the machine health condition are considered by incorporating exogenous factors into the model. Designed around the integrative model, four interrelated research tasks include: (i) Learning quality interactions and local anomaly indicators, (ii) Learning machine degradation model and failure prognosis, (iii) Stochastic control for system-level operation optimization, and (iv) Testing and validation. The project provides added capabilities for a modern manufacturing factory by making it more integrated in control through the exploitation of ever-growing available quality data and machine health condition data. 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.