Data-driven optimization solutions for in-situ monitoring, prognostics, and optimization in production systems: addressing randomness from micro to macro levels
Creators: name: Chen, Mengfei
Publisher: name: No Publisher Supplied
Year: 2024

description: Ensuring optimal production performance is crucial for achieving high-quality products, meeting customer demands, and maximizing profitability. However, unpredictable events and inherent randomness can lead to variations in the system, thereby affecting system performance. This randomness exists at each level of the system, from a single machine to a manufacturing system composed of machines and extending to the supply chain, resulting in performance fluctuations. Recently, advanced sensing technologies have provided opportunities to leverage rich sensing data for in-situ monitoring, leading to more accurate prediction and better decision-making. The applicability of data-driven optimization approaches in production systems, however, is limited by the lack of understanding from both engineering and data aspects due to the inherent randomness and complex nature. This dissertation proposes data-driven optimization solutions integrated with domain knowledge for in-situ monitoring, prognostics, and optimization, addressing randomness in production systems at each critical link, from the micro to the macro level of scope. At the single-machine level, the quality of a part is one of the most concerned metrics. In order to achieve desired quality, it is necessary to develop methods that facilitate in-situ process monitoring and quality prediction. However, the deployment of data-driven approaches necessitates substantial datasets for training, posing challenges in real manufacturing scenarios with limited defect data, insufficient historical data, and frequently changing process settings. To mitigate the issue of imbalanced data, a novel data augmentation approach integrating deep convolutional generative adversarial networks (DCGAN) with physics-informed constraints is proposed. To address the issue of insufficient historical data due to process changes, a process-informed transfer learning method is proposed to leverage the strength of pre-trained deep learning models for quality prediction, allowing them to be reused on similar tasks with minimal additional training. To address the issue of changing process settings, a new quality prediction method based on continual learning is proposed so that the method can adapt to the dynamic nature of the production system without forgetting previous ones. The proposed methods are demonstrated in process monitoring and quality prediction in laser-based manufacturing. The performance of an individual machine is critical to the throughput of an entire manufacturing system, which is one of the primary metrics at the system level. Therefore, it is essential to investigate the interdependence among machines and understand how they affect the system’s overall performance. Moreover, the randomness in machine status, such as machine breakdown, poses challenges to throughput prediction. To address this challenge, this research proposes a machine learning-based hierarchical model to characterize between-machine relationship, identify machine condition and analyze root cause. The effectiveness of this approach is demonstrated in the context of automotive manufacturing by leveraging rich sensor data from each machine on the production floor. Manufacturing system is one of the fundamental components in delivering high-quality products to end customers. At the supply chain’s scope, traditional methods have focused on maximizing profit and minimizing the cost. Advances in data collection offer opportunities to access real-time data from different regions. This introduces new concerns about resource accessibility that linked with fairness, inclusion, and equity. These new concerns are further coupled with uncertainty in supply/demand. The presence of uncertainties such as stochastic supply/demand and system disruptions can make it challenge to ensure optimization model parameters. Therefore, it is essential to develop solutions for decision-making that prioritize system resilience. To address this challenge, this research proposes an approach that combines stochastic optimization and simulation. The effectiveness of this approach is demonstrated in the context of a food supply chain, where the approach initially solves for stochastic demand and supply and further explores solutions for system disruption under emergency scenarios. This research aims to offer a comprehensive investigation to the analysis of complex systems, specifically, addressing randomness at each critical link in production systems. By incorporating advanced sensing technologies and domain knowledge, these solutions enable in-situ production performance monitoring, prognostics, and optimization. This research contributes to enhancing quality prediction, production performance, and supply chain resilience, ultimately ensuring the efficient delivery of high-quality products to customers. The methodology can be generalized to applications in production systems and other systems with sensing capabilities and prediction needs, but grapple with challenges of insufficient data, systems variation, and accessibility concerns.