Process Monitoring and Control of Advanced Manufacturing based on Physics-Assisted Machine Learning

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Virginia Tech

With the advancement of equipment and the development of technology, the manufacturing process is becoming more and more advanced. This appears as an advanced manufacturing process that uses innovative technology, including robotics, artificial intelligence, and autonomous systems. Additive manufacturing (AM), also known as 3D printing, is the representative advanced manufacturing technology that creates 3D geometries in a layer-by-layer fashion with various types of materials. However, quality assurance in the manufacturing process requires high expectations as the process develops. Therefore, the objective of this dissertation is to propose innovative methodologies for process monitoring and control to achieve quality assurance in advanced manufacturing. The development of sensor technologies and computational power offer process data, providing opportunities to achieve effective quality assurance through a machine learning approach. Hence, exploring the connections between sensor data and process quality using machine learning methodologies would be advantageous. Although this direction is promising, some constraints and complex process dynamics in the actual process hinder achieving quality assurance from the existing machine learning methods. To address these challenges, several machine learning approaches assisted by the physics knowledge obtained from the process have been proposed in this dissertation. These approaches are successfully validated by various manufacturing processes, including AM and multistage assembly processes. Specifically, three new methodologies are proposed and developed, as listed below. -To detect the process anomalies with imbalanced process data due to different ratios of occurrence between process states, a new Generative Adversarial Network (GAN)-based method is proposed. The proposed method jointly optimizes the GAN and classifier to augment realistic and state-distinguishable images to provide balanced data. Specifically, the method utilizes the knowledge and features of normal process data to generate effective abnormal process data. The benefits of the proposed approach have been confirmed in both polymer AM and metal AM processes. -To diagnose process faults with a limited number of sensors caused by the physical constraints in the multistage assembly process, a novel sparse Bayesian learning is proposed. The method is based on a practical assumption that it will likely have a few process faults (sparse). In addition, the temporal correlation of process faults and the prior knowledge of process faults are considered through the Bayesian framework. Based on the proposed method, process faults can be accurately identified with limited sensors. -To achieve online defect mitigation of new defects that occurred during the printing due to the complex process dynamics of the AM process, a novel Reinforcement Learning (RL)-based algorithm is proposed. The proposed method is to learn the machine parameter adjustment to mitigate the new defects during the printing. The method transfers knowledge learned from various sources in the AM process to RL. Therefore, with a theoretical guarantee, the proposed method learns the mitigation strategy with fewer training samples than traditional RL. By overcoming the challenges in the process, the above-proposed methodologies successfully achieve quality assurance in the advanced manufacturing process. Furthermore, the methods are not designed for the typical processes. Therefore, they can easily be applied to other domains, such as healthcare systems.

Quality Assurance, Reinforcement Learning, Sparse Bayesian Learning, Generative Adversarial Network, Additive Manufacturing