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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 …