| description abstract | In modern manufacturing processes, ensuring the precision of 3D profiles of products is crucial. Nonetheless, achieving this accuracy is challenging due to the complex interactions between process inputs and the data structure of the 3D profile data. Our solution, a 3D profile-based control framework, addresses this challenge by actively adapting and controlling the manufacturing process to enhance 3D shape accuracy. 3D profile scans represent the ultimate measure of desired part quality. Therefore, utilizing them as the system responses for control purposes yields the most direct and effective feedback. We leverage recent advancements from Koopman operator theory to create an effective model-based control strategy. Initially, we estimate the process model by exploring the relationship between 3D profiles and heterogeneous process inputs. Then, we formulate an online model predictive control law. Challenges include dealing with unstructured, high-dimensional 3D point cloud data, capturing spatial and temporal structures, and integrating heterogeneous, high-dimensional process input data into the control model. To overcome these challenges, we introduce RETROFIT, a solution designed for the real-time control of time-dependent 3D point cloud profiles. Unlike traditional models, RETROFIT is not bound by linear assumptions and can handle unstructured 3D point cloud data directly. We demonstrate its effectiveness through a wire arc additive manufacturing case study, highlighting its potential to enhance 3D profile accuracy in manufacturing processes. | |
