This week, I focused on improving our robot’s data collection and classification pipeline. I worked closely with Rayann to continue training the robot’s SVM model, refining its ability to distinguish between defective and non-defective regions. As part of this effort, we also re-evaluated our feature set to identify the most informative signals, aiming to enhance model performance and reduce noise in the data.
On the hardware side, we made significant progress by assembling the 3D-printed components of the robot, bringing us closer to a fully integrated system. We also added a second acoustic emission sensor, allowing us to collect data more efficiently and speed up the testing process. This improvement is especially valuable as we scale up data collection for training and validation.
Additionally, we revisited our data strategy and made an important decision regarding dataset composition. After analyzing the differences between handheld and robot-collected data, we observed that the sensing characteristics varied significantly. To maintain consistency and improve model reliability, we decided to exclude the handheld data and focus solely on robot-collected samples for training moving forward.