Level-Aware Residual Mixup for Defect Classification in Material Extrusion with Limited Print Jobs

Reliable in-situ defect classification is essential for ensuring the mechanical performance of parts produced by material extrusion. However, machine learning and deep learning models often suffer from poor generalization because training datasets typically consist of many correlated signal segments generated from only a limited number of independent print jobs. To address this problem, we analyze how job-specific characteristics are distributed across signal components and find that they are concentrated primarily in the signal level rather than in the residual component. Motivated by this observation, we propose Level-Aware Residual Mixup(LARM), a data augmentation method that separately interpolates the level and residual components of sensor signals. LARM preserves realistic job-level characteristics by restricting level interpolation to values observed in real print jobs while allowing flexible mixing of residual components within the same defect class. We evaluate LARM against representative augmentation methods across diverse classification models under a realistic job-level leave-one-group-out cross-validation protocol. Experimental results demonstrate that LARM achieves better generalization than both training without augmentation and representative augmentation methods.