Wire arc additive manufacturing (WAAM) has become a key technology in the fabrication of electrical equipment due to its high deposition rate and excellent material utilization efficiency. However

this process is inherently influenced by coupled multi-physical fields

making it prone to the formation of structural defects during multilayer deposition

which can significantly compromise the forming quality and structural reliability. To address the challenges of defect recognition in multilayer deposition

including the scarcity of labeled samples

the high dimensionality of spectral inputs

and the limited physical interpretability of existing models

this paper proposes a spectral information-driven optimization method that integrates physical spectral mechanisms with machine learning strategies to tackle high-dimensional defect recognition under small-sample constraints. First

key spectral regions of interest (ROI) associated with metal evaporation and gas excitation processes are identified based on elemental radiation mechanisms and empirical knowledge

enabling initial feature compression. Subsequently

a physically-constrained genetic optimization framework is constructed

where spectral feature selection and model parameter tuning are jointly encoded. A fitness function incorporating spectral retention ratio is further designed to guide each individual in the population toward a solution that balances classification performance with physical consistency. Finally

a lightweight extreme gradient boosting (XGBoost) classifier is employed for model training and prediction

ensuring both robustness and deployment efficiency. Experimental results demonstrate that the proposed method achieves an accuracy of 93.21% in recognizing multi-layer defects under limited sample conditions

exhibiting notable advantages over comparative methods in terms of accuracy

stability

and interpretability.