Sensor and feature selection for cost- and time-efficient online monitoring of ultrasonic metal welding

Ultrasonic metal welding (UMW) is a solid-state joining process widely used in industrial applications. However, its sensitivity to tool wear, surface contamination, and material variability presents persistent challenges for ensuring weld quality. Existing online monitoring systems often emphasize predictive accuracy while neglecting practical constraints such as hardware cost, data acquisition rate, and computational latency. To overcome this gap, this paper develops a systematic framework for cost- and time-efficient sensor and feature selection in UMW monitoring. The proposed method integrates signal decomposition, feature importance analysis, cost-aware genetic algorithm optimization, and a separability-analysis-based adaptation mechanism to identify an optimal subset of sensors, features, and time segments that balance predictive accuracy with resource efficiency. Extensive case studies using a multi-sensor data acquisition system demonstrate that the framework achieves high monitoring accuracy in both weld quality prediction and mixed tool and sample surface condition classification while reducing the feature pool by 96.8%–99.4%. Even under reduced sampling frequency (6.25 kHz) and shortened time windows (0.3 s), the model maintains strong predictive performance. Furthermore, the separability-analysis-based adaptation accurately recognizes new fault types using only three samples, reducing retraining data requirements by 90%. Overall, the proposed framework provides a new, scalable solution for cost- and time-efficient UMW monitoring and establishes a foundation for adaptive, lightweight monitoring systems applicable to other manufacturing processes.