Meta-Learning-Based Domain Generalization for Cost-Effective Tool Condition Monitoring in Ultrasonic Metal Welding

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Abstract
Online tool condition monitoring (TCM) is a pivotal capability in many manufacturing applications including ultrasonic metal welding (UMW). Effective and efficient TCM can facilitate predictive maintenance, improve product quality, and enhance productivity. Existing online TCM systems based on conventional machine learning models often require a large amount of labeled data, the collection of which is cost-prohibitive, time-consuming, and labor-intensive. Such models fail to satisfy the requirements of cost-effectiveness and agility posed by modern, reconfigurable UMW systems. As such, data-efficient TCM methods with excellent generalization ability are of vital importance. To this end, we develop a novel similarity-based meta-representation learning (SMRL) method for domain generalization. SMRL effectively learns high-level meta-knowledge that is shared among different welding scenarios or domains. Therefore, the model trained in source domains can be generalized to other domains without access to labeled data in the training phase. Case studies are performed using four welding scenarios with varied welding materials and welding parameters. It is demonstrated that the proposed method is superior to the baseline methods, including neural network, hierarchical neural network, model-agnostic meta-learning (MAML), and hierarchical MAML. Compared with the baseline methods, SMRL offers an average improvement of 15.44%–31.62% in TCM accuracy. These results show that SMRL is readily applicable to industrial applications to enable cost-effective and data-efficient TCM.
Funding Information
  • National Science Foundation (1944345, 2433484)

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