Meta-learning with similarity-weighted transfer for manufacturing optimization

The MBAE (Meta-Bayesian Active Explorer) framework addresses the critical challenge of sample efficiency in Industry 4.0 manufacturing optimization, where high-dimensional search spaces and expensive physical evaluations limit experimental budgets. By integrating hierarchical Bayesian meta-learning with similarity-weighted transfer, the model effectively overcomes the “cold-start” problem by leveraging historical task data while preventing negative transfer through a learned task-feature mapping. Unlike traditional evolutionary algorithms or standard Bayesian Optimization, MBAE utilizes a manufacturing-aware acquisition function that balances exploration, exploitation, and operational preferences with formal regret-bound guarantees. Experimental validation on 9D manufacturing problems demonstrates a 4.3x to 4.6x convergence speedup, reaching 95% optimality in approximately 6 iterations compared to over 26 for baseline methods, representing a significant advancement for rapid adaptation in reconfigurable production systems.