Balancing Exploration and Exploitation for Efficient Black-Box Cloning in Smart Manufacturing

The increasing complexity of smart manufacturing systems demands advanced digital twinning solutions for process optimization. A key challenge is the cloning of hidden functions, where direct access to complex systems or human experts is restricted or expensive. This paper introduces an adversarially guided cloning framework that leverages hybrid learning strategies, i.e. balancing exploitation (efficient weight optimization) and exploration (intelligent input selection), to replicate black-box functions under constrained query budgets and temporal drift. We propose an adversarial sampling strategy, inspired by active learning, adversarial training, and curriculum learning, to select informative queries and improve cloning efficiency. Through controlled experiments, we demonstrate that our method outperforms random sampling in replicating hidden supervisory functions, particularly in scenarios with limited access to ground truth. Additionally, we investigate cloning under temporal drift conditions, where the supervisor’s decision boundaries evolve over time, requiring adaptive strategies to maintain cloning accuracy. While our study focuses on synthetic experiments, future research will explore cloning multiple interrelated supervisor functions and integrating them into a unified decision model for complex industrial processes. Our approach contributes to AI-driven digital twin enhancements by enabling more efficient modeling of unknown industrial processes with minimal data while preserving reasonable precision.