  Metal Additive Manufacturing (MAM) has reshaped the manufacturing industry,
offering benefits like intricate design, minimal waste, rapid prototyping,
material versatility, and customized solutions. However, its full industry
adoption faces hurdles, particularly in achieving consistent product quality. A
crucial aspect for MAM's success is understanding the relationship between
process parameters and melt pool characteristics. Integrating Artificial
Intelligence (AI) into MAM is essential. Traditional machine learning (ML)
methods, while effective, depend on large datasets to capture complex
relationships, a significant challenge in MAM due to the extensive time and
resources required for dataset creation. Our study introduces a novel
surprise-guided sequential learning framework, SurpriseAF-BO, signaling a
significant shift in MAM. This framework uses an iterative, adaptive learning
process, modeling the dynamics between process parameters and melt pool
characteristics with limited data, a key benefit in MAM's cyber manufacturing
context. Compared to traditional ML models, our sequential learning method
shows enhanced predictive accuracy for melt pool dimensions. Further improving
our approach, we integrated a Conditional Tabular Generative Adversarial
Network (CTGAN) into our framework, forming the CT-SurpriseAF-BO. This produces
synthetic data resembling real experimental data, improving learning
effectiveness. This enhancement boosts predictive precision without requiring
additional physical experiments. Our study demonstrates the power of advanced
data-driven techniques in cyber manufacturing and the substantial impact of
sequential AI and ML, particularly in overcoming MAM's traditional challenges.
