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.

翻译：金属增材制造（MAM）重塑了制造业，在复杂设计、最小浪费、快速原型制作、材料多样性和定制解决方案等方面具有优势。然而，其全面工业应用面临挑战，尤其是在实现稳定产品质量方面。MAM成功的关键在于理解工艺参数与熔池特性之间的关系。将人工智能（AI）融入MAM至关重要。传统机器学习（ML）方法虽然有效，但依赖于大型数据集来捕捉复杂关系，而在MAM中，创建数据集需要大量时间和资源，因此这是一个重大挑战。我们的研究引入了一种新颖的惊喜引导序列学习框架——SurpriseAF-BO，标志着MAM的重大转变。该框架采用迭代自适应学习过程，在有限数据条件下模拟工艺参数与熔池特性之间的动态关系，这在MAM的信息物理制造环境中具有关键优势。与传统ML模型相比，我们的序列学习方法在熔池尺寸预测精度上表现出显著提升。为进一步改进方法，我们在框架中集成了条件表格生成对抗网络（CTGAN），形成CT-SurpriseAF-BO，可生成与真实实验数据相似的合成数据，从而提高学习效果。这一增强在不增加实际实验的前提下提升了预测精度。本研究展示了先进数据驱动技术在信息物理制造中的强大能力，以及序列AI和ML，特别是在克服MAM传统挑战方面的巨大影响。