Additive Manufacturing (AM), particularly Selective Laser Melting (SLM), has revolutionized the industrial manufacturing sector owing to its remarkable design flexibility and precision. However, it is well known that slight changes in SLM process parameters may highly affect the surface quality of the as-built product. In this paper, we investigate the influence of SLM printing parameters (laser power, laser scanning speed, layer thickness, and hatch distance) on surface quality and develop a predictive model for surface quality based on the given printing parameters. The developed model is constructed by a Bayesian Optimization and soft Attention mechanism-enhanced Transfer learning (BOAT) framework with superior domain adaptability and generalization capability. Through experimental validation, the effectiveness of the BOAT approach in estimating printing parameters and correlating them with surface quality has been verified. The comprehensive methodology, experimental configurations, prediction results, and ensuing discussions are all presented. This study contributes to providing valuable insights and practical implications for improving the competitiveness and impact of SLM in advanced manufacturing by accurately predicting surface quality with specified printing parameters.

增材制造 (AM)，特别是选择性激光熔化(SLM)，因其卓越的设计灵活性和精度而彻底改变了工业制造领域。然而，众所周知，SLM 工艺参数的微小变化可能会严重影响竣工产品的表面质量。在本文中，我们研究了 SLM 打印参数（激光功率、激光扫描速度、层厚度和填充距离）对表面质量的影响，并根据给定的打印参数开发了表面质量的预测模型。所开发的模型由贝叶斯优化和软注意力机制增强的迁移学习（BOAT）框架构建，具有卓越的领域适应性和泛化能力。通过实验验证，BOAT 方法在估计打印参数并将其与表面质量相关联方面的有效性得到了验证。全面的方法、实验配置、预测结果和随后的讨论都被呈现。这项研究通过使用指定的打印参数准确预测表面质量，有助于为提高 SLM 在先进制造中的竞争力和影响力提供宝贵的见解和实际意义。