Powder bed fusion (PBF) represents a class of additive manufacturing processes with the unique advantage of being able to fabricate functional products with complex three-dimensional geometries. PBF has been broadly applied in highly value-added industries, including the biomedical device and aerospace industries. However, it is challenging to construct a comprehensive knowledgebase to guide material selection and process optimization decisions to satisfy the product standards of various industries based on a poor understanding of process-structure-property/performance relationships for each type of thermoplastic. In this paper, an intelligent optimization system is proposed to establish quantitative relationships between process parameters and multiple optimization objectives, including mechanical properties, productivity, energy efficiency, and degree of material degradation. Polyurethane is considered as a representative thermoplastic because it is sensitive to thermal-induced degradation and has a relatively narrow process window. Material and powder properties as functions of temperature are investigated using systematic material screening. Numerical models are created to analyze the interactions between laser beams and polymeric powders by considering the effects of chamber thermal conditions, laser parameters, temperature-dependent properties, and phase transitions of polymers, as well as laser beam characteristics. The theoretically predicted features of melting pools are validated experimentally and then utilized to develop quantitative relationships between process parameters and multiple optimization objectives. The established relationships can guide process parameter optimization and material selection decisions for polymer PBF.

粉末床熔合（PBF）代表了一类增材制造工艺，其独特的优势是能够制造具有复杂三维几何形状的功能产品。PBF已广泛应用于高附加值行业，包括生物医学设备和航空航天行业。然而，基于对每种类型的热塑性塑料对过程-结构-性能/性能关系的了解不足，构建一个全面的知识库来指导材料选择和过程优化决策以满足各种行业的产品标准是一项挑战。本文提出了一种智能优化系统，以建立过程参数与多个优化目标之间的定量关系，包括机械性能，生产率，能源效率，和材料降解程度。聚氨酯被认为是代表性的热塑性塑料，因为它对热引起的降解敏感，并且加工窗口相对较窄。使用系统的材料筛选研究了材料和粉末特性随温度的变化。通过考虑腔室热条件，激光参数，温度相关特性和聚合物的相变以及激光束特性的影响，创建了数值模型来分析激光束与聚合物粉末之间的相互作用。对熔池的理论预测特征进行了实验验证，然后用于建立工艺参数与多个优化目标之间的定量关系。