Intelligent optimization system for powder bed fusion of processable thermoplastics

Abstract

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.