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Powder bed fusion of thermoplastic polymers is a powder based additive manufacturing process that allows for manufacturing individualized components with high geometric freedom. Despite achieving higher mechanical properties compared to other additive manufacturing processes, statistical variations in part properties and the occurrence of defects cannot be avoided systematically. In this paper, a novel method for the inline assessment of part porosity is proposed in order to detect and to compensate for inherent limitations in the reproducibility of manufactured parts. The proposed approach is based on monitoring the parameter-specific decay of the optical melt pool radiance during the melting process, influenced by a time dependency of optical scattering within the melt pool. The underlying methodology compromises the regression of the time-dependent optical melt pool properties, assessed in visible light using conventional camera technology, and the resulting part properties by means of artificial neural networks. By applying deep residual neural networks for correlating time-resolved optical process properties and the corresponding part porosity, an inline assessment of the spatially resolved part porosity can be achieved. The authors demonstrate the suitability of the proposed approach for the inline porosity assessment of varying part geometries, processing parameters, and material aging states, using Polyamide 12. Consequently, the approach represents a methodological foundation for novel monitoring solutions, the enhanced understanding of parameter–material interactions and the inline-development of novel material systems in powder bed fusion of polymers.

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Current research on laser-based powder bed fusion of polymers (PBF-LB/P) is heavily focused on the relationship between the process and component properties of existing commercially available powder materials, thus constraining the scope of application. An innovative approach is presented in this study, which first emphasizes the synthesis of a tailored polypropylene for PBF-LB/P, and subsequently the performance of the synthesized polymer in the process. Syndiotactic polypropylene (sPP) was chosen because of its advantageous properties, such as low crystallinity and crystallization kinetics compared to isotactic polypropylene. Therefore, a well-known, highly active zirconocene dichloride catalyst was used with appropriate polymerization settings to yield moderately high-molecular-weight sPP with high syndiotacticity. As the obtained product already precipitated directly from the synthesis in particle form, no further intermediate process step to the feedstock material for PBF-LB/P was required. The obtained polymer was analyzed in terms of molecular weight, polydispersity, and syndiotacticity. Furthermore, key properties of the PBF-LB/P process, such as thermal properties, melt viscosity, and powder flow behavior, were investigated. The initial PBF-LB/P processability was assessed by building single layers in a parameter study using an EOS P 396 machine. Based on these findings, a multilayer component was manufactured demonstrating the processability of the material system.

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