In Bone Tissue Engineering (BTE), autologous bone-regenerative cells are combined with a scaffold for large bone defect treatment (LBDT). Microporous, polylactic acid (PLA) scaffolds showed good healing results in small animals. However, transfer to large animal models is not easily achieved simply by upscaling the design. Increasing diffusion distances have a negative impact on cell survival and nutrition supply, leading to cell death and ultimately implant failure. Here, a novel scaffold architecture was designed to meet all requirements for an advanced bone substitute. Biofunctional, porous subunits in a load-bearing, compression-resistant frame structure characterize this approach. An open, macro- and microporous internal architecture (100 µm–2 mm pores) optimizes conditions for oxygen and nutrient supply to the implant’s inner areas by diffusion. A prototype was 3D-printed applying Fused Filament Fabrication using PLA. After incubation with Saos-2 (Sarcoma osteogenic) cells for 14 days, cell morphology, cell distribution, cell survival (fluorescence microscopy and LDH-based cytotoxicity assay), metabolic activity (MTT test), and osteogenic gene expression were determined. The adherent cells showed colonization properties, proliferation potential, and osteogenic differentiation. The innovative design, with its porous structure, is a promising matrix for cell settlement and proliferation. The modular design allows easy upscaling and offers a solution for LBDT.

Additive manufacturing, in particular the powder bed fusion of metals using a laser beam, has a wide range of possible technical applications. Especially for safety-critical applications, a quality assurance of the components is indispensable. However, time-consuming and costly quality assurance measures, such as computer tomography, represent a barrier for further industrial spreading. For this reason, alternative methods for process anomaly detection using process monitoring systems have been developed. However, the defect detection quality of current methods is limited, as single monitoring systems only detect specific process anomalies. Therefore, a new methodology to evaluate the data of multiple monitoring systems is derived using sensor data fusion. Focus was placed on the causes and the appearance of defects in different monitoring systems (photodiodes, on- and off-axis high-speed cameras, and thermography). Based on this, indicators representing characteristics of the process were developed to reduce the data. Finally, deterministic models for the data fusion within a monitoring system and between the monitoring systems were developed. The result was a defect detection of up to 92% of the melt track defects. The methodology was thus able to determine process anomalies and to evaluate the suitability of a specific process monitoring system for the defect detection.

Die Qualitätssicherung beim pulverbettbasierten Laserstrahlschmelzen (PBF-LB/M) ist kostenintensiv und eine der großen Barrieren für eine breite industrielle Anwendung. Neue Ansätze der digitalen Qualitätssicherung durch Überwachungssysteme sollen dieses Problem lösen. Um diese Systeme für die lokale Fehlererkennung auslegen zu können, wird eine Methode zur Verknüpfung von Prozessüberwachungsdaten mit einer CT-Visualisierung der Probe vorgestellt. Quality assurance in powder bed fusion with a laserbeam (PBF-LB/M) is cost-intensive and remains one of the major barriers to widespread industrial application. New approaches of digital quality assurance through monitoring systems are expected to solve this problem. To design these systems for use in local defect detection, a method for linking process-monitoring data to the CT visualization of the sample is presented.