a b s t r a c tSynthetic amorphous silica (SAS) like NM-200 is used in a wide variety of technological applications and consumer products. Although SAS has been widely investigated the available reproductive toxicity studies are old and do not cover all requirements of current OECD Guidelines. As part of a CEFIC-LRI project, NM-200 was tested in a two-generation reproduction toxicity study according to OECD guideline 416. Male and female rats were treated by oral gavage with NM-200 at dose levels of 0, 100, 300 and 1000 mg/kg bw/day for two generations. Body weight and food consumption were measured throughout the study. Reproductive and developmental parameters were measured and at sacrifice (reproductive) organs and tissues were sampled for histopathological analysis. Oral administration of NM-200 up to 1000 mg/kg bw/day had no adverse effects on the reproductive performance of rats or on the growth and development of the offspring into adulthood for two consecutive generations. The NOAEL was 1000 mg/kg body weight per day.

The combination of different additive manufacturing techniques to produce freeform products with multifunctional properties is gaining increasing popularity. In the research presented, Aerosol Jet® Printing (AJP) is combined with extrusion-based 3D printing. AJP starts with an ink to create micro-tracks. These tracks commonly have widths ranging from a few micrometers up to several millimeters and track heights ranging from a few tenths of a micrometer up to several micrometers, unlike extrusion-based 3D printers with which the extruded material usually has a resolution of tenths of millimeters. AJP can therefore be a complementary technique for extrusion-based 3D printing; in this manner, fine high resolution features can be added onto relatively rapidly produced extrusion-based 3D printed parts. Furthermore, AJP can be used to produce electrically conductive tracks to create interconnections, inductors, capacitors, strain gauges, etc. In this paper, the creation of AJP-manufactured interconnects on extrusion-based 3D printed substrates is investigated. The relevant AJP process parameters to take into account are the flow rates of the aerosol, the flow rate of the sheath gas, the temperature settings of the ink and substrate, and the platform speed and nozzle-to-substrate distance. To obtain reliable results, the AJP process parameters are optimized for printing single-layered and multilayered silver ink tracks on extrusion-based 3D-printed surfaces. Important quality output parameters include the dimensions and the electrical properties of the printed interconnects.

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This paper analyses temperature fields and their variations in fused filament fabrication (FFF) from the filament entering the hot-end to the printed parts, aiming at a deeper understanding of the thermal process of this additive manufacturing technology. A standard E3D print head assembly was mounted on a robot arm for printing. A stable filament feeding region was determined with an upper limit in the volume flow rate at different nozzle temperatures. Within the limit, the steady state temperature fields inside the hotend were studied by a computational fluid dynamics model. Simulations indicated that the temperature became less homogeneous at higher flow rates, leading to a lower extrudate temperature at the nozzle outlet.These outlet temperatures were analysed, validated, and used as input to simulate temperature variations in printed parts with a self-developed open-access numerical model. An inter-layer time similarity rule was found in printing single-walled geometries, which specifies temperature similarities at the same inter-layer time. The findings provide new insights into FFF processes, pointing out opportunities for improved production efficiency and scalability to large scale manufacturing.

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Machine learning (ML) may improve and automate quality control (QC) in injection moulding manufacturing. As the labelling of extensive, real-world process data is costly, however, the use of simulated process data may offer a first step towards a successful implementation. In this study, simulated data was used to develop a predictive model for the product quality of an injection moulded sorting container. The achieved accuracy, specificity and sensitivity on the test set was 99.4%, 99.7% and 94.7%, respectively. This study thus shows the potential of ML towards automated QC in injection moulding and encourages the extension to ML models trained on real-world data.The research leading to these results has received funding from VLAIO (TETRA project 'AI4IM ', project number HBC.2020', project number HBC. .2556

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