The demand for products made of fiber‐reinforced polymer composites (FRPC) is constantly growing. These lightweight products are characterized by high stiffness, high tensile strength, and high service life. FRPC processes that employ thermoset‐impregnated continuous rovings are easily automated and provide the products with the highest unidirectional tensile strength. A critical disadvantage of continuous fiber‐reinforced polymers is caused by relatively high production costs. Among others, three main factors contribute to these production costs: (1) material costs, especially when carbon fibers are used, (2) costs for manufacturing semi‐finished products, such as textiles or preimpregnated fabrics, and (3) costs for waste occurring along the entire chain of process steps. In this context, one group of processes shows outstanding characteristics: processes in which rovings are in situ impregnated with a thermoset resin and then directly processed. Wet filament winding and pultrusion are the most popular but not the only representatives of this group. For all these processes, in situ impregnation is the key element, and various technologies have been developed for this purpose, each with its own unique fluid‐mechanical effects on rovings. A fundamental understanding of these effects is crucial to achieve products of the utmost quality. The paper at hand provides an overview of manufacturing processes that employ in situ impregnation of continuous rovings, specifically focusing on impregnation technologies. On this basis, phenomenological models describing the effects on the rovings during processing (impregnation, tension, and spreading) are reviewed.

Machine vision is revolutionizing modern manufacturing, with new applications emerging regularly. The composites industry, relying on precision in aligning fibers, stands to benefit significantly from machine vision. Ensuring the exact fiber orientation is critical, as deviations can compromise product mechanical properties and lead to failure. Machine vision, particularly in wet fiber placement (WFP), offers a solution for monitoring and enhancing quality control in composite manufacturing. WFP involves pulling fiber bundles, impregnating them with resin, and precisely transporting them to mold tooling for layer-by-layer fabrication. The challenge lies in handling tacky, wet fiber bundles, making tactile sensors impractical. This makes WFP an ideal candidate for contactless process monitoring. The objective of this study is to employ a low budget machine vision in WFP, utilizing a webcam connected to a single-board computer. Artificial intelligence is trained using images of fiber bundles just before placement on the tooling mold. The module detects and measures the position and orientation of a roving in the starting position, enabling the initiation of the WFP process. The methods employed are thoroughly evaluated for reliability and feasibility. After completing only 50 training epochs, a roving detection accuracy of 91.3% could be achieved with almost no critical errors. With additional iterations per placement process, the system functions almost flawlessly at its current state.

The miniaturization of components and the functionalization via micro structures demands for flexible and economic manufacturing processes. Micro machining, i.e. micro milling and micro grinding can meet these requirements. In this paper, desktop-sized machine tools and their components that were developed at our institute are presented. With those machine tools, micro tools can be machined and used in one clamping, allowing for increased machining quality. Grooves milled with such machine tools achieve a bottom surface roughness below 10 nanometer.