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Christian Boehlmann

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Proceedings Article | 15 August 2023 Presentation + Paper

Sensor-guided machining of large-scale CFRP components based on resin transfer molded features

M. Muth, P. Carstensen, W. Hintze, S. Gnadt, C. Brillinger, C. Möller, C. Böhlmann

Proceedings Volume 12618, 1261809 (2023) https://doi.org/10.1117/12.2673830

KEYWORDS: Carbon fiber reinforced plastics, Sensors, Carbon fibers, Manufacturing, Reflection, Absorption, Epoxies, Blue lasers, Design and modelling, Metrology, Composite resins, Robotic systems, Injection molding

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Carbon fiber-reinforced Plastics (CFRP) are a key factor for achieving a carbon-neutral mobility sector due to their lightweight properties. Despite their near-net-shape manufacturing subsequent precision machining is required. In case of large-scale CFRP components, which are characterized by small quantities and low machining volumes, such processes are difficult to operate economically with conventional systems. Robotic systems offer a suitable solution. Their use in machining, however, requires additional measures to compensate for structural system limitations. In addition, individual deviations of the blanks require time-consuming measuring for referencing the machining system. To enable the use of cost-efficient robotic systems and to eliminate unproductive downtime for measuring, the intended manufacturing of CFRP components by Resin Transfer Molding (RTM) offers a new opportunity. RTM allows for the integration of defined features into the molding tool, which are reproduced inverted on the component and can be referenced by subsequent processes. This is utilized with adapted strategies for time-efficient measuring and high-precision machining. Fundamentals for the corresponding feature design as well as the selection of a suitable measuring device are presented. Focusing on laser-based sensors, the relationship between wavelength and (semi-)transparent surfaces is discussed and validated. Besides the metrological detection, production-specific aspects are also taken into account. Based on the gained knowledge, the main aspects of a proper sensor-feature combination are elaborated and prototypically realized. Finally, an outlook is provided on the challenges involved when implementing a robust algorithm for feature detection.

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