Multiphysics model of induction thermoplastic welding predicting the crystallinity of the interface
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Fasteners, such as rivets or bolts, account for a large proportion of aircraft weight. With the aim of exploring potential weight-saving opportunities, the aerospace industry is increasingly turning to alternative joining methods such as adhesives. Adhesive joints are usually riveted due to the lack of knowledge about their behavior and their use alone can increase the overall weight of the aircraft. Therefore, alternative methods of cohesive joints, such as welding, are gaining importance. Since they do not require an external agent as a joining element leads to a significant reduction in weight. In induction welding, an alternating magnetic field, generated by an induction coil, penetrates the material, inducing eddy currents in the conductive fibers, such as carbon fiber, within the thermoplastic matrix. The electrical resistance of the fibers converts these currents into heat through the Joule effect, raising the temperature at the interface of the thermoplastic material to its melting point. During the following phase, pressure is applied to ensure proper contact between the surfaces, and upon cooling, the thermoplastic solidifies, forming a cohesive bond. The quality of this bond is critically dependent on the degree of crystallinity in the thermoplastic. This study presents a model that aims to contribute to the development of a digital twin of the induction welding process in carbon fiber reinforced thermoplastic composite. By obtaining the temperature-time curves of the process, it is possible to model the crystallization of the interlayer in the weld. The degree of crystallinity will determine the mechanical properties and the quality of the weld using this model as a starting point for this prediction. Crystallization is experimentally corroborated using heat maps generated during experimental welding as well as Differential Scanning Calorimetry (DSC) tests. This approach offers a valuable tool for process optimization, reducing thermal defects and allowing prediction of the final mechanical properties of the weld.