In this article, a hybrid finite element model is presented for the simulation of induction heating of layered composite plates. Modeling includes the alternating electromagnetic field generated by an alternating current running through a coil, the current densities in the composite plate resulting from the electromagnetic field, the heat generation resulting from the current density distribution, and the heat transfer resulting from the nonuniform heat generation in the plate and the temperature distribution in the plate. The different elements of the model are shown to capture the time-dependent temperature distribution resulting from a coil moving over the surface of a composite laminate.

Induction welding for thermoplastic composite materials uses an alternating current flowing through a coil to induce an electromagnetic field and generate eddy current inside laminate with various fiber orientations-the generated heat causes the laminate to heat up and melt the polymer. As a pressure is applied to the induction heating zones, cohesive bonding may occur during the melting of the polymer. The welding quality of the composite materials is highly influenced by the temperature varying inside the heating zones. Thus, it is beneficial for induction welding if temperature varying during heating can be acquired given a set of welding parameters, such as current, pressure, fiber orientations, etc. Conducting practical induction heating experiments for this purpose is laborious and time consuming given the large varying space of welding parameters. In this paper, we propose to address this problem by using machine learning techniques to model the relation between the welding parameters and the temperature varying inside the heating zones. We conduct two sets of induction heating experiments for laminate welding and the collected sample temperature varying data are used to train the neural networks with input of welding parameters and output of the predicted temperature varying. Testing of the models demonstrates that process modeling of induction welding with machine learning techniques is viable.

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Several morphing unmanned aircraft systems which can be deployed in-flight are currently being developed for a variety of missions. Key to a successful in-flight deployment of these aircraft is that they enter a stable and controllable flight phase following a potentially highly dynamic transition phase without exceeding structural limitations. The aim of the current study is to develop a new physics-based methodology which can be used to assess under which flight conditions an unmanned morphing aircraft can be safely deployed in terms of stability, controllability and dynamic flight loads. The method is based on a Monte Carlo Simulation of the deployment phase with a multibody dynamics simulation model. As test case, the Dash X UAV is analyzed in combination with different deployment scenarios. Parameters to be varied are initial flight conditions such as body angular rates and the morphing strategy. The model is validated against a limited set of flight test data in its deployed state. Example results of the aircraft motion and loads are presented for safe deployments with a highly dynamic transition phase. The procedure to construct stability limits and deployment load envelopes is presented. The deployment load envelopes are a natural extension to the V-n diagram typically used for structural design. The stability limits can be used to determine the operational limits under which a UAV can be deployed safely without the risk of entering an unstable or uncontrollable flight regime. Ultimately, this method can be used to support the design of in-flight deployable morphing UAVs and the related operational procedures. It is demonstrated that the Dash X UAV can be safely deployed under realistic conditions with acceptable structural loads.

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