Automated fiber placement (AFP) is an advanced manufacturing method for composites, which is especially suitable for large-scale composite components. However, some manufacturing defects inevitably appear in the AFP process, which can affect the mechanical properties of composites. This work aims to investigate the recent works on manufacturing defects and their online detection techniques during the AFP process. The main content focuses on the position defect in conventional and variable stiffness laminates, the relationship between the defects and the mechanical properties, defect control methods, the modeling method for a void defect, and online detection techniques. Following that, the contributions and limitations of the current studies are discussed. Finally, the prospects of future research concerning theoretical and practical engineering applications are pointed out.

Wind turbine drivetrains consist of components that directly convert kinetic energy from the wind to electricalenergy. Therefore, guaranteeing robust and reliable drivetrain designs is important to prevent turbine downtime. Currentdrivetrain models often lack the ability to model both the impacts of electrical transients as well as wind turbulenceand shear in one package. In this paper, the capability of the FAST wind turbine computer-aided-engineering tool,developed by the National Renewable Energy Laboratory, is enhanced through the integration of a dynamic model of thedrivetrain. The dynamic drivetrain model is built using Simscape in the MATLAB/Simulink environment and incorporatesdetailed electrical generator models. This model can be used to evaluate internal drivetrain loads due to excitationsfrom both the wind and generator.

Motor current signal analysis (MCSA) provides an alternative nonintrusive approach to detect mechanical faults by using the fault signature transmitted along the torsional direction through the rotor. In existing fault detection methods based on MCSA, the gearbox health condition is monitored through the amplitude of the fault-related sidebands in the lower frequency range of the motor current spectra. However, their practical implementation is challenged by the harmonics resulting from the structural properties of the electrical machines and the inherent system imperfections. This effect is even more severe in case of a drivetrain containing planetary gearboxes due to its more complex assembly. In this paper, the resonance residual technique, which investigates the spectrum region around the resonance frequency where rich fault information may occur, is applied for the first time to MCSA to detect planetary gearbox faults. This proposed approach is verified through both simulation and experiments. A lumped parameter model for an electromechanical drive train with an annulus gear tooth crack is simulated to investigate its effect on the stator current. For experimental verification, a similar 4-kW motor-planetary gearbox- generator test rig is used. The robustness of the proposed method is demonstrated through simulations of a nonlinear finite-element model and experiments under different operating conditions. Furthermore, the effectiveness of the proposed method to extract fault information over the existing methods is also shown.