This research accentuates to explore designing the drone frame using Generative design tools. A quadcopter is designed using Autodesk generative design embedded in Fusion 360. The simulation results such as static stress-strain, modal frequency and displacement results of additive manufactured quadcopter are compared with a DJI flame wheel F450 drone frame. The generative designed frame has minimum displacement compared to traditional designed drone frame. It is observed that generative designing technique along with additive manufactured frames yields better frames with improved resistance to fracture and minimum displacement compared to traditional designed DJI flame wheel F450 drone frame.

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Convolutional Neural Networks need the construction of informative features, which are determined by channelwise and spatial-wise information at the network's layers. In this research, we focus on bringing in a novel solution that uses sophisticated optimization for enhancing both the spatial and channel components inside each layer's receptive field. Capsule Networks were used to understand the spatial association between features in the feature map. Standalone capsule networks have shown good results on comparatively simple datasets than on complex datasets as a result of the inordinate amount of feature information. Thus, to tackle this issue, we have proposed ME-CapsNet by introducing deeper convolutional layers to extract important features before passing through modules of capsule layers strategically to improve the performance of the network significantly. The deeper convolutional layer includes blocks of Squeeze-Excitation networks which use a stochastic sampling approach for progressively reducing the spatial size thereby dynamically recalibrating the channels by reconstructing their interdependencies without much loss of important feature information. Extensive experimentation was done using commonly used datasets demonstrating the efficiency of the proposed ME-CapsNet, which clearly outperforms various research works by achieving higher accuracy with minimal model complexity in complex datasets.