A vision-based hole quality assessment technique for robotic drilling of composite materials using a hybrid classification model

peer reviewedRobotic drilling has advantages over traditional computer numerical control machines due to its flexibility, dexterity and the potential for rapid production and process automation. The dexterity and reach of the robotic drill end effector enables the efficient drilling of large composite components, such as aircraft wing structures. Due to the anisotropy and inhomogeneity of fibre reinforced polymer composite materials, drilling remains a challenging task. Inspection of the drilled hole is required at the end of the process to ensure the final product is free from defects. Typically, such inspections require the parts to be transferred to a dedicated inspection station, which is a time-consuming non-value-added task and impractical for large components. In the interest of an efficient and sustainable manufacturing process, this work proposes a hybrid classification model implemented with a robotic drilling system to investigate the quality of drilled holes in-situ. The classifier is trained and tested with a random selection of drilled holes and the most accurate classifier is implemented. The selected classifier returns 90% overall prediction accuracy on unseen drilled holes. This machine learning based approach, using a convolutional neural network and support vector machine classifier, can significantly improve inspection reliability while reducing production time for drilled composite components. This is the first study that demonstrates a hole quality assessment technique for robotic drilling of composite material in-situ at scale

Intra-Body Molecular Communication via Blood-Tissue Barrier for Internet of Bio-Nano Things

Molecular communication is an emerging communication paradigm that allows bio-nanomachines to communicate using biochemical molecules as information carriers. It can be used in many promising biomedical applications such as the Internet of Bio-Nano Things (IoBNT) for targeted drug delivery and healthcare applications. In particular, the blood-tissue barrier inside the body forms the main communication pathway for molecular information exchange between the nanomachines as well as between the intra-body nanonetwork and the Bio-Cyber interface in the IoBNT network. However, overcoming this barrier by the molecules is one of the main challenges for molecular communication in the body. Therefore, spatiotemporal modeling of molecular communication across the blood-tissue barrier is of particular interest. In this paper, we develop a mathematical model and stochastic particle-based simulator for molecular communication over high spatiotemporal resolution between mobile bio-nanomachines in the blood capillary and the surrounding tissue. The transmitting bio-nanomachine is modeled as a moving sphere with a continuous emission pattern over a specific duration. In this work, the blood capillary characteristics including the blood-tissue barrier and blood flow are modelled and their effect is examined on the molecular received signal. In addition, we examined the impact of the emission duration, the elimination rate, and the separation distance on the molecular received signal. The numerical results are verified using the developed particle-based simulator. This work can help in the optimum design and development of the IoBNT systems based on molecular communication for biomedical applications such as smart drug delivery and health monitoring systems