The development of a digital twin framework for an industrial robotic drilling process

A digital twin is a digital representation of a physical entity that is updated in real-time by transfer of data between physical and digital (virtual) entities. In this manuscript we aim to introduce a digital twin framework for robotic drilling. Initially, a generic reference model is proposed to highlight elements of the digital twin relevant to robotic drilling. Then, a precise reference digital twin architecture model is developed, based on available standards and technologies. Finally, real-time visualisation of drilling process parameters is demonstrated as an initial step towards implementing a digital twin of a robotic drilling process.</p

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

Robotic 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.</p

The PHARMS (Patient Held Active Record of Medication Status) feasibility study: a research proposal

Abstract Objective Medication errors are a major source of preventable morbidity, mortality and cost and many occur at the times of hospital admission and discharge. Novel interventions (such as new methods of recording medication information and conducting medication reconciliation) are required to facilitate accurate transfer of medication information. With existing evidence supporting the use of information technology and the patient representing the one constant in the care process, an electronic patient held medication record may provide a solution. This study will assess the feasibility of introducing a patient held electronic medication record in primary and secondary care using the Consolidated Framework for Implementation Research (CFIR).This feasibility study is a mixed method study of community dwelling older adult patients admitted to an urban secondary care facility comprising a non-randomised intervention and qualitative interviews with key stakeholders. Outcomes of interest include clinical outcomes and process evaluation.This study will yield insights pertaining to feasibility, acceptability and participation for a more definitive evaluation of the intervention. The study also has the potential to contribute to knowledge of implementation of technology in a healthcare context and to the broader area of implementation science