Using open-source microcontrollers to enable digital twin communication for smart manufacturing

One of the key enabling technologies for the smart factory is the Digital Twin, which can be described as a digital model of a physical entity. In smart manufacturing, a digital twin, also referred to as a cyber-physical model of a physical entity. In smart manufacturing, a digital twin, also referred to as a cyber-physical production system, comprises three integrated components: the product, the process and the machine. In this paper, the design, fabrication and development of a proof of principal manufacturing digital twin demonstrator is discussed. The manufacturing process is a V-bending operation of a thin metallic plate. All three components of a manufacturing digital twin are represented: the metallic plate product, the bending process and the bending machine. Low-cost, IoTenabled, open-source microcontrollers are used to communicate between the physical and the digital twins. The microcontroller is used to control machine operations, while also extracting sensor data from the system. It is demonstrated that the digital twin enables real-time stress prediction in the product during the bending process, while also tracking and recording machine performance. An information dashboard has also been developed which presents key performance indicators to the end user

A model-based approach to automated validation and generation of PLC code for manufacturing equipment in regulated environments

Validation is a critical stage of the equipment design process as it provides documentary evidence that the equipment is performing as per specification and ensures consistent product quality is maintained at all times. The advent of Industry 4.0 has led to a requirement for reconfigurable manufacturing systems as manufacturers adapt to an increased customer demand for personalised products. As equipment control software becomes increasingly complex to accommodate these equirements, a new approach to equipment validation is required. This paper presents a methodology for the design and validation of equipment in regulated manufacturing environments, using a model-based design platform (MathWorks® Simulink®) to model and digitally validate the Programmable Logic Controller (PLC) code required to control manufacturing equipment. A workflow is presented detailing the steps required to implement this approach and a demonstration model was developed as a proof of concept. Validation documentation and PLC code are automatically generated based on the system model and the functionality of the generated PLC code was successfully verified on a physical demonstrator, proving the feasibility of the proposed approach. Adoption of the approach outlined in this work would enable manufacturers in regulated industries, such as medical devices and pharmaceutical products, to rapidly design, build, reconfigure and revalidate manufacturing equipment as required to accommodate an increased demand for customised products. </p

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

Multi-objective optimisation of ultrasonically welded dissimilar joints through machine learning

The use of composite materials is increasing in industry sectors such as renewable energy generation and storage, transport (including automotive, aerospace and agri-machinery) and construction. This is a result of the various advantages of composite materials over their monolithic counterparts, such as high strength-to-weight ratio, corrosion resistance, and superior fatigue performance. However, there is a lack of detailed knowledge in relation to fusion joining techniques for composite materials. In this work, ultrasonic welding is carried out on a carbon fibre/PEKK composite material bonded to carbon fibre/epoxy composite to investigate the influence of weld process parameters on the joint’s lap shear strength (LSS), the process repeatability, and the process induced defects. A 33 parametric study is carried out and a robust machine learning model is developed using a hybrid genetic algorithm–artificial neural network (GA–ANN) trained on the experimental data. Bayesian optimisation is employed to determine the most suitable GA–ANN hyperparameters and the resulting GA–ANN surrogate model is exploited to optimise the welding process, where the process performance metrics are LSS, repeatability and joint visual quality. The prediction for the optimal LSS was subsequently validated through a further set of experiments, which resulted in a prediction error of just 3%