Showing papers by "Noel P. O’Dowd published in 2022"

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

Abstract: 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%.

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

Abstract: 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%.

The Development of a Digital Twin Framework for an Industrial Robotic Drilling Process

Abstract: 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.

A Model-Based Approach to Automated Validation and Generation of PLC Code for Manufacturing Equipment in Regulated Environments

Abstract: 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 requirements, 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.

FEA Dual Model and Experimental Characterization to Investigate the Effect of an FCC Microstructure on the Macroscale and Microscale Stresses of a PZT Actuated Vibrating Thin Plate

Abstract: A numerical finite element analysis (FEA) dual model which combines a macroscale (Ansys) and a microscale (Abaqus) model, was employed to investigate the effect of a face centre cubic (FCC) microstructure on the macroscale and microscale stresses of a piezoelectrically (PZT) actuated Pd/Ni alloy thin plate subjected to high frequency vibration [...]