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%.
Highlights
Manufacturing of lightweight components is at the research forefront in the transportation industry (Kim et al, 2019), as a result of the ever-increasing demand for weight reduction due to increasing environmental restrictions regarding harmful emissions
The lap shear strength (LSS) response envelope for the ultrasonic welding scenario implemented in this study is extremely non-linear with respect to process input parameters
The multi-objective optimisation model developed in this study demonstrates the ability to efficiently optimise the LSS using a hybrid genetic algorithm–artificial neural network (GA–Artificial neural networks (ANN)) model from a single design of experiment (DoE) dataset
Summary
Manufacturing of lightweight components is at the research forefront in the transportation industry (Kim et al, 2019), as a result of the ever-increasing demand for weight reduction due to increasing environmental restrictions regarding harmful emissions. The joining of thermoset matrix composites components to advanced thermoplastic matrix components is an active area of research that requires innovative joining technologies. One such technology, showing promising potential in this space is ultrasonic welding (USW). This joining method works by converting high frequency electrical energy (typically 10–70 kHz) into high frequency low amplitude mechanical vibrations (10–250 μm). It has been shown that joint quality shows a strong dependence on weld input parameters and the relationships are extremely non-linear (Mongan et al, 2021). The complex non-linear relationships are difficult to model using traditional techniques
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