ULTRASONIC WELDING OF CF/PEKK TO CF/EPOXY THROUGH OPTIMISATION USING MACHINE LEARNING

Vedant Modi, Patrick Mongan, Karthik Ramaswamy, Tomas Flanagan, Conor T. McCarthy, Ronan M. O'Higgins

Research output: Contribution to conferencePaperpeer-review

Abstract

The application of fibre-reinforced thermoset material systems has been established in the aerospace industry, e.g. primary structure on commercial aircrafts. However, there is an increasing interest in thermoplastic-based material systems due to their potential for fast forming, weldability, their inherently superior fatigue performance, and excellent fire/smoke/toxicity (FST) properties. Current repair techniques for thermoset panels are adhesive bonding and mechanical fastening. However, these techniques are limited when applied to thermoplastic composites as mechanical fastening leads to stress concentrations and localized delamination which is worse for thermoplastic composites. In this paper, the optimisation of dissimilar material was carried out using a hybrid genetic algorithm - artificial neural network (GA-ANN) model. Due to the complexity of the ultrasonic welding (USW) process, Bayesian optimisation is adapted to determine the most suitable ANN architecture to develop a robust model. The predictive model is developed to map the relationship between welding energy, vibration amplitude, and welding force to the corresponding Lap Shear Strength (LSS). The model was trained on 27 experiments using the leave-one-out cross-validation method to measure the model's ability to generalise. To evaluate the optimised joint performance, The bonded joints were tested to determine the tensile load carrying capability, and their failure modes were analyzed with the primary aim to develop an efficient repair joining methodology.

Original languageEnglish
Publication statusPublished - 2023
Event23rd International Conference on Composite Materials, ICCM 2023 - Belfast, United Kingdom
Duration: 30 Jul 20234 Aug 2023

Conference

Conference23rd International Conference on Composite Materials, ICCM 2023
Country/TerritoryUnited Kingdom
CityBelfast
Period30/07/234/08/23

Keywords

  • Dissimilar materials
  • Machine learning
  • Ultrasonic welding

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