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Optimisation of ultrasonically welded joints through machine learning

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Date
2020
Abstract
The quality of joint achievable through ultrasonic welding is highly dependent on the process input parameters. In this study an artificial neural network (ANN) is combined with a genetic algorithm (GA) to develop a high-fidelity model for predicting the strength of ultrasonically welded joints. Initial weights of the ANN were optimized using the GA. The model was then trained by the Levenberg-Marquardt algorithm on 27 training experiments and validated on 10 experiments. The model demonstrated a high level of accuracy with a mean relative error of 6.79% on validation data and a correlation coefficient of 0.9827 for all 37 experiments.
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Description
peer-reviewed
Publisher
Elsevier
Citation
Procedia CIRP; pp.527-531
Collections
School of Engineering
Bernal Institute
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Mongan_2020_Optimisation.pdf
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Keywords
ultrasonic wedling, machine learning, artificial neural networks, Engineering
ULRR Identifiers
https://hdl.handle.net/10344/9493
 https://doi.org/10.34961/2905
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Funding Information
Science Foundation Ireland (SFI)
Sustainable Development Goals
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