Abstract
Due to the recent advances in digitisation of the manufacturing industry and the generation of manufacturing data, there is increasing interest to integrate machine learning on the shop floor to improve efficiency and quality control. Ultrasonic welding is an emerging joining process used in various manufacturing industries, and is an energy efficient, cost-effective method of joining similar or dissimilar materials. However, the quality of the joint achievable is heavily dependent on process input parameters. In this study, a Gaussian Process Regression (GPR) model is developed to map the relationship between process parameters and joint performance for ultrasonically welded aluminium joints, with a view to improving quality control in a manufacturing setting. Initially, a 33 full factorial design of experiments is conducted to investigate the influential parameters, then a GPR model is trained on the experimental data. In-process sensor data is also used to infer process performance. To assess the prediction performance of the model, ten unseen parameter combinations are predicted and compared to their respective experimental result. The model demonstrates a high level of accuracy producing a Pearson’s correlation coefficient of 0.982 between the predicted and actual results for all data. The mean relative predictive error for unseen data is 7.35%.
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References
Wang, X.: Vehicle noise and vibration refinement. Woodhead Publishing (2010)
Day, C.P.: Robotics in industry—their role in intelligent manufacturing. Engineering 4(4), 440–445 (Aug. 01, 2018). https://doi.org/10.1016/j.eng.2018.07.012
Villegas, I.F.: Strength development versus process data in ultrasonic welding of thermoplastic composites with flat energy directors and its application to the definition of optimum processing parameters. Compos. A Appl. Sci. Manuf. 65, 27–37 (2014). https://doi.org/10.1016/j.compositesa.2014.05.019
Ni, Z.L.L., Ye, F.X.X.: Ultrasonic spot welding of aluminum alloys: A review. J. Manuf. Process. 35(July), 580–594 (2018). https://doi.org/10.1016/j.jmapro.2018.09.009
Patel, V.K., Bhole, S.D., Chen, D.L., Patel, V.K., Bhole, S.D., Chen, D.L.: Ultrasonic spot welded AZ31 magnesium alloy: Microstructure, texture, and lap shear strength. Mater. Sci. Eng., A 569, 78–85 (2013). https://doi.org/10.1016/j.msea.2013.01.042
Nong, L., Shao, C., Kim, T.H., Hu, S.J.: Improving process robustness in ultrasonic metal welding of lithium-ion batteries. J. Manuf. Syst. 48, 45–54 (2018). https://doi.org/10.1016/j.jmsy.2018.04.014
Mongan, P.G., Hinchy, E.P., O’Dowd, N.P., McCarthy, C.T.: Optimisation of ultrasonically welded joints through machine learning. Proc. CIRP 93, 527–531 (2020). https://doi.org/10.1016/j.procir.2020.04.060
Mongan, P.G., Hinchy, E.P., O’Dowd, N.P., McCarthy, C.T.: Quality prediction of ultrasonically welded joints using a hybrid machine learning model. J. Manuf. Process. 71, 571–579 (2021). https://doi.org/10.1016/J.JMAPRO.2021.09.044
Li, Y., et al.: An artificial neural network model for predicting joint performance in ultrasonic welding of composites. Proc. CIRP 76, 85–88 (2018). https://doi.org/10.1016/j.procir.2018.01.010
Zhao, D., Ren, D., Zhao, K., Pan, S., Guo, X.: Effect of welding parameters on tensile strength of ultrasonic spot welded joints of aluminum to steel—By experimentation and artificial neural network. J. Manuf. Process. 30, 63–74 (2017). https://doi.org/10.1016/j.jmapro.2017.08.009
Lee, D.Y., Leifsson, L., Kim, J.Y., Lee, S.H.: Optimisation of hybrid tandem metal active gas welding using Gaussian process regression. Sci. Technol. Weld. Joining 25(3), 208–217 (2020). https://doi.org/10.1080/13621718.2019.1666222
Rasmussen, C.E., Williams, C.: Gaussian processes for machine learning (2006)
Cheng, M., et al.: Prediction of surface residual stress in end milling with Gaussian process regression. Meas. J. Int. Meas. Confederation 178, 109333 (Jun. 2021). https://doi.org/10.1016/j.measurement.2021.109333
Leco, M., Kadirkamanathan, V.: A perturbation signal based data-driven Gaussian process regression model for in-process part quality prediction in robotic countersinking operations. Robot. Comput. Integr. Manuf. 71, 102105 (2021). https://doi.org/10.1016/j.rcim.2020.102105
Snelson, E.: Flexible and efficient Gaussian process models for machine learning (2007)
Kong, D., Chen, Y., Li, N.: Gaussian process regression for tool wear prediction. Mech. Syst. Signal Process. 104, 556–574 (2018). https://doi.org/10.1016/j.ymssp.2017.11.021
Mukesh Kumar, P.C., Kavitha, R.: Regression analysis for thermal properties of Al2O3/H2O nanofluid using machine learning techniques. Heliyon 6(6), e03966 (Jun. 2020). https://doi.org/10.1016/j.heliyon.2020.e03966
Reggente, M., et al.: Prediction of ultrafine particle number concentrations in urban environments by means of Gaussian process regression based on measurements of oxides of nitrogen. Environ. Model. Softw. 61, 135–150 (2014). https://doi.org/10.1016/j.envsoft.2014.07.012
Tolba, H., Dkhili, N., Nou, J., Eynard, J., Thil, S., Grieu, S.: GHI forecasting using Gaussian process regression: Kernel study. IFAC-PapersOnLine 52(4), 455–460 (2019). https://doi.org/10.1016/j.ifacol.2019.08.252
Balz, I., et al.: Process monitoring of ultrasonic metal welding of battery tabs using external sensor data. J. Adv. Joining Process. 1, 100005 (2020). https://doi.org/10.1016/j.jajp.2020.100005
Acknowledgement
This publication has emanated from research conducted in the Confirm Smart Manufacturing Research Centre, with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/16/RC/3918, co-funded by the European Regional Development Fund.
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Mongan, P.G., Hinchy, E.P., O’Dowd, N.P., McCarthy, C.T. (2023). Increasing Quality Control of Ultrasonically Welded Joints Through Gaussian Process Regression. In: Kim, KY., Monplaisir, L., Rickli, J. (eds) Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus. FAIM 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-17629-6_38
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