Advanced search
2 files | 839.21 KB Add to list
Author
Organization
Project
Abstract
In this research, Artificial Intelligence (AI) was used to support the optimization of six bonding process parameters for maximal joint strength and minimal production costs. Two industrial bonding processes were investigated, one from electronic potting and another from the manufacturing industry. The focus was on optimizing the plasma treatment of the substrate materials. Two approaches for optimization were compared, namely the traditional approach where the adhesive expert proposes experiments and interpret the results, and an AI approach with Bayesian optimization and Gaussian process models. Similar joint strengths could be achieved via the Bayesian optimization approach with 40% less budget to find the optimum compared to the traditional approach. Additionally, in the electronic potting process, the AI approach resulted in 18% reduction in production cost, while achieving a similar joint strength, compared to the traditional approach. Ageing of the samples did not result in a significant drop in joint strength nor changes in failure type or mechanism. This indicates that AI can support adhesive experts to find the optimal bonding process settings and manufacture robust and cost-efficient adhesive bonds.

Downloads

  • (...).pdf
    • full text (Published version)
    • |
    • UGent only
    • |
    • PDF
    • |
    • 400.08 KB
  • 8472 acc.pdf
    • full text (Accepted manuscript)
    • |
    • open access
    • |
    • PDF
    • |
    • 439.12 KB

Citation

Please use this url to cite or link to this publication:

MLA
Jordens, Jeroen, et al. “Optimization of Plasma-Assisted Surface Treatment for Adhesive Bonding via Artificial Intelligence.” 2nd International Conference on Industrial Applications of Adhesives 2022 : Selected Contributions of IAA 2022, edited by Lucas F. M. da Silva et al., Springer, 2023, pp. 47–64, doi:10.1007/978-3-031-11150-1_4.
APA
Jordens, J., Van Doninck, B., Satrio Loka, N. R. B., Morales Hernández, A., Couckuyt, I., Van Nieuwenhuyse, I., & Witters, M. (2023). Optimization of plasma-assisted surface treatment for adhesive bonding via artificial intelligence. In L. F. M. da Silva, R. D. Adams, & K. Dilger (Eds.), 2nd International Conference on Industrial Applications of Adhesives 2022 : selected contributions of IAA 2022 (pp. 47–64). https://doi.org/10.1007/978-3-031-11150-1_4
Chicago author-date
Jordens, Jeroen, Bart Van Doninck, Nasrulloh Ratu Bagus Satrio Loka, Alejandro Morales Hernández, Ivo Couckuyt, Inneke Van Nieuwenhuyse, and Maarten Witters. 2023. “Optimization of Plasma-Assisted Surface Treatment for Adhesive Bonding via Artificial Intelligence.” In 2nd International Conference on Industrial Applications of Adhesives 2022 : Selected Contributions of IAA 2022, edited by Lucas F. M. da Silva, Robert D. Adams, and Klaus Dilger, 47–64. Springer. https://doi.org/10.1007/978-3-031-11150-1_4.
Chicago author-date (all authors)
Jordens, Jeroen, Bart Van Doninck, Nasrulloh Ratu Bagus Satrio Loka, Alejandro Morales Hernández, Ivo Couckuyt, Inneke Van Nieuwenhuyse, and Maarten Witters. 2023. “Optimization of Plasma-Assisted Surface Treatment for Adhesive Bonding via Artificial Intelligence.” In 2nd International Conference on Industrial Applications of Adhesives 2022 : Selected Contributions of IAA 2022, ed by. Lucas F. M. da Silva, Robert D. Adams, and Klaus Dilger, 47–64. Springer. doi:10.1007/978-3-031-11150-1_4.
Vancouver
1.
Jordens J, Van Doninck B, Satrio Loka NRB, Morales Hernández A, Couckuyt I, Van Nieuwenhuyse I, et al. Optimization of plasma-assisted surface treatment for adhesive bonding via artificial intelligence. In: da Silva LFM, Adams RD, Dilger K, editors. 2nd International Conference on Industrial Applications of Adhesives 2022 : selected contributions of IAA 2022. Springer; 2023. p. 47–64.
IEEE
[1]
J. Jordens et al., “Optimization of plasma-assisted surface treatment for adhesive bonding via artificial intelligence,” in 2nd International Conference on Industrial Applications of Adhesives 2022 : selected contributions of IAA 2022, Carvoeiro, Portugal, 2023, pp. 47–64.
@inproceedings{01HMRQKQW97KVGN9KECWBQ5HZS,
  abstract     = {{In this research, Artificial Intelligence (AI) was used to support the optimization of six bonding process parameters for maximal joint strength and minimal production costs. Two industrial bonding processes were investigated, one from electronic potting and another from the manufacturing industry. The focus was on optimizing the plasma treatment of the substrate materials. Two approaches for optimization were compared, namely the traditional approach where the adhesive expert proposes experiments and interpret the results, and an AI approach with Bayesian optimization and Gaussian process models. Similar joint strengths could be achieved via the Bayesian optimization approach with 40% less budget to find the optimum compared to the traditional approach. Additionally, in the electronic potting process, the AI approach resulted in 18% reduction in production cost, while achieving a similar joint strength, compared to the traditional approach. Ageing of the samples did not result in a significant drop in joint strength nor changes in failure type or mechanism. This indicates that AI can support adhesive experts to find the optimal bonding process settings and manufacture robust and cost-efficient adhesive bonds.}},
  author       = {{Jordens, Jeroen and Van Doninck, Bart and Satrio Loka, Nasrulloh Ratu Bagus and Morales Hernández, Alejandro and Couckuyt, Ivo and Van Nieuwenhuyse, Inneke and Witters, Maarten}},
  booktitle    = {{2nd International Conference on Industrial Applications of Adhesives 2022 : selected contributions of IAA 2022}},
  editor       = {{da Silva, Lucas F. M. and Adams, Robert D. and Dilger, Klaus}},
  isbn         = {{9783031111495}},
  issn         = {{2731-0221}},
  language     = {{eng}},
  location     = {{Carvoeiro, Portugal}},
  pages        = {{47--64}},
  publisher    = {{Springer}},
  title        = {{Optimization of plasma-assisted surface treatment for adhesive bonding via artificial intelligence}},
  url          = {{http://doi.org/10.1007/978-3-031-11150-1_4}},
  year         = {{2023}},
}

Altmetric
View in Altmetric