Experimental, numerical, and multi-objective optimization analysis of atmospheric pressure cold plasma treatment on aluminum 7075 T6-CFRP adhesive joints
Materialpruefung/Materials Testing, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1515/mt-2026-0030
- Dergi Adı: Materialpruefung/Materials Testing
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex
- Anahtar Kelimeler: atmospheric pressure cold plasma treatment, contact angle, finite element analysis, lap shear strength, NSGA-II
- Bursa Uludağ Üniversitesi Adresli: Evet
Özet
In this study, the effects of atmospheric pressure cold plasma treatment on surface contact angles and adhesive joint performance of aluminum 7075-T6 alloy and carbon fiber reinforced polymer composites were investigated using experimental, numerical, and statistical methods. A full factorial experimental design was applied to evaluate three plasma parameters: application distance, number of passes, and application speed. Contact angle measurements were conducted to assess surface activation, and single lap adhesive joints were fabricated and tested to determine lap shear strength. The results were analyzed using analysis of variance to identify significant parameters and their contribution ratios. Process–output relationships were modeled using response surface methodology. Finite element analyses with LS-DYNA were performed to investigate stress distributions and mechanical behavior of the adhesive joints. Multi-objective optimization was carried out using the non-dominated sorting genetic algorithm II to minimize contact angles and maximize lap shear strength. Application distance was found to be the most influential parameter. The lowest contact angles and highest lap shear strength were achieved at high number of passes and low application distance and speed. Numerical results showed good agreement with experiments, and a Random Forest model was used to improve prediction accuracy within the optimization framework.