Response surface-guided multi-objective optimization of CR/NR blend mechanical and rheological properties using artificial bee colony algorithm
Polymer Bulletin, cilt.83, sa.11, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 83 Sayı: 11
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s00289-026-06653-6
- Dergi Adı: Polymer Bulletin
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chemical Abstracts Core, Chimica, Compendex, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: Chloroprene, Multi-objective Artificial Bee Colony Algorithm, Response Surface Methodology, Rubber, Vulcanization
- Eskişehir Osmangazi Üniversitesi Adresli: Evet
Özet
This study investigates the optimization of vulcanization characteristics and mechanical properties of Chloroprene Rubber (CR) and Natural Rubber (NR) blends by integrating Response Surface Methodology (RSM) with the Artificial Bee Colony (ABC) algorithm. Since CR is widely used in the automotive industry but is relatively expensive, blending it with NR was explored as a strategy to achieve a cost–performance balance suitable for industrial applications. The effects of formulation parameters—including accelerators, retarders, vulcanization agents, and the NR proportion—on key response variables were systematically examined. In the first stage, experiments were designed using a Central Composite Design (CCD), and regression models for hardness (H), tensile strength (TS), and Tan δ were developed based on Analysis of Variance (ANOVA). In the second stage, these models were integrated into a multi-objective optimization framework using the ABC algorithm. To address conflicting objectives, scalarization methods (weighted sum, conic, and Tchebycheff) were employed. The Conic Scalarization Method (CSM) yielded the most robust optimization performance, allowing flexible trade-offs between mechanical strength and dynamic properties. For instance, prioritizing Tan δ minimization (w₃ = 0.8) resulted in a Tan δ value of 0.076, with corresponding hardness and tensile strength of 49.53 ShA and 16.23 MPa, respectively. Conversely, when H was prioritized (w₁ = 0.8), values of 57.15 ShA hardness, 19.38 MPa tensile strength, and 0.198 Tan δ were obtained. Prioritizing TS (w₂ = 0.8) yielded 24.86 MPa, with 53.60 ShA hardness and 0.238 Tan δ. These findings highlight that the proposed CSM-based MOABC approach enables engineers to adjust CR/NR formulations according to targeted design requirements, offering a versatile tool for developing cost-effective and high-performance automotive rubber components.