Thermal and mechanical properties of a new insulation composite material


Kocyigit F., KAYA A. M.

MATERIALS TESTING, cilt.65, sa.9, ss.1453-1463, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 65 Sayı: 9
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1515/mt-2023-0035
  • Dergi Adı: MATERIALS TESTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex
  • Sayfa Sayıları: ss.1453-1463
  • Anahtar Kelimeler: abrasion loss, compressive strength, insulation composite material, optimization, Taguchi, thermal conductivity, EXPANDED PERLITE AGGREGATE, WASTE MARBLE DUST, OPTIMIZATION ALGORITHM, DESIGN OPTIMIZATION, FLY-ASH, CONCRETE BLOCKS, SILICA FUME, LIGHTWEIGHT, CLAY, CONDUCTIVITY
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

The Taguchi optimization method was used to optimize waste and natural different components such as waste marble dust, expanded perlite, perlite aggregate size, cement, and molten tragacanth in the production of new insulation composite material. Compressive strength, thermal conductivity, abrasion loss, and water adsorption properties of the developed composite material were investigated. Taguchi's standard L18 array was chosen for optimization of these four components with different levels. Response plots were created using the Taguchi and the optimum test condition was determined. The insulation composite material with the best thermal and mechanical properties was obtained under the condition of waste marble dust (1), expanded perlite (1), perlite aggregate size (1) and molten tragacanth (1). In addition, using the anova (Analysis of Variance), percentage impacts on the mechanical and thermal properties of the test parameters were determined. Statistical values obtained from anova and mathematical models are developed by using multi-linear regression method. It was found that the mathematical model and the experimental results were quite compatible. The optimum test conditions detected were verified by confirmation experiments. Confirmation experiment results were obtained between 99.9 % confidence interval values.