Optimization of hole quality in drilling of direct hot-pressed Al/SiC composites using Taguchi method
SCIENTIFIC REPORTS, cilt.16, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1038/s41598-026-42714-6
- Dergi Adı: SCIENTIFIC REPORTS
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Chemical Abstracts Core, MEDLINE, Directory of Open Access Journals
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Dokuz Eylül Üniversitesi Adresli: Evet
Özet
In this study, aluminium (Al) matrix composites reinforced with 0, 5, and 10 vol% silicon carbide (SiC) particles were fabricated by direct hot-pressing under 35 MPa pressure at 600 degrees C for a holding time of 5 min. The morphological characteristics of the initial Al and SiC powders were examined using scanning electron microscopy. The fabricated composites were characterized through density measurements, microstructural analysis, X-ray diffraction, and microhardness testing. Microstructural observations confirmed a homogeneous distribution of SiC particles within the Al matrix. Although the relative density decreased with increasing SiC content, microhardness increased due to restricted dislocation motion induced by the hard ceramic reinforcement. The cutting speed, feed rate, point angle, and reinforcement ratio were selected as control factors in the drilling process of Al/SiC composites. In contrast, thrust force, surface roughness, deviation from diameter, and deviation from circularity were selected as performance indicators. The Taguchi method was used to determine the optimal experimental conditions for hole performance indicators. The effects of drilling parameters on the drill bit, hole quality, and chip morphology were investigated. The contribution ratios of the control factors on the responses were determined using analysis of variance. The feed rate was found to be the most effective control factor on hole quality. Regression analysis was applied to establish a mathematical relationship between the control factors and the responses. The R2 values obtained from the regression equations were found to be quite high. Finally, confirmation experiments conducted with the determined optimal parameter sets have proven the validity of the models by yielding statistically significant results within a 95% confidence interval.