Functionally Graded High-Entropy Coatings: Design, Processing and Applications
Engineering Materials, Springer International Publishing Ag, ss.407-496, 2026
- Yayın Türü: Kitapta Bölüm / Araştırma Kitabı
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/978-3-032-21001-2_9
- Yayınevi: Springer International Publishing Ag
- Sayfa Sayıları: ss.407-496
- Anahtar Kelimeler: Coatings, Corrosion, Functionally graded, High entropy, Mechanical properties, Microstructure, Tribology
- Karadeniz Teknik Üniversitesi Adresli: Evet
Özet
Functionally graded materials (FGMs) effectively address interfacial mismatches by gradually varying composition and structure, resulting in improved thermal, mechanical, and tribological properties. When combined with high-entropy materials (HEMs)—comprising multiple principal elements with superior structural and functional attributes—functionally graded high-entropy coatings (FGHECs) emerge as a novel class of advanced materials for demanding applications. Although high-entropy coatings have been widely studied, FGHECs remain relatively unexplored, with limited literature mostly consisting of isolated experimental efforts. This chapter seeks to bridge this gap by providing the first comprehensive review of FGHECs, covering design principles, synthesis strategies, characterization tools, and application potentials. It will begin by discussing the fundamentals of functional grading and its integration with high-entropy alloy concepts. Processing techniques such as thermal spraying, physical vapor deposition (PVD), and additive manufacturing will be examined in terms of their suitability for creating compositional gradients. The influence of processing parameters on microstructure and performance will also be evaluated. Advanced characterization methods for analyzing compositional and microstructural gradients will be detailed. Industrially relevant case studies from aerospace, biomedical, and energy sectors will be presented. Finally, the chapter will highlight future directions including modeling, scalability, and AI-driven design strategies for next-generation FGHECs.