Cited 0 time in
Advanced Machining Technologies for CVD-SiC: Hybrid Approaches and AI-Enhanced Control for Ultra-Precision
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Han, Su-Yeon | - |
| dc.contributor.author | Lee, Seung-Min | - |
| dc.contributor.author | Jang, Min-Su | - |
| dc.contributor.author | Yang, Ho-Soon | - |
| dc.contributor.author | Kwak, Tae-Soo | - |
| dc.date.accessioned | 2026-01-12T06:00:09Z | - |
| dc.date.available | 2026-01-12T06:00:09Z | - |
| dc.date.issued | 2025-12 | - |
| dc.identifier.issn | 2076-3417 | - |
| dc.identifier.uri | https://scholarworks.gnu.ac.kr/handle/sw.gnu/81801 | - |
| dc.description.abstract | Chemically vapor-deposited silicon carbide (CVD-SiC) is a high-performance material that possesses excellent mechanical, chemical, and electrical properties, making it highly promising for components in the semiconductor, aerospace, and automotive industries. However, its inherent hardness and brittleness present significant challenges to precision machining, thereby hindering the commercialization of reliable, high-precision parts. Therefore, the application of CVD-SiC in fields that require ultra-precision shaping and nanometric surface finishing necessitates the exploration of machining methods specifically tailored to the material's unique characteristics. This paper presents a comprehensive review of CVD-SiC machining-from traditional mechanical approaches to advanced hybrid and high-energy techniques-aimed at overcoming machining limitations from its material properties and achieving high-efficiency and nanometric-quality machining. The study discusses various grinding tools designed for superior surface finishing and efficient material removal, as well as machining techniques that utilize micro-scale removal mechanisms for ductile regime machining. Looking ahead, the integration of AI-based process optimization with enhanced machining methods is expected to fully exploit the superior properties of CVD-SiC and broaden its industrial application as a high-performance material. | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | MDPI | - |
| dc.title | Advanced Machining Technologies for CVD-SiC: Hybrid Approaches and AI-Enhanced Control for Ultra-Precision | - |
| dc.type | Article | - |
| dc.publisher.location | 스위스 | - |
| dc.identifier.doi | 10.3390/app152412892 | - |
| dc.identifier.scopusid | 2-s2.0-105025918438 | - |
| dc.identifier.wosid | 001646152800001 | - |
| dc.identifier.bibliographicCitation | Applied Sciences-basel, v.15, no.24 | - |
| dc.citation.title | Applied Sciences-basel | - |
| dc.citation.volume | 15 | - |
| dc.citation.number | 24 | - |
| dc.type.docType | Review | - |
| dc.description.isOpenAccess | Y | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Chemistry | - |
| dc.relation.journalResearchArea | Engineering | - |
| dc.relation.journalResearchArea | Materials Science | - |
| dc.relation.journalResearchArea | Physics | - |
| dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
| dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
| dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
| dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
| dc.subject.keywordPlus | DEPOSITED SILICON-CARBIDE | - |
| dc.subject.keywordPlus | SURFACE CHARACTERISTICS | - |
| dc.subject.keywordPlus | TOOL | - |
| dc.subject.keywordAuthor | chemically vapor-deposited silicon carbide (CVD-SiC) | - |
| dc.subject.keywordAuthor | machining | - |
| dc.subject.keywordAuthor | AI-based process | - |
| dc.subject.keywordAuthor | removal process | - |
| dc.subject.keywordAuthor | surface roughness | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Gyeongsang National University Central Library, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do, 52828, Republic of Korea+82-55-772-0532
COPYRIGHT 2022 GYEONGSANG NATIONAL UNIVERSITY LIBRARY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.
