Bayesian techniques are vital in mechanical manufacturing for uncertainty quantification and process optimization. This review explores their diverse applications, highlighting advantages in handling small data and incorporating expertise for improved decision-making in quality control, reliability, and machining. It also discusses integration with machine learning and applications in specialized areas. Future research should focus on Industry 4.0 integration and user-friendly tools, emphasizing Bayesian methods' role in intelligent manufacturing.

Abstract
1. 서 론
2. 이론적 배경
3. 제조 분야에서의 응용 사례
    3.1. 품질 관리 및 프로세스 모 니터링
    3.2. 신뢰성 엔지니어링 및 유지 관리
    3.3. 가공 작업 및 안정성 분석
    3.4 머 신러닝 및 인과 추론
    3.5 시뮬레이션 및 모 델링
4. 특수 제조 분야에서의 응용
    4.1. 생물공정의 제조
    4.2. 메 타물질의 설계
    4.3. 반도체 제조
5. 향후 연구를 위한 시사점과결론
References

• 변해원(Dept. of Future Technology, Korea University of Technology and Education, South Korea) | Haewon Byeon Corresponding author