This study addresses the challenge of imputing missing values in incomplete process data collected from high-cost data acquisition environments. Such missingness arises due to insufficient completeness, accuracy, and consistency, which significantly affect the quality of critical-to-quality (CTQ) attributes in manufacturing processes. We systematically evaluate three state-of-the-art imputation methods—Multiple Imputation by Chained Equations (MICE), the machine learning-based missForest algorithm, and a deep learning- based one-dimensional convolutional neural network (1D-CNN)—using real-world industrial data. Our analysis aims to identify the most effective imputation technique for handling complex and noisy process datasets typical in manufacturing settings. The results highlight the strengths and limitations of each method, providing practical guidance for selecting appropriate imputation approaches to improve the reliability of quality prediction and decision-making in industrial applications.

1. 서 론
2. 선행 연구
    2.1 결측치의 대체
    2.2 친환경 시멘트 공정 설계의 결측치 해소
3. 적용 모델과 활용 데이터
    3.1 적용 모델
    3.2 활용 데이터
4. 실험내용 및 결과
    4.1 실험내용
    4.2 실험결과
5. 결론 및 향후 연구과제
Acknowledgement
References

• Jae-Ho Bae(Department of Safety and Health Management, Osan University) | 배재호 (오산대학교 안전보건관리학과) Corresponding author
• Sun-mi Choi(CSM Co., Ltd.) | 최선미 (㈜씨에스엠)
• Seong-Yoon Bae(Department of AI, Big data Management, Kookmin University) | 배성윤 (국민대학교 AI빅데이터융합경영학과)