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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2002.6
- 수록면
- 226 - 232 (7page)
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초록· 키워드
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POSCO #2 Stainless steel making plant produces more than 600 thousand ton per year with a variety of products consisting of austenite and ferrite stainless steel to meet customers’needs since 1996.
The plant has four different major processes, that are, EAF-AOD-VOD-CC to finally produce semiproduct called as slab. In this study, we importantly took AOD process into consideration due to its roles such as to check and verify the final qualities through
sampling inspection. But the lead-time from sampling to its verification takes five to ten minutes causing productivity loss as much as the lead-time as a result. Of all indices for quality and process control the plant has, carbon ingredient in liquid type of steel is the most important since it affects in a great way to
the characteristics of steel, if any problem, customers not satisfied with quality could issue a claim; therefore there is no way but to guarantee it before delivery.
In this study, to reasonably reduce lead-time can save a cycle time and finally improve our productivity from a state-of-art alternative just such as applying statistical model based on multiregression analysis into the A.O.D line by analyzing
the statistical and technical relationship between carbon and the relevant some vital independent variables.
In consequence, the model with R-square 87% allowed the plant to predict, abbreviating the process in relations to sampling to verification, approximately the value of [C] so that operators
could run the process line with reliability on data automatically calculated instead of actual inspection. In the future, we are going to do the best to share this type of methodology with other processes, if possible, to apply into them.
The plant has four different major processes, that are, EAF-AOD-VOD-CC to finally produce semiproduct called as slab. In this study, we importantly took AOD process into consideration due to its roles such as to check and verify the final qualities through
sampling inspection. But the lead-time from sampling to its verification takes five to ten minutes causing productivity loss as much as the lead-time as a result. Of all indices for quality and process control the plant has, carbon ingredient in liquid type of steel is the most important since it affects in a great way to
the characteristics of steel, if any problem, customers not satisfied with quality could issue a claim; therefore there is no way but to guarantee it before delivery.
In this study, to reasonably reduce lead-time can save a cycle time and finally improve our productivity from a state-of-art alternative just such as applying statistical model based on multiregression analysis into the A.O.D line by analyzing
the statistical and technical relationship between carbon and the relevant some vital independent variables.
In consequence, the model with R-square 87% allowed the plant to predict, abbreviating the process in relations to sampling to verification, approximately the value of [C] so that operators
could run the process line with reliability on data automatically calculated instead of actual inspection. In the future, we are going to do the best to share this type of methodology with other processes, if possible, to apply into them.
목차
Abstract
1. Introduction
2. Body
3. Conclusion
4. Further Study
참고문헌 (0)
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