Continuous Casting Breakout Prediction via Mold Temperature Interpolation
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Solution Overview
Problem
Existing breakout prediction methods in continuous casting machines are prone to erroneous detection due to factors other than breakout signs, such as changes in casting speed or solid product width, leading to inaccurate predictions and decreased productivity.
Innovation Solution
A breakout prediction method that uses thermometers embedded in the mold to calculate sensitivity coefficients and degree of deviation through interpolation processing, reducing casting speed when a breakout is predicted to prevent shell breakage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high-speed casting is performed to improve productivity, then casting speed increases, but the thickness of the solidified shell decreases and becomes uneven, causing breakout
Solution Approach 1:
The system performs preliminary detection of breakout signs by monitoring temperature changes in the mold before actual breakout occurs. By detecting temperature patterns that indicate shell thinning or uneven solidification, the system can take preventive action to avoid breakout during high-speed casting operations.
Solution Approach 2:
The system continuously monitors temperature data from thermometers embedded in the mold and provides real-time feedback on shell thickness conditions. This feedback mechanism allows the system to detect when the solidified shell becomes too thin or uneven, enabling corrective measures to maintain shell integrity during high-speed casting.
2Reliability
If conventional temperature monitoring methods are used to detect breakout, then breakout prediction is possible, but erroneous detection occurs due to temperature changes from factors other than breakout
Solution Approach 1:
The system segments the temperature monitoring function by using multiple thermometers positioned at different locations and depths within the mold. Each thermometer provides independent data points that can be analyzed separately, allowing the system to distinguish between temperature changes caused by breakout and those caused by other factors like casting speed variations.
Solution Approach 2:
The system transitions from single-point temperature measurement to multi-dimensional temperature field monitoring. By measuring temperature at multiple positions and depths simultaneously, the system creates a three-dimensional temperature distribution map that provides context for interpreting temperature changes and reduces erroneous detections.
3Measurement precision
If multiple temperature measuring devices are arranged in arrays to improve detection accuracy, then breakout prediction improves, but device complexity increases
Solution Approach 1:
The thermometers embedded in the mold serve multiple functions: they monitor temperature for breakout detection, track shell thickness development, and provide data on solidification patterns. This multi-functionality reduces the need for separate specialized sensors, thereby limiting the increase in device complexity while maintaining high detection accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Accurately predicts breakouts, reducing the risk of erroneous detections and maintaining productivity by adjusting casting speed based on temperature changes and shell thickness.
Implementation Method 1
a plurality of thermometers 8 embedded in a mold 5
Data Source
Figure 1
Figure 2~3(b)
Figure 4(a)~4(b)
AI summary
A breakout prediction method includes: a step of inputting a dimension of a solid product withdrawn from a mold in a continuous casting machine; a step of detecting a temperature of the mold by a plurality of thermometers embedded in the mold; a step of executing interpolation processing on the detected temperatures detected by the plurality of thermometers according to the dimension of the solid product; a step of calculating, based on the temperatures calculated by executing the interpolation processing, a component in a direction orthogonal to an influence coefficient vector obtained by principal component analysis as a degree of deviation from during a normal operation in which a breakout has not occurred; and a step of predicting a breakout based on the degree of deviation.