Porosity Detection in Continuous Casting via Infrared Thermography
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Solution Overview
Problem
During the continuously casting of metal products, incorrect chemistry or cooling conditions can lead to the formation of voids in the casting process, which are detrimental to the final product, affecting its mechanical properties and leading to potential breakage or structural issues.
Innovation Solution
A porosity detection system using infrared thermography creates a natural temperature profile for the casting, fits a polynomial to it, and compares the peaks to indicate the presence of voids, allowing for real-time monitoring and optimization of the casting process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional inspection methods are used to detect voids in castings, then product quality can be ensured, but production efficiency is reduced due to time-consuming inspection processes
Solution Approach 1:
The patent replaces traditional mechanical inspection methods with infrared thermal imaging technology. The system uses infrared cameras to capture thermal profiles of the casting surface, which are then processed through polynomial fitting algorithms to detect voids. This substitution of mechanical inspection with optical-thermal detection enables real-time monitoring without interrupting the continuous casting process, thereby maintaining product quality while significantly improving production efficiency.
2Loss of substance
If real-time porosity detection is implemented, then waste can be reduced by alerting operators to process issues, but device complexity increases due to the need for infrared thermography systems and polynomial analysis
Solution Approach 1:
The patent creates a mathematical model (polynomial curve) that represents the expected thermal profile of a defect-free casting. By comparing the actual measured thermal profile against this polynomial model, the system can detect deviations indicating voids. This copying approach using mathematical modeling simplifies the detection logic while maintaining high accuracy, reducing the need for overly complex hardware systems.
Solution Approach 2:
The patent introduces polynomial fitting as an intermediary step between raw thermal data collection and void detection. The polynomial model serves as a mediator that translates complex thermal field data into a simplified reference profile, making the comparison process more manageable and the detection system less complex while still achieving accurate real-time monitoring.
3Measurement precision
If polynomial fitting is used to analyze temperature profiles, then measurement precision is improved for void detection, but calculation time increases
Solution Approach 1:
The patent applies polynomial fitting to the entire thermal profile data set to ensure comprehensive coverage and high detection accuracy. By performing the fitting calculation across all measured points rather than sampling, the system achieves maximum measurement precision. The computational burden is managed through efficient algorithms that can process the complete data set within the real-time requirements of continuous casting operations.
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
This method effectively detects internal flaws in real-time, reducing waste by alerting operators to process issues before significant product loss occurs, optimizing production speed, and ensuring accurate classification and shipping of products.
Implementation Method 1
Infrared detection device 120 may be used for detecting flaws, for example, in steel billet castings
Implementation Method 2
a natural temperature profile may be created for a casting from a first edge to a second edge
Data Source
AI summary
A computer executing a software algorithm may be used to detect a depression in a temperature profile. The temperature profile may be smoothed to eliminate noise. Next, the temperature profile's center may be extracted. A polynomial may be fitted to extracted data. An algorithm used to fit the polynomial may guarantee that the fitted curve's peak may be below the actual temperature data's peak. Next, residuals may be calculated by subtracting the fitted curve from the actual data. If there is a dip at the center, then the residuals in the center may be less than zero. The software algorithm executing on the computer may then make a decision based on a sign of the residuals. For example, residuals less than zero may indicate bar porosity. Residuals above zero may indicate no porosity. The magnitude of the residuals may then be used to classify a size of a detected defect.