研究目的
To explore the feasibility of using the Random Forest Regression (RFR) method combined with Laser Induced Breakdown Spectroscopy (LIBS) technology for the simultaneous on-line analysis of multielements in alloy steel, aiming to improve prediction accuracy and robustness compared to traditional methods like Partial Least Squares (PLS).
研究成果
The combination of LIBS technology with RFR algorithm provides a feasible and accurate method for on-line quantitative analysis of alloy steel elements, outperforming the PLS method in terms of prediction accuracy, lower RMSE, and better robustness. The RFR correction model using partial band LIBS spectra (220-400nm) yields satisfactory results, making it a promising approach for quality control in the metallurgical industry. Future work could focus on optimizing model parameters and expanding applications to other materials.
研究不足
The study is limited to specific alloy steel samples and conditions (e.g., vacuum and 180Pa); generalization to other materials or industrial settings may require further validation. The RFR model, while accurate, may have slower modeling speed and lower generalization ability compared to PLS in some cases. The experimental setup relies on specific equipment and parameters, which might not be universally applicable.
1：Experimental Design and Method Selection:
The study combines LIBS technology with RFR algorithm for quantitative analysis of alloy steel elements. LIBS is used for real-time element detection, and RFR is employed to build a correction model to avoid overfitting and improve accuracy. The performance is compared with PLS method.
2：Sample Selection and Data Sources:
Alloy steel samples with varying concentrations of elements (Si, Mn, Cr, Ni, Cu) are used. Data is collected using an Avantes spectrometer in LIBS mode under vacuum and atmospheric conditions, with spectra averaged to improve signal-to-noise ratio.
3：List of Experimental Equipment and Materials:
Equipment includes an Nd:YAG Q laser (wavelength 1064nm, pulse width 10ns, frequency 1-10Hz, single pulse energy 50-400mJ), optical lens (size 200mm), optical fiber (diameter 100 μm, aperture 0.22mm), coupling lens (focal length 10mm), Avantes spectrometer, detector, computer, and adjustable bracket table for samples.
4：22mm), coupling lens (focal length 10mm), Avantes spectrometer, detector, computer, and adjustable bracket table for samples.
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: Laser light is focused on the sample surface to induce plasma; emitted spectral lines are sent to the spectrometer via optical fiber; data is transmitted to a computer for analysis. Spectra are preprocessed, and RFR model parameters (ntree and mtry) are optimized using Out-of-Bag (OOB) error estimation. The model is implemented in Visual Studio 2010 environment.
5：Data Analysis Methods:
RFR algorithm is used for regression, with performance evaluated using root mean square error (RMSE) and coefficient of determination (R2). Comparison is made with PLS method for prediction accuracy and robustness.
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