| FAN Peng-fei,ZHANG Guan.Prediction on Geometrical Characteristics of Cermet Laser Cladding Based on Linear Regression and Neural Network[J],48(12):353-359 |
| Prediction on Geometrical Characteristics of Cermet Laser Cladding Based on Linear Regression and Neural Network |
| Received:August 31, 2019 Revised:December 20, 2019 |
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| DOI：10.16490/j.cnki.issn.1001-3660.2019.12.043 |
| KeyWord:cone bit laser cladding cermet multiple linear regression analysis artificial neural network prediction of geometrical characteristics |
| Author | Institution |
| FAN Peng-fei |
1.Xinjiang University, Urumqi , China |
| ZHANG Guan |
1.Xinjiang University, Urumqi , China; 2.Engineering Training Center of Xinjiang University, Urumqi , China |
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| Abstract: |
| The work aims to study the quantitative relationship among the key technological parameters (laser power, scanning speed, powder feeding rate) and the geometrical characteristics (width, height, bath depth) of single-track cladding layer, so as to predict the geometrical size of WC-Co50 composite coating and provide a reference for the repair of cone bit. Different experiments were designed and single-track WC-Co50 composite coatings were deposited on the surface of cone bit 15MnNi4Mo steel by 4 kW fiber laser. Meanwhile, the cross-sectional macrostructure of the single-track coating was observed and its three-dimensional size was measured by industrial microscopy. Based on the above data, multiple linear regression analysis and artificial neural network were used to establish the mathematical models between the key process parameters and the geometrical characteristics of the cladding layer. Then, the experimental results were compared with those predicted by the models. The neural network model was more accurate in predicting the cladding layer characteristics, with an average relative error of 5.3187%, while the mean relative error of multiple linear regression analysis model was 6.0028%. The analysis indicated that the prediction result of the coating width was the most accurate, and the average relative error of the two methods was only 1.2999%. Meanwhile, the prediction results of the height and molten pool depth were slightly worse, with average relative errors of 8.0586% and 7.6237% respectively. The both prediction methods have high accuracy, but the function of the neural network is vague, and the calculation process is complex. The both prediction methods have high accuracy. However, the function relationship of neural network is not clear and the operation process is complex, so further algorithm optimization is needed to improve the prediction accuracy. |
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