Full
Outline
To enhance the accuracy of wear prediction for coating layers on descaling rolls in critical metallurgical components and improve high-pressure water descaling efficiency, the application of machine learning in predicting the wear of the surface coating of the descaling rollser was deeply studied. The principles and structures of the prevalent algorithms of the multiple linear regression, the X G Boost and the random forest wear prediction model were introduced respectively. A data set was established using production data to predict wear coating on the surface of the descaling rolls. The predicted results were compared longitudinally with the measured ones, and the prediction results of the three algorithms were compared horizontally. The random forest wear prediction model achieved the highest prediction accuracy rates of 90.4% and 95.9% at error tolerances of ±0.001 0 mm and ±0.001 5 mm, respectively. Among the four performance indicators, the model achieved the highest coefficient of determination, while exhibiting the lowest values for root mean square error, mean square error, and mean absolute error in the other three indicators. The random forest wear prediction model proved optimal for actual production, providing high-value guidance for the setting of the deformation and reengineering empowerment mechanism of descaling rolls, which were key metallurgical components.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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