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In order to achieve intelligent predication of magnesium alloy die-casting parts, reduce offline labor inspection cost, and improve intelligent level of magnesium alloy die-casting industry, this paper collects big data on “process parameters-quality parameters” of large thin-walled magnesium alloy castings, and uses random forest model to establish the relationship between process parameters and the types of defects in castings, and analyzes the effect of long-tailed distribution of labels in the industrial data on the predictive performance of machine learning models. Then the “Random Downsampling + SMOTE Over-sampling” algorithm is emptoyed to balance the distribution of the data set. Finally, an accurate prediction model with an accuracy of 89.54%, an area under ROC curve of 0.983 8, and an average true rate of 87.65% are obtained, which achieves a precise detection of a small number of defective samples, and obtains the ranking of the importance of key process parameters for magnesium alloy casting.
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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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