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Dual−phase steel with improved formability (DH steel) is developed as an evolution of conventional dual−phase steel (DP steel) to meet the increased ductility requirements associated with the fabrication of complex−shaped automotive components. Currently, DH steel with a tensile strength of 980 MPa has reached mass production, while the development of DH steel with a tensile strength of 1180 MPa has attracted significant research interest. In this study, a performance−driven machine learning methodology was employed to design the chemical composition and processing parameters of 1180 MPa−grade DH steel. Additionally, interpretable machine learning techniques were used to elucidate the fundamental relationships between the microstructural characteristics and mechanical properties. Initially, leveraging data extracted from the literature, a composition and process−performance predictive model was developed using a neural network algorithm. Subsequently, a multi−objective genetic algorithm was implemented to efficiently design the chemical composition of the novel DH steel. Following this, based on orthogonal experimental data concerning the processing parameters of the newly designed DH steel, a random forest algorithm was applied to construct predictive models for tensile strength and fracture elongation, with processing parameters serving as input variables. An optimized set of preparation process parameters was determined using a multi−objective genetic optimization algorithm. The resulting parameters are as follows: a coiling temperature of 510°C, an annealing temperature of 860°C, an annealing duration of 160 s, a slow cooling temperature of 715°C, an over−aging temperature of 340°C, and an over−aging duration of 110 s. The resulting DH steel demonstrated an exceptional balance between strength and ductility, achieving a tensile strength of 1214 MPa and an elongation after fracture (A80) of 15.5%. Finally, SHAP analysis was conducted to reveal the influence patterns of microstructural features on mechanical performance, thereby providing theoretical insights to guide the design and microstructure−performance optimization of advanced high−strength steels.
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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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