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Compared to conventional mechanical testing methods, the indentation method offers the advantages of simple manufacturing of samples and in-situ testing. This study proposes an alternative to deriving material mechanical parameters solely from indentation load-depth curves. It introduces an effective method for deducing metal plastic mechanical parameters based on residual indentation morphology and neural network learning. An Instron universal material testing machine was used to conduct spherical indentation tests on Cu, Mg, and Fe, followed by scanning their residual indentation morphology through the contour morphology system. The extracted morphology features served as the basis for further analysis. Data processing techniques such as amplification, rounding, binarization, and high-order digit supplementation were applied to the acquired data. Through Abaqus software and numerical simulations, residual indentation depth data associated with various material parameters were automatically extracted for neural network learning. Selections of activation function, neural network parameter initialization and updating mode, loss function, parameter optimization strategy, and neural network structure were carefully conducted to ensure effective learning. The plastic mechanical parameters of Cu, Mg, and Fe were obtained based on the residual indentation morphology feature data from indentation tests and the neural networks after learning. Additionally, the related plastic mechanical parameters of Cu, Mg, and Fe were also acquired through conventional uniaxial tensile tests and characterization using the Instron machine. By comparing the neural network learning results with tensile test data, relative errors in plastic mechanical parameters were identified. The effectiveness of the proposed method in obtaining metal plastic mechanical parameters based on neural network learning and residual indentation morphology was validated. This method can be expanded for characterizing mechanical properties and acquiring plastic parameters of other metal/alloy materials.
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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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