Prediction of heavy-section ductile iron fracture toughness based on machine learning

Abstract

The preparation process and composition design of heavy-section ductile iron are the key factors affecting its fracture toughness. These factors are challenging to address due to the long casting cycle, high cost and complex influencing factors of this type of iron. In this paper, 18 cubic physical simulation test blocks with 400 mm wall thickness were prepared by adjusting the C, Si and Mn contents in heavy-section ductile iron using a homemade physical simulation casting system. Four locations with different cooling rates were selected for each specimen, and 72 specimens with different compositions and cooling times of the heavy-section ductile iron were prepared. Six machine learning-based heavy-section ductile iron fracture toughness predictive models were constructed based on measured data with the C content, Si content, Mn content and cooling rate as input data and the fracture toughness as the output data. The experimental results showed that the constructed bagging model has high accuracy in predicting the fracture toughness of heavy-section ductile iron, with a coefficient of coefficient (R²) of 0.9990 and a root mean square error (RMSE) of 0.2373.

Figure 1

Effect of C, Si, Mn and cooling time on micro-structure of heavy-section ductile iron (part of the measured data) (a) C contents is 3.3 wt%, (b) C contents is 3.6 wt%, (c) Si contents is 1.9 wt%, (d) Si contents is 2.3 wt%, (e) Mn contents is 0.1 wt%, (f) Mn contents is 0.7 wt%, (g) Cooling time (min) 145, (h) Cooling time (min) 265.

Figure 2

The fracture toughness fracture morphology of heavy-section ductile iron (a) C contents is 3.3 wt%, (b) C contents is 3.6 wt%, (c) Si contents is 1.9 wt%, (d) Si contents is 2.3 wt%, (e) Mn contents is 0.1 wt%, (f) Mn contents is 0.7 wt%, (g) Cooling time (min) 145, (h) Cooling time (min) 265.

Figure 3

Effect of C, Si, Mn and cooling time on the fracture toughness of heavy section ductile iron (part of the measured data).

Figure 4

Four positions in castings chosen for temperature measurement and specimen collection.

Figure 5

Temperature measurement process of pouring and the cooling time of the four positions in castings (a) Casting process, (b) temperature measurement process, (c) cooling time of four positions in castings.

Figure 6

Dimensions of the fracture toughness specimen in mm.

Figure 7

XGBoost model structure.

Figure 8

Structure of the SVR model.

Figure 9

MLP model diagram.

Figure 10

Diagram of the standard Gaussian process model.

Figure 11

Schematic diagram of the bagging algorithm.

Figure 12

Random forest model structure.

Figure 13

Comparison of the degree of fit of the models (a) XGBoost, (b) SVR, (c) MLP, (d) Gaussian process regression, (e) Bagging, (f) Random forest.

Figure 14

Evaluation indicators for each model.

Figure 15

Bagging model prediction results (a) Comparison of the true and predicted values, (b) absolute error.

Figure 16

Comparison of the fitting degree of each model. (a) XGBoost, (b) SVR, (c) MLP, (d) Gaussian process regression, (e) Bagging, (f) Random forest.

Figure 17

Evaluation indicators for each model.