ANN prediction of impact toughness of composites produced by explosive welding
Abstract
In this study, ship steel-stainless steel composite plates were produced by joining ship steel with stainless steel using explosive welding process. The characterization of the joining interface of the plates was carried out by optical microscopic examination. Charpy impact tests were conducted at different temperatures to determine the impact toughness of the ship steel-stainless steel composites and the effect of the rolling direction on the impact toughness. In addition, after the Charpy impact test, the fractured surfaces of the samples were investigated via scanning electron microscopy (SEM). Using the data obtained as a result of the impact toughness tests, an artificial neural network (ANN) model was improved for the prediction of the impact toughness. Five different material types, two different rolling directions, and eight different temperatures were used as the input parameters of the Charpy impact tests. The impact toughness values obtained at the end of the tests were used as the output parameters of the generated prediction model. The high R2 value obtained in the developed prediction model demonstrated that it could be successfully used for predicting impact toughness.
References
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