Comprehensive performance evaluation and prediction of additive manufactured cast iron/GH4169 bimetal based on forward design requirements

To satisfy the high-load bearing and high-temperature resistance requirements put forward by the increased power density of the cast iron cylinder head, the cast iron/GH4169 bimetal was manufactured by Powder Bed Fusion (PBF) in forward design. The performance of bimetals is simultaneously affected by different properties in several regions. Thus, a more comprehensive approach to evaluating bimetallic performance is urgently needed. This paper uses the Multi-Attribute-Decision-Making (MADM) method to evaluate the bondability and thermal fatigue resistance of the cast iron/GH4169 bimetal under different process parameters. Meanwhile, the shear test was used to verify the evaluation results of the bimetallic bondability. Furthermore, the matching relationship between process parameters and performance evaluation scores was established by combining the BP neural network to achieve the prediction of bimetallic thermal fatigue resistance. The results show that the evaluation results using the combination weighting method based on Game Theory (GT) were more accurate than those of the Analytic hierarchy process (AHP), Entropy method (EM), and combination weighting method based on average. The evaluation results of the interfacial bondability had a relatively high degree of congruence with the shear test results. It is feasible to use MADM to comprehensively evaluate the performance of the cast iron/GH4169 bimetal. Finally, the prediction model based on the BP neural network demonstrates good prediction capability. This study can provide research methods for evaluating and predicting the performance of bimetals to meet engineering needs in forward design.