This invention provides a
laser cladding wear-resistant
coating design method guided by a physical metallurgical
knowledge graph, relating to the fields of
nickel-based
alloy wear-resistant
coating design and
machine learning applications. First, data is collected, and the data is divided into training and test sets using a multiple hold-out method. A graph neural
network model guided by a physical metallurgical
knowledge graph is established based on the
training set. The graph neural
network model with a squared
correlation coefficient greater than 90% is used as the objective function in the
genetic algorithm. The optimized composition, process, and material with the best target performance are obtained. This method introduces physical metallurgical mechanisms into a statistical graph neural network
algorithm in the form of a
knowledge graph, further improving the prediction accuracy of the
machine learning model. Simultaneously, the combination with optimization algorithms makes the
alloy design process more efficient, and the design results more consistent with the principles of
physical metallurgy.