High-precision quantitative analysis methods of lamellar α phase in TC4-DT alloy
It is difficult to quantify the volume fraction of lamellar α phase in basket-weave microstructure accurately and to characterize the adhesive α phase by separation.In order to solve these problem,the random forest,genetic algorithm and improved genetic algorithm were used to characterize the lamellar α phase in basket-weave microstructure of TC4-DT alloy combined with the principle of stereology.Firstly,the collected basket-weave microstructure images were preprocessed.Then,the random forest model was trained by using the characteristics of lamellar α phase and β phase in the sample.Considering that the traditional genetic algorithm image segmentation are prone to fall into the local optimal solution and the convergence speed is too fast,the combination method of elite selection and roulette was used to initialize the population in the paper.Also,a two-stage crossover probability and parabolic mutation probability optimization genetic algorithm was designed.Finally,the Java program was used to verify the random forest model and quantitative analysis the volume fraction of lamellar α phase automatically,and quantitative analysis of the lamellar α phase characteristic parameters were performed based on the examples.The results show that the run time of the improved genetic algorithm is shortened by 60%and the effect of image processing is improved.The classification accuracy of the random forest model not only reaches 99.89%in the training samples,but also reaches 99.29%in the test samples.It is proved that the lamellar α phase and β phase can be separated accurately by the model and its generalization performance is better.
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