Injection molding product size prediction based on WOA-Stacking ensemble learning method
In the existing machine learning-based injection molding product size prediction models,there is the problem that the prediction accuracy of a single model is not high.In order to improve the accuracy of real-time monitoring of injection molding product dimensional changes,an injection molding product size prediction method based on whale optimization algorithm(WOA)optimized Stacking ensemble learning was proposed.First,the data collected from the injection molding process were integrated,the outliers were screened using the 3σ criterion,and the key features were selected by random forest and mutual information method as the input features for the subsequent model.Second,in the Stacking ensemble learning framework,K-nearest neighbor,random forest and light gradient boosting machine were selected as the base learners,and elastic net regression was selected as the meta learner,and WOA was used to optimize the hyperparameters in each base learner to construct the WOA-Stacking ensemble learning prediction model.Finally,the proposed model was applied to the injection molding product size prediction,and then other models were compared and analyzed to verify the effectiveness of the present method.Taking the data of the 4th Industrial Big Data Innova-tion Competition as an example,in the comparison experiment containing three ensemble models and three single models,the three-dimensional size of the products were selected as the prediction target,and the experimental results showed that the WOA-Stacking ensemble learning model had higher prediction accuracy and fitting ability.
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