Product Quality Prediction Of Complex Production Process Based On Data-driven Model Fusion

The rapid development of artificial intelligence technology has provided new solutions for the field of quality prediction.Through deep mining of production data using methods such as big data analysis,the complex nonlinear relationship between product quality and process parameters can be effectively addressed,improving the accuracy of product quality prediction.This paper analyzes the production characteristics,data characteristics,and existing data-driven quality prediction methods of complex production processes,and proposes a model fusion-based data-driven product quality prediction method to enhance the accuracy of product quality prediction in complex production processes.The main research contents are as follows.(1)Analyzing the complex production characteristics,data characteristics,and shortcomings of existing data-driven quality prediction research,a model fusion-based quality prediction method is proposed by integrating models built from two different perspectives of the production process.(2)A feature-enhanced whole product quality prediction model is proposed based on modeling the entire production process to predict the final product quality.The proposed feature selection method based on mutual information effectively enhances the strong correlation and low redundancy of the model’s input features,thereby improving the prediction accuracy.A production whole product quality prediction model based on improved PSO-LSTM is established based on the strong correlation and low redundancy feature selection results.(3)A segmented product quality prediction model based on error correction mechanism is proposed from the perspective of modeling the production process in stages to predict the quality of work in progress.Work-in-progress quality prediction models based on improved RF are established for each stage of production.The error correction mechanism based on historical production data is proposed to effectively solve the problem of error accumulation in the segmented prediction process.(4)From the perspective that the predictive ability of a single model is weak in complex production processes,a model fusion-based product quality prediction method based on stacking is proposed using the idea of ensemble learning.The two quality prediction models built for the production process are integrated,and the training process of the meta-learner is improved using five-fold cross-validation and polynomial feature processing methods to obtain a more accurate quality prediction fusion model.(5)Through simulation analysis of practical cases,the effectiveness and accuracy of the proposed data-driven model fusion-based quality prediction method are verified.The prediction of the moisture content of tobacco entering the dryer in the production process of Wuhan Tobacco Factory is selected as the case study.By comparing the prediction performance before and after model fusion,the proposed model fusion method is found to be more accurate than a single model.By comparing with other research methods for predicting the moisture content of tobacco entering the dryer,the proposed method is found to be highly accurate and feasible in complex production processes.The research and analysis of the case study demonstrate that the proposed data-driven model fusion-based quality prediction method has high accuracy and effectiveness,expands the research content of product quality prediction in complex production processes,and has strong theoretical and practical significance,providing a new way for the field of quality prediction.