Multi-Additive Special Coating Formula Calculation System Based On Deep Learning

The realization of the properties and indexes of special coatings depends on a variety of additives added in the preparation process.However,there is a lack of effective model guidance for the selection of additives and the calculation of the proportion of additives,which leads to the high cost of time and capital in the research and development of special coatings,and seriously affects the research and development of new products,quality improvement and large-scale application.In order to solve the above problems,based on the existing multi-additive special coating formula data,a multi-additive special coating formula calculation model based on K-Nearest Neighbor(KNN)regression algorithm combined with deep learning mechanism was studied and established to realize the prediction of special coating characteristics under different additives ratios.Based on the model,a multiagent special coating formula calculation system is built,which provides a practical scientific tool for the researchers of materials and chemical technology.The main work is as follows:(1)Determine the basic framework of the formula calculation model of multiagent special coating.Since the special coating formula data set is a typical small data set with uneven data distribution and a large number of features,several regression algorithms were comprehensively investigated and compared,and experiments were carried out in the field of multi-agent special coating formula calculation.Experiments show that KNN regression has the smallest mse error compared with other regression algorithms.Therefore,KNN regression is selected as the basic framework to make the basis for the next research.(2)In order to improve the accuracy of KNN regression algorithm applied to the calculation of multi-additive special coating formula,an improved method of feature value extraction and similarity weight based on deep learning is proposed.TabNet was used to adaptively learn the features of the input data in the KNN algorithm,and different feature selection was performed on the input data according to different tasks to extract the complex interactive information of multiple numerical features.Based on the extracted features,the optimal K value was selected for regression.The Deep Siamese network calculates the similarity weight between samples,selects the K training samples closest to the test sample,calculates the weighted average as the output result of the test sample,adjusts the influence of neighbor data points,and establishes a more accurate calculation model.Experiments show that the deep learning-based computational model reduces the relative error from 5% to 0.3% in the best case.(3)A multi-agent special coating formula calculation system based on deep learning computing model is constructed,which focuses on calculating the formula according to requirements and predicting the characteristics of the coating according to the input formula.The system is deployed on the Web side,and has the functions of experimental data recording,historical formula query and so on.It has strong practicability,and has been tried in relevant laboratories and got good feedback.In summary,this paper introduces the deep learning mechanism into the KNN regression algorithm,and achieves certain results in the field of multi-assistant special coating formula calculation,and implements a multi-assistant special coating formula calculation system based on this algorithm,which has certain theoretical and practical significance.