Multivariate Nonlinear Regression Model of Laser Fusion in vitro Skin Tissue Incision Performance Based on Response Surface Methodology

Qimeng, 刘其蒙 Liu; Jun, 黄俊 Huang; Kehong, 王克鸿 Wang; Qi, 周琦 Zhou

doi:10.3788/cjl201845.0807002

Cited by 2 publications

(1 citation statement)

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“…It can be seen that the P-value is less than 0.01, indicating that the correlation between the three variables is significant. The correlation coefficient between thickness and porosity reached 0.92, indicating a strong positive correlation; While thickness and compressive resilience, porosity and compressive resilience showed negative correlation, but the correlation was low, and the correlation coefficient was below 0.5. and searches and optimizes the optimal individuals in the group through global parallel search to obtain the optimal solution (Liu, Huang, Wang, Zhou, Liu, Huang, Wang, Zhou et al, 2018;Tu, 2021). In the optimization process, in order to control the maximum filtration efficiency, according to the above established process parameters and filtration efficiency model, the corresponding optimization model is:…”

Section: Relationship Between Structural Variablesmentioning

confidence: 99%

Study on Performance Control of Intercalation Melt-Blown Nonwovens

Liu,

Jin,

Zhao

et al. 2024

ASIR

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Intercalation melt-blown nonwovens are made by intercalation process by inserting fibers such as polyester staple fiber into melt-blown nonwovens. They have many excellent properties. However, the preparation process is complicated and the parameters are many, so the control study of the process parameters will help to provide a certain theoretical basis for the establishment of the product property control mechanism.In this paper, based on data analysis and descriptive statistics, Spearman correlation coefficient, BP neural network, typical correlation analysis, multiple nonlinear regression, genetic algorithm, multiple linear regression, NSGA-II multi-objective optimization model and other algorithms and models are used to solve the performance control problems of interlayer melt-blown nonwovens.Firstly, the data of structural variables and product performance parameters before and after intercalation were compared by mapping. It was found that the thickness and porosity increased after intercalation, some groups of compression resilience increased, and some groups became smaller, and the overall trend was larger. In addition to the thickness parameter, other parameters became better after intercalation. Finally, the intercalation rate and parameter change rate were analyzed based on Spearman correlation analysis, and it was found that intercalation rate had no effect on the changes of the above 6 parameters.The BP neural network model between process parameters and structural variables is established. The error of the model is only 0.03, and the goodness of fit is 0.99. Thus, the relationship between process parameters and structural variables is obtained. Using the neural network model, 8 process parameters were substituted to obtain the predicted structural variable data.Canonical correlation analysis was used to study the relationship between structural variables and product performance, and two pairs of canonical correlation variables were found to be significantly correlated. It was recognized that the greater the thickness, the smaller the filtration resistance and the lower the filtration efficiency. Then, the relationship between structural variables and product performance was studied based on Spearman correlation analysis, and then the relationship between process parameters and filtration efficiency was studied based on multiple nonlinear regression. It is found that there is a cubic polynomial nonlinear relationship between filtration efficiency and process parameters, and a regression model is obtained. The model has good significance and high fitting degree. Finally, the regression function is taken as the objective function, and the filtering efficiency is optimized based on genetic algorithm.Multiple linear regression model was established, and the linear regression model of thickness and receiving distance and hot air speed, compression resilience and receiving distance and hot air speed, filtration efficiency and receiving distance, hot air speed, thickness, compression resilience, filtration resistance and receiving distance, hot air speed, thickness, compression resilience was obtained. Then, the regression model of filtration efficiency and filtration resistance was taken as the objective function, and multi-objective optimization based on NSGA-II model was carried out. The known information was taken as the constraint condition, and the optimal process parameters were obtained to meet the requirement of making filtration efficiency as high as possible and filtration resistance as small as possible.

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Section: Relationship Between Structural Variablesmentioning

confidence: 99%