Modeling and Optimization of the Shear Strength of Cassava Starch-Based Adhesives Using Artificial Intelligence Methods  

作　　者：Weixing Zhang Chunxia He 

机构地区：[1]College of Engineering,Nanjing Agricultural University,Nanjing 210031,China

出　　处：《Journal of Renewable Materials》2022年第12期3263-3283,共21页可再生材料杂志（英文）

基　　金：This work was supported by the Fundamental Research Funds for the Central Universities(Y0201800586);the Regional Cooperative Innovation in Autonomous Region(2019E0241).

摘　　要：With the exponential growth of the computing power,machine learning techniques have been successully used in various applications.This paper intended to predict and optimize the shear strength of single lap cassava starch-based adhesive joints for comparison with the application of artificial intelligence(AI)methods.The shear strength was firstly determined by the experiment with three independent experimental variables(starch content,NaOH concentration and reaction temperature).The analysis of range(ANORA)and analysis of variance(ANOVA)were applied to investigate the optimal combination and the significance of each factor for the shear strength based on the orthogonal experiment.The performance of all AI models was char acterized by mean absolute error(MAE),root mean square error(RMSE)and regression coefficient(R^(2))compared with the experi-mental ones.The GA optimized ANN model was combined with the genetic algorithm(GA)to find the optimal combination of factors for the finalized optimized cassava starch adhesives(CSA-OP).The physicochemical prop-erties of the cassava starch and CSA-OP were determined by the FTIR,TGA and SEM EDS,respectively.The results showed that the numerical optimized condition of the GA optimized ANN model was superior to the orthogonal experimental optimized condition.The sensitivity analysis revealed that the relative importance of variables was consistent with the results from ANOVA.FTIR results showed that there were high hydroxyl groups in cassava starch.TGA results showed that the residue of CSA OP was higher than the assava starch.SEM EDS results showed that both the cassava starch and CSA OP had abundant carbon and oxygen functional groups.Consequently,the obtained results revealed that the use of AI methods was an adequate approach to model and optimize the experimental variables of the shear strength of single lap cassava starch-based adhesive joints.

关 键 词：Cassava starch shear strength adhesives artifial inteligence orthogonal experiment 

分 类 号：S533[农业科学—作物学] TP18[自动化与计算机技术—控制理论与控制工程]