Evaluation and Prediction of Self-Healing Assessments For Aa2014 Based Hybrid Smart Composite Structures: A Novel Fuzzy Logic Approach

An idea to heal the damaged surface through healing agents in metallic composite are at their developmental stage. therefore, the selection of design parameters for a new generation smart composite is more complex and difficult. in the present study the two case studies on self healing smart strucures are included with different input design parameters to evaluate the healing properties. taguchi based l-8 experiments were conducted to analyze the influencial parameters responsible for higher self healing assessments (i.e. recovery in crack width, recovery in crack depth and flexural strength recovery). to evaluate the self-healing assessments of the damaged structure, a soft computing technique based on s/n ratio from anova analysis is obtained. the experimental results were further considered for constructing the fuzzy logic predictive model. linear regression models i.e. a statistical tool is generated to judge the accuracy of the fuzzy based predicted model through various error analyses. based on s/n ratio fuzzy logic model, results show less error values of 6.33 and 4.94 % for case studies i and ii, respectively in compared to the regression model adapted for all self-healing assessments. the model offers a close resemblance with the experimental observations even with less number of experimental runs. this concludes that the fuzzy logic model provides a powerful soft computing tool to perform large research work related to the design of input parameters for metallic based self-healing composites structures in near future.