Evaluation and Prediction of self-healing assessments for AA2014 based hybrid smart composite structures: A Novel fuzzy logic approach

A concept to heal the damaged surface through healing agents in metallic composites is expensive, as the selection of design parameters for such new generation smart composites are at their initial developmental stage. In the present study, the two case studies based on self-healing smart structure are included with different input design parameters to evaluate the mending of crack after healing. Taguchi based L8 experiments were conducted to analyze the influential parameters responsible for higher self-healing assessments (i.e. recovery in crack width, recovery in crack depth and flexural strength recovery). For evaluating the self-healing assessments, a soft computing technique based on S/N ratio obtained for ANOVA analysis is considered for constructing the fuzzy logic predictive model. Further, Linear regression models i.e. a statistical tool is generated to judge the accuracy of the 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 study-I and case study-II respectively in comparison to the regression model adapted for all self-healing assessments. This provides a close resemblance with the experimental observations even with less number of experimental runs. It 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.