Fuzzy decision-making in laser-assisted joining of polymer-metal hybrid structures

Abstract

A fuzzy decision-making approach was developed to optimize laser-assisted joining process of polymer-metal hybrid structures. A systematic approach based on Design of Experiment was adopted to identify and explain the effect of each process parameter, i.e. laser energy and laser power, on the response variables, i.e. maximum operating temperature and shear strength. Then, a combined fuzzy-genetic algorithm model was developed to find the best input parameters’ combination capable to satisfy the requirement of the best processing performances, in terms of appropriate range of operating temperature and highest shear strength. The use of genetic algorithms involved the choice of the best nominal regression model and the optimisation of the fuzzy numbers. This enabled to account most of the experimental data in combination with the smallest uncertainty level. Experimental results showed the success of the joining process ensuring sufficient adhesion of the hybrid structure over time, with shear strength of almost 29 MPa by using a laser power of 150 W and energy of 2 kJ. Moreover, the fuzzy process maps enable the selection of the process parameters with the aim of achieving desired process output along with the lowest uncertainty level. In addition, it also provides the amount of uncertainty of the model. The results indicated that a low laser energy level of 2250 J and a laser power in the range 120–140 W would provide the optimal solution in terms of operating temperature (i.e. between 343 and 520 °C), shear strength (i.e. greater than 26 MPa) and uncertainty level (i.e. membership level greater than 0.9).