Process Optimization of Bending SS 304L Sheets using Multi-Objective Genetic Algorithm and FEA

With the advancement in fabrication technology and the requirement for assembling intricate parts, the sheet metal bending accuracy needs to be improved. The sheet metal bending errors arise from the variation in material properties of the bent part and the bending process parameter discrepancies. In this work, the effect of displacement of part with respect to bending dies and the variation in yield strength of the material is studied. The bending process is simulated by preparing the finite element model of the part and bending dies. Explicit dynamic analysis is performed to find bending stresses, displacement, reaction forces, and residual stresses of the part. The variable input parameters in the analysis are part displacement with respect to die and punch and the yield strength of the material. The design of experiments approach is deployed, and a response surface is generated to study the effect of input parameters on residual stresses and reaction forces. A multi-objective genetic algorithm is used further to optimize the process and find the minimum reaction force and residual stress. The sensitivity analysis reveals that among the two input parameters yield strength of the material has a major effect on bending force and residual stresses.