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The Incremental Sheet Metal Forming (ISMF) process is a new and flexible method that is well suited for small batch production or prototyping. In this study, after the process simulation with ABAQUS software and verification of results through experimental tests, the effects of three parameters including friction coefficient, tool diameter and vertical step size on three objectives including vertical force, minimum thickness of deformed sheet and amount of spring-back are investigated. A neural-network model is developed based on simulation data and the effects of parameters are studied on each objective. Also multi-objective genetic algorithm is performed to get the Pareto front of optimum points.