Finite element analysis and response surface method for robust multi-performance optimization of radial turning of hard 300M steel

300M steel is commonly used in the automotive and aerospace industries due to its high strength and fatigue life. Residual stresses induced by machining of these materials can greatly affect the fatigue life. The present research aims mainly to develop an optimization strategy to identify optimal cutting parameters to improve machining characteristics and residual stresses induced by radial orthogonal turning 300M steel. To achieve this, first, a predictive finite element tool is developed to model cutting temperature, cutting and thrust forces, and residual stresses. Orthogonal turning experiment is conducted to measure machining forces, chip thickness, and residual stresses to validate the developed finite element model. The validated model is then used to construct response functions (meta models) using combined design of experiment and response surface method for practical and efficient implementation of the optimization problem. Finally, the developed response functions are utilized to formulate various multi-objective optimization problems. A hybrid optimization algorithm combining genetic algorithm and sequential quadratic programming method is employed to solve optimization problems in order to identify optimal cutting conditions and tool geometry. The results show that for unconstrained optimization problems, the percentage improvement in the total objective function is greater than that of constraint optimization ones. For multi-objective optimizations, it is found that weighting factors affect the optimum values of the total objective function more than those of the machining parameters. Since very few efforts were exerted to perform finite element modeling, experimental tests, and multi-performance optimization of machining characteristics and residual stresses induced by orthogonal turning 300M steel, the present results can be utilized as a reference for future works along this filed.