Multiobjective Optimization of Experimental and Simulated Residual Stresses in Turning of Nickel-Alloy IN100

In this study, physics-based simulations are utilized to predict the forces and residual stresses induced during machining, and the results were validated using the experimental measurements. Physics-based simulations also involve uncertainty in the predicted values that can be represented as expected value and variance of the predictions. These predictions are inputted to a multiobjective optimization methodology to select the optimal machining parameters where competing or conflicting objectives constitute hurdles in the decision-making of the manufacturing plans. The objectives are chosen as related to residual stress measurements and predictions. Multiobjective particle swarm optimization (PSO) procedure is employed in optimizing process parameters. Objectives are solved for minimizing tensile residual stresses on the surface, maximizing peak compressive residual stresses, and minimizing the variance of these variables in order to increase certainty in the predictions. The optimum machining parameters corresponding to this multiobjective optimization are represented in both objective function and decision variable spaces.