Cost optimization and experimental design in milling using surrogate models and value of information

Abstract

Tool wear experiments are necessary for tool life model development and cost optimization. However, tool wear experiments are costly and time-consuming, thereby limiting the number of tool life evaluations as a function of machining parameters. This paper describes an approach for experimental design for tool life testing and machining cost optimization in milling using surrogate modeling and the value of information method. The machining cost in milling was modeled using a kriging surrogate. The machining cost function was evaluated using experimental tool life results and was considered to be the “true” function. The value of information, expressed as expected reduction in machining cost optimum after experiment, was used to identify optimal test parameters for tool life experiments. The procedure to calculate value of information is demonstrated using a one-dimensional cost function. Results show a convergence to the true optimum in two and three dimensions. The value of information criterion balances local search by exploiting where the cost function is minimum and global search by exploring where the prediction uncertainty is high. The method performs better than the traditional statistical design of experiments such as Taguchi orthogonal arrays, central composite design, and factorial designs, especially in three or more dimensions.