Abstract
Tool wear experiments are necessary for tool life model development and cost optimizations. 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 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 machining cost function was evaluated using experimental tool life results and was considered to be the "true" function. The convergence of the kriging prediction to the true optimum was evaluated. Results showed convergence to the true value in seven tests for a two dimensional cost function. 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.
Original language | English |
---|---|
Pages (from-to) | 238-247 |
Number of pages | 10 |
Journal | Transactions of the North American Manufacturing Research Institute of SME |
Volume | 42 |
Issue number | January |
State | Published - 2014 |
Externally published | Yes |
Event | 42nd North American Manufacturing Research Conference 2014, NAMRC 2014 - Detroit, United States Duration: Jun 9 2014 → Jun 13 2014 |
Keywords
- Cost
- Experimental design
- Kriging
- Milling
- Tool life testing
- Value of information