Value of information and experimentation in milling profit optimisation. (4th September 2009) - Zapata-Ramos, R.E.; Schmitz, T.L.; Traverso, M.; Abbas, A.
This paper presents a decision-analytic approach to milling optimisation in the presence of uncertainty. The decisions include the milling parameter settings and the uncertainties include the probability of stability and tool life. We quantify the stability uncertainty by Monte Carlo simulation, where the force model coefficients and frequency response function uncertainties are propagated through the stability model. We treat tool life uncertainty using encoding. We then apply a single attribute objective function, expected profit, to determine optimal settings. Using this formulation, we determine the value of an experiment, the optimal experiment settings, and the value of perfect information.