Dynamic performance measures for tools with multi-state wear processes and their applications for tool design and selection

Traditional models to evaluate the reliability and performance of tools are binary models, working (success) or failure, to classify the state of the tool. Most machine tools degrade with time and thus a multi-state discrete classification is more realistic for the continuous degradation of the tool. We propose a non-homogeneous continuous-time Markov process model for tool degradation, because the length of time the machine tool stays in a certain state depends not only on the current state, but also on how long the tool has been in the current state. The traditional reliability and life performance measures focus on the mean time between failure or the failure rate. The performance measures must capture the total experience of the manufacturer over the target life of the tool and the impact of its degradation on the quality of the products to the downstream customers. We propose several new measures for tool performance. These measures can be used to evaluate different tool designs or we can use them to select the best tool for a certain application based on the economic/disutility functions.