| Abstract: | Due to non‐linear, multidimensional and random character of most processes of machining, an use of analytical methods to process monitoring is difficult, time‐consuming and expensive. An application of algorithms which base on models in real processes is very limited, especially in the case of grinding processes. On the one hand, such methods require realistic and exact models of monitored process, on the other hand, they can characterize restrictive hypothesis concerning the process modeled. Most of model‐basing learning algorithms have an application to linear and steady‐state processes. However, majority of monitored processes are non‐linear, and, additionally, of nonstationary character. The system of monitoring proposed in the paper bases on artificial intelligence methods, which makes it possible to exclude from it a model of the grinding process. |
