Fuzzy modelling of machine-tool cutting process

Fuzzy control of machining process is a very promissory approach, taking into account the machine-tool complexity and efficiency. Creation of the knowledge base for this fuzzy controller requires something more than operators experience: an objective support. Such objective support is to be obtained from experiments, during which the machine-tool actually performs the cutting process and the corresponding input and output data must be gathered. A rather efficient approach, fuzzy clustering, was chosen for elaborating this data in order to obtain the required knowledge base for the fuzzy model. The paper describes the algorithms, experiments and fuzzy models for the cutting process of a milling machine. The models obtained are the basis for designing the model-based fuzzy supervisory controller for this machine. >