Identification Of Cutter Wear In Variable Cutting Parameters Milling

During the milling process.we confirm that the change of cutter wearing is the most familiar factor of affecting the automation of work. So, supervising the cutter wearing is important. It not only can raise the degree of automation the workpiece processed, but also can lower the manufacturing cost and reduce the pollution of the environment and guarantee the product quantity again. Therefore, on the milling machine, the research of inspecting the tool wearing contains the important meaning.The paper consists of picking-up all kinds of signal eigenvalues in machining process ,doing some fettle discrimination and analyzing all kinds of elements which may influence tool wearing in milling the workpiece. For Vibration signal, we adopt the method of wavelet transformation to carry on the processing. The vibration signal is divided into several frequency strips which have a further pick-up as the tool wearing character signal. At the same time, we will contrast the normal signal and the severe cutter wearing signal in that we have to replace the cutter. Trough the wavelet analysis, we can make sure the time of severe cutter wearing state. For the power signal, we withdraw statistics values to carry on the analysis to reflect the wearing state.The design of data collecting system, we adopt the DAC-7112DG of the I-LACS technology to collect the data. The C language takes on software and the development circulates under the environment of VC. The sensor checkout and database, data realtime collecting and data realtime processing set up the cutter wearing system. This system can realtime collects the data and shows the signal that reflects the tool wearing: machine power signal, the vibration signal, carrys on the processing in advance and picks up these signal eigenvalues.The point of this topic lies in the soft hardware design and signals processing of cutter wearing ,the picking-up of the characteristic signal of cutter wearing and the wearing eigenvalues, computing the characteristic value .The paper provide theory foundation for the supervising of the cutter wearing state in the milling process.