2.2.5 Wear and damage Tool files are stored in the main parameters of worn and damaged tools. The parameters of each tool are taken as one record.
3 On-line detection of tool wear and damage
There are many methods for online detection of tool wear and damage, including power detection, acoustic emission detection, learning mode, and force detection. A method for detecting a tool in a flexible manufacturing system using a neural network is described herein.
3.1 The establishment of the tool load model The load on the tool during the cutting process is related to many factors. According to the requirements of the online test, only a few large influencing factors are considered, namely the spindle speed, the feed speed, the cutting depth, and the processed material. The cutting performance is 4 factors, the model of the tool load is F = f (s, v, h, m)
In the formula:
F - load vector;
h — depth of cut;
s — spindle speed;
m — the cutting performance of the material;
v — the amount of feed.
Obviously, the above formula can only explain that the load is related to various influencing factors. The mathematical relationship or differential method of differential geometry can be used to establish the corresponding relation, but the effect of applying online detection is not ideal. Here, the load model of the tool is processed using neural network technology.
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