Big-data-mining-based tool wear state prediction method under variable working condition

The invention discloses a big-data-mining-based tool wear state prediction method under a variable working condition. The method is used for solving the technical problem that an existing tool wear state prediction method is poor in accuracy. By means of the technical scheme, the large data technology is adopted, complete sample data under the variable working condition factor are obtained, the FFNN data mining method is improved, the incremental learning capacity is achieved, the new condition can be continuously fused, and the more accurate predication model is obtained. Due to consideration of the complete sample data affecting tool wear and incremental learning of the new tool state feature vector, the new condition is continuously fused and learnt, the more accurate prediction model is obtained, and the basis is provided for further analysis of relevant factors affecting tool wear. Compared with the prior art, the newly-added condition is learnt, and accuracy of the tool wear state predication under the