Data Fusion in on-line tool wear estimation monitoring using wavelet transform and artificial neural networks

It is known that the force sensor signal in a turning process is sensitive to the gradually increasing tool wear. Based on this fact, this paper investigates a tool wear assessmenttechnique in turning through force signals. In this paper we applied wavelet analysis for tool condition monitoring (TCM). Wavelet analysis has been the most important nonstationarysignal processing tool today, and popular in machining sensor signal analysis.Based on the nature of monitored signals, wavelet approaches are introduced and the superiorities of wavelet analysis to Fourier methods are discussed for TCM. According tothe multiresolution, sparsity and localization properties of wavelet transform, literatures are reviewed in three categories in TCM: time–frequency analysis of machining signal, feature extraction, and estimation tool wear. A neural network architecture similar to a standardone-hidden-layer feed forward neural network is used to relate sensor signal measurements to tool wear classes. A novel training algorithm for such a network is developed. Theperformance of this new method is compared with a previously developed tool wearassessment method which uses a separate feature extraction step. The proposed wavelet network can also be useful for developing signal interpretation schemes for manufacturing process monitoring, critical component monitoring, and product quality monitoring

کلیدواژه ها:

Tool condition monitoring ، Cutting force ، Wavelet transform ، Artificial neural networks