کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
560648 | 1451881 | 2013 | 14 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Applying the self-organization feature map (SOM) algorithm to AE-based tool wear monitoring in micro-cutting Applying the self-organization feature map (SOM) algorithm to AE-based tool wear monitoring in micro-cutting](/preview/png/560648.png)
This study applies a self-organization feature map (SOM) neural network to acoustic emission (AE) signal-based tool wear monitoring for a micro-milling process. An experiment was set up to collect the signal during cutting for the system development and performance analysis. The AE signal generated on the workpiece was first transformed to the frequency domain by Fast Fourier transformation (FFT), followed by feature extraction processing using the SOM algorithm. The performance verification in this study adopts a learning vector quantification (LVQ) network to evaluate the effects of the SOM algorithm on the classification performance for tool wear monitoring. To investigate the improvement achieved by the SOM algorithms, this study also investigates cases applying only the LVQ classifier and based on the class mean scatter feature selection (CMSFS) criterion and LVQ. Results show that accurate classification of the tool wear can be obtained by properly selecting features closely related to the tool wear based on the CMSFS and frequency resolution of spectral features. However, the SOM algorithms provide a more reliable methodology of reducing the effect on the system performance contributed by noise or variations in the cutting system.
► Tool wear monitoring for micro end-mill is developed based on Neural Network System.
► SOM algorithm is adopted to reduce the noise effect on the system.
► The system integrated with SOM algorithm makes the system more robust than others.
Journal: Mechanical Systems and Signal Processing - Volume 34, Issues 1–2, January 2013, Pages 353–366