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|標題:||Application of Acoustic Emission Signals for Surface Roughness of Zirconia Ceramic in the Precision Grinding
|關鍵字:||zirconia;plane grinding;acoustic emission (AE);surface roughness;neural networks;氧化鋯;平面磨削;聲射(AE);表面粗糙度;類神經網路||引用:|| P.S Sreejith, B.K.A Ngoi, 'Material removal mechanisms in precision machining of new materials', International Journal of Machine Tools and Manufacture, Vol. 41, Issue 12, September 2001, pp. 1831-1843.  Muhammad Arif, Zhang Xinquan, Mustafizur Rahman, Senthil Kμmar, 'A predictive model of the critical undeformed chip thickness for ductile–brittle transition in nano-machining of brittle materials', Journal of Materials Processing Technology, Vol. 209, Issue 7, 1 April 2009, pp. 3306-3319.  J. Xie, H.F. Xie, X.R. Liu, T.W. Tan, 'Dry micro-grooving on Si wafer using a coarse diamond grinding', International Journal of Machine Tools and Manufacture, Vol. 61, October 2012, pp. 1-8.  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Due to advances in the technology industry, subtle manufacturing technology continues to improve, zirconia such brittle materials used in high-precision parts more extensive and grinding technology also needs to follow the high-precision, high efficiency working in high-precision grinding improve processing efficiency, quality, and reduce processing costs is seen as key to improving competitiveness, so the precision grinding of automation and intelligent systems is one of the conditions.
This study aimed to analyze investigate AE (Acoustic Emission) signal in the application of the grinding surface roughness characteristics.This experiment in Taguchi method, AE signals interception zirconia grinding processing, the AE signals do Fourier transform, and root mean average (RMS) , the zirconia surface with grinding message after the signal correlation of roughness for the final will have relevance AE signal input neural network to predict the zirconia surface roughness.
In the experimental analysis of acoustic emission signals , the surface roughness of the assessment made the following points, primarily to determine the roughness of the grinding poor basis: First, most of the high frequency(200kHz-320kHz) has peaks, and the second is high frequency(200kHz-320kHz) has high energy, and finally in the low frequency(50kHz-100kHz) with high energy.
The experimental results show that AE signals for neural network to predict the surface roughness 10μm wheel,10-10-1 network architecture with the greatest difference is 0.0124μm, the minimμm difference is 0.0007μm, an average difference of 0.0061 μm, MSE (mean square error) is 0.0001, MAPE (Mean absolute percentage error) is 0.04%, R (Sample Correlation Coefficient) of 0.6988, with the wheel 10μm AE signal is applied to the neural network to predict the surface roughness MAPE 0.04% to reach a very low error.
In 25μm wheel of 4-4-1 network architecture with the greatest difference is 0.0144μm, the minimμm difference is 0.0015μm, the average difference was 0.0086μm, MSE is 0.0001, MAPE is 0.11%, R is 0.6194, at 25μm wheel with AE signals for neural network to predict the surface roughness is 0.11% MAPE error value is quite low, observed that AE signals for grinding surface roughness prediction is a high correlation.
本實驗結果得知類神經網路預測表面粗糙度，在磨粒10μm的砂輪下的實驗中，10-10-1網路架構以最大差值為0.0124μm、最小差值為0.0007μm、平均差值為0.0061μm、MSE(mean square error)為0.0001、MAPE(Mean absolute percentage error)為0.04%、R(Sample Correlation Coefficient)為0.6988，AE訊號應用於類神經網路是可預測表面粗糙度到MAPE為0.04%，達到相當低的誤差值。
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