The objective of this study is to investigate the feasibility of utilizing the signal features in vibration measurements during the milling process and the cutting parameters for predicting the surface roughness of S45C steel. The features of vibration signals are extracted by means of the envelope analysis, statistical computation, such as RMS (root-mean-square), kurtosis, skewness, and multi-scale entropy (MSE), as well as the frequency normalization. Through the correlation analysis, the features of higher priority are sifted out so that the prediction computation efforts can be reduced. The sifted vibration signal features are then collected as the input layer parameters of artificial neural network (ANN) for surface roughness prediction. The prediction results and accuracy through using different classes of input features are also discussed and compared. The experimental results show that the surface roughness is affected not only by the cutting parameters, but also by the vibration behavior during the milling process. Therefore, the cutting parameters combining the essential vibration features can be utilized to enhance the prediction accuracy of surface roughness during the milling process.
This research is partially financially supported by the Ministry of Science and Technology in Taiwan, Republic of China, under the project numbers MOST 105-2221-E-005-025-MY3 and MOST 106-2218-E-194-002.
Karayel D (2009) Prediction and control of surface roughness in CNC lathe using artificial neural network. J Mater Process Technol 209:3125–3137CrossRefGoogle Scholar
Asiltürk İ, Çunkaş M (2011) Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Syst Appl 38:5826–5832CrossRefGoogle Scholar
Chang HK, Kim JH, Kim IH, Jang DY, Han DC (2007) In-process surface roughness prediction using displacement signals from spindle motion. Int J Mach Tools Manuf 47:1021–1026CrossRefGoogle Scholar
Abouelatta OB, Mádl J (2001) Surface roughness prediction based on cutting parameters and tool vibrations in turning operations. J Mater Process Technol 118:269–277CrossRefGoogle Scholar
Abu-Mahfouz I, El Ariss O, Esfakur Rahman AHM, Banerjee A (2017) Surface roughness prediction as a classification problem using suport vector machine. Int J Adv Manuf Technol 92:803–815CrossRefGoogle Scholar
Daniel KE, Chen JC (2007) Development of a fuzzy-nets-based surface roughness prediction system in turning operations. Comput Ind Eng 53:30–42CrossRefGoogle Scholar
Yang CY, Wu TY (2015) Diagnostics of gear deterioration using EEMD approach and PCA process. Measurement 61:75–87CrossRefGoogle Scholar
Yan R, Gao RX (2007) Approximate entropy as a diagnosis tool for machine health monitoringm. Mech Syst Signal Process 21:824–839CrossRefGoogle Scholar
Wu TY, Yu CL, Liu DC (2016) On multi-scale entropy analysis of order-tracking measurement for bearing fault diagnosis under variable speed. Entropy 18(2):292CrossRefGoogle Scholar
Tangjitsitcharoen S, Thesniyom P, Ratanakuakangwan S (2017) A wavelet approach to predict surface roughness in ball-end milling. Proc Inst Mech Eng B J Eng Manuf 231(14):2468–2478CrossRefGoogle Scholar
Khorasani A, Yazdi MRS (2017) Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation. Int J Adv Manuf Technol 93:141–151CrossRefGoogle Scholar
Wu TY, Lai CH, Liu DC (2016) Defect diagnostics of roller bearing using instantaneous frequency normalization under fluctuant rotating speed. J Mech Sci Technol 30(3):1037–1048CrossRefGoogle Scholar
Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A 454:903–995MathSciNetCrossRefzbMATHGoogle Scholar