Zhu, Z.;                     Jin, D.;                     Wu, Z.;                     Xu, W.;                     Yu, Y.;                     Guo, X.;                     Wang, X.    
        Assessment of Surface Roughness in Milling of Beech Using a Response Surface Methodology and an Adaptive Network-Based Fuzzy Inference System. Machines 2022, 10, 567.
    https://doi.org/10.3390/machines10070567
    AMA Style
    
                                Zhu Z,                                 Jin D,                                 Wu Z,                                 Xu W,                                 Yu Y,                                 Guo X,                                 Wang X.        
                Assessment of Surface Roughness in Milling of Beech Using a Response Surface Methodology and an Adaptive Network-Based Fuzzy Inference System. Machines. 2022; 10(7):567.
        https://doi.org/10.3390/machines10070567
    
    Chicago/Turabian Style
    
                                Zhu, Zhaolong,                                 Dong Jin,                                 Zhanwen Wu,                                 Wei Xu,                                 Yingyue Yu,                                 Xiaolei Guo,                                 and Xiaodong (Alice) Wang.        
                2022. "Assessment of Surface Roughness in Milling of Beech Using a Response Surface Methodology and an Adaptive Network-Based Fuzzy Inference System" Machines 10, no. 7: 567.
        https://doi.org/10.3390/machines10070567
    
    APA Style
    
                                Zhu, Z.,                                 Jin, D.,                                 Wu, Z.,                                 Xu, W.,                                 Yu, Y.,                                 Guo, X.,                                 & Wang, X.        
        
        (2022). Assessment of Surface Roughness in Milling of Beech Using a Response Surface Methodology and an Adaptive Network-Based Fuzzy Inference System. Machines, 10(7), 567.
        https://doi.org/10.3390/machines10070567