The momentary control of manufacturing processes is one of the ways to increase the productivity of production lines. The online measurement of the surface roughness by non-contact methods can be utilized in order to predict the future of surface texture and modify the machining parameters. In the technique proposed at this paper, the surface texture is extracted by combining the 2D surface photography and wavelet approach. Then, by extracting the time delay parameters, the embedding dimension and the false nearest neighbor of the produced surface texture, the future surface roughness is predicted. The results show that this technique can be used in lapping, grinding, turning, and milling processes. Although the maximum roughness error occurred in the surface roughness prediction is 24%, the prediction error is almost constant after Ra = 0.4 μm in different machining processes (about 7%). This study is in line with the development of the proposed method by Pour (2018).

制造过程的瞬时控制是提高生产线生产力的方法之一。通过非接触方法在线测量表面粗糙度可用于预测表面纹理的未来并修改加工参数。在本文提出的技术中，通过结合二维表面摄影和小波方法来提取表面纹理。然后，通过提取产生的表面纹理的时间延迟参数、嵌入维度和假最近邻，预测未来的表面粗糙度。结果表明，该技术可用于研磨、磨削、车削和铣削工艺。虽然表面粗糙度预测中出现的最大粗糙度误差为 24%，但在 Ra=0 后预测误差几乎不变。4 μm 在不同的加工工艺中（约 7%）。本研究与 Pour (2018) 提出的方法的发展一致。