Prediction of surface roughness of various machining processes by a hybrid algorithm including time series analysis, wavelet transform and multi view embedding.

• This paper presents a future prediction technique for using in S.R. measurement. • The suggested method reduces the required data amount used in future prediction. • To obtain a quicker response, the time series analysis is combined with the ICA and MVE algorithms. • The maximum error of suggested method is equal or less than 8% for Ra ≥ 0.4 μm. 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). [ABSTRACT FROM AUTHOR]